JP6191708B2 - Information selection device, information selection method, terminal device, and computer program - Google Patents

Description

  The present invention relates to an information selection technique for selecting recommendation information.

There is a growing need for a technique for recommending items useful for users among a large number of items. As one of the methods for recommending items, a method for recommending in units of categories is known. When recommending by category, it is necessary to determine the recommended category. Patent Document 1 discloses a program recommendation device that determines a recommended category and a recommended program belonging to the recommended category. The program recommendation apparatus of patent document 1 determines a recommendation category using the user preference degree for every program category. Here, the user preference level for each program category is the degree of relevance between each program category and the television program included in the user's recording history, or each program category and the user's EPG (Electron
ic Program Guide) shows the degree of association with the TV program selected.

More specifically, in order to determine a program category to be recommended, the program recommendation device of Patent Document 1 firstly records a TV program recording reservation history (or selection history from an EPG) by a user.
Is used to calculate the user preference for each program category. Next, the program recommendation device determines a program category having a user preference level exceeding a threshold as a recommended program category. Then, the program recommendation device determines a recommended program from a group of programs corresponding to the determined recommendation category (see FIG. 8 of Patent Document 1). The determined recommended program category and the recommended program are displayed on the display in units of categories, for example (see FIG. 10 of Patent Document 1).

International Publication No. 2005/048587

As described above, the technique disclosed in Patent Document 1 is calculated using a program included in the recording reservation history (or selection history from the EPG) of the TV program by the user in order to determine the recommended program category. The user preference level for each program category is used. However, the technique disclosed in Patent Document 1 does not sufficiently consider information of television programs belonging to each program category when determining a recommended program category. For this reason, in the technique disclosed in Patent Document 1, there are not always recommended programs associated with the recommended program category.

Therefore, even if the technique disclosed in Patent Document 1 can present the recommended program category based on the program included in the recording reservation history (or selection history from the EPG) of the TV program by the user, the recommended program is sufficiently presented. It may not be possible. For the user, the program that is the target of direct use is more important than the program category, and the program category is merely auxiliary information. That is, even if the recommended program category can be presented, viewing of the program that is the original purpose of the recommendation is not promoted unless the recommended program category is sufficiently associated with the recommended program.

This problem is not limited to providing a recommended category related to a television program, but can occur widely when providing a recommended category related to some item.

The information selection device according to the first aspect of the present invention selects a recommended category recommended to a user from the plurality of candidate categories based on a plurality of category priorities calculated for each of a plurality of candidate categories. A category selection unit (1071) that calculates each of the plurality of category priorities using an item relevance level that indicates the strength of relevance between an item belonging to each candidate category and a reference item.
Each of the plurality of category priorities may be further calculated using a dissimilarity degree indicating the strength of the relation between the reference item and each candidate category.
The heterogeneous relevance level may be calculated using a category relevance level indicating the strength of relevance between a reference category corresponding to the reference item and each candidate category.
Each of the plurality of category priorities may be calculated so as to be higher as the dissimilarity degree is higher and the item relation degree is higher.
Each of the plurality of category priorities is (a) a first value based on a value using the heterogeneous relevance.
A multiplication value of a power value with an index of a predetermined value of the value and a power value with a value using the item relevance as a base and a second predetermined value as an index, and (b) a value using the dissimilarity It may be calculated using any one of a weighted addition value with a value using the item relevance level.
Each of the plurality of category priorities may be calculated as a function of the extended relevance obtained by inputting the heterogeneous relevance into a predetermined conversion rule.
The conversion rule may be a conversion function having a section in which the output value decreases as the input value increases.
Each of the plurality of category priorities is (a) a third value based on the value using the expanded relevance.
A multiplication value of a power value with an index of the predetermined value and a power value with the value using the item relevance as a base and a fourth predetermined value as an index, and (b) a value using the expanded relevance It may be calculated using any one of a weighted addition value with a value using the item relevance level.
The category selection unit includes, for each of the plurality of candidate categories, an item group in which the item relevance is a fifth predetermined value or more, or a number of items that do not exceed a sixth predetermined value in descending order of the item relevance A group is selected as a higher-level item set, a total value or a representative value of a plurality of item relevances of a plurality of items included in the higher-level item set and the reference item is calculated, and the total value or the representative value is used. The category priority may be calculated.
The category selection unit may calculate the category priority for each of the plurality of candidate categories using the number of items whose item relevance is equal to or greater than a seventh predetermined value.
A control information creating unit for creating display control information for outputting a display screen showing information on the recommended category;
The recommended category includes a plurality of recommended categories, the display screen includes a plurality of display objects corresponding to the plurality of recommended categories, and a display state of each display object in the display screen is a display object. May be determined according to the category priority level of the recommended category represented by.
You may further provide the control-information production | generation part which produces the display control information for outputting the display screen which receives adjustment of the characteristic of the said conversion rule.
You may further provide the item selection part which selects a recommended item from the items which belong to the said recommendation category based on the said item relevance degree.
The item selection unit may set a selection condition for the recommended item according to the category priority.
The terminal device according to the second aspect of the present invention operates to output the display screen to a display unit based on the display control information received from the information selection device via the network, and adjusts the characteristics of the conversion rule. A processing unit that operates to transmit information to the information selection device.
A terminal device according to a third aspect of the present invention includes a receiving unit that receives a recommended category and a category priority indicating the priority of the recommended category, and a display for outputting a display screen indicating information related to the recommended category. A control information creating unit for creating control information, wherein the recommended category includes a plurality of recommended categories, and the display screen includes a plurality of display objects corresponding to each of the plurality of recommended categories, The display state of each display object is determined according to the category priority level of the recommended category represented by each display object.
In the information selection method according to the fourth aspect of the present invention, for each of a plurality of candidate categories,
The category priority is calculated using the item relevance indicating the strength of the relevance between the item belonging to each candidate category and the reference item, and recommended to the user based on the calculated plurality of category priorities. Selecting a recommended category from the plurality of candidate categories;
Is provided.
The calculating may include calculating the plurality of category priorities using a dissimilarity degree indicating a strength of relevance between the reference item and each candidate category.
The heterogeneous relevance level may be calculated using a category relevance level indicating the strength of relevance between a reference category corresponding to the reference item and each candidate category.
The computer program which concerns on the 5th aspect of this invention is a computer program for making a computer perform the information selection method which concerns on the 4th aspect mentioned above.

According to each aspect mentioned above, it can contribute to provision of the recommendation information which shows the recommendation category with high possibility that the recommendation item which has high relevance with a reference | standard item exists.

It is a block diagram which shows the structural example of the network system containing an information selection apparatus. It is a block diagram which shows the other structural example of the network system containing an information selection apparatus. It is a block diagram which shows the structural example of an information selection apparatus. It is a flowchart which shows an example of the selection procedure of the recommendation category by the information selection apparatus which concerns on 1st Embodiment. It is a flowchart which shows an example of the selection procedure of the recommendation category by the information selection apparatus which concerns on 2nd Embodiment. It is a block diagram which shows the structural example of the information selection apparatus which concerns on 3rd Embodiment. It is a figure which shows the example of the item information table, the category information table, and the item-category correspondence table which are stored in the metadata storage unit. It is a figure which shows the example of the item utilization history table and category utilization history table which are stored in a utilization history storage part. It is a figure which shows the example of the category relevance table stored in a category relevance storage part. It is a figure which shows the example of the recommendation information table stored in a recommendation information storage part. It is a figure which shows the example of the item relevance degree table stored in an item relevance degree storage part. It is a block diagram which shows the structural example of an item provision server. It is a block diagram which shows the structural example of a terminal device. It is a flowchart explaining the specific example of operation | movement of the whole system. It is a figure which shows the example of a display screen based on the response data from an item provision server. It is a figure which shows the example of a screen which displays a recommendation list. It is a flowchart explaining the specific example of the operation | movement which an information selection apparatus produces recommendation information. It is a flowchart explaining the specific example of an item relevance degree calculation process. It is a flowchart explaining the specific example of a category relevance calculation process. It is a flowchart explaining the 1st specific example of a recommendation information creation process. It is a flowchart explaining the 2nd specific example of recommendation information creation processing. It is a figure which shows the example of a characteristic of the conversion function F (X) which makes a category relevance the input X, and makes the expansion relevance the output Y. It is a figure for demonstrating the specific example of the recommendation item determination process in the 1st specific example of a recommendation information creation process. It is a figure which shows the example of the temporary table in the 2nd specific example of recommendation information creation processing. It is a figure which shows the 1st modification of the screen which displays a recommendation list. It is a figure which shows the example of a screen which adjusts an expansion relevance degree. It is a figure for demonstrating the adjustment of the characteristic of the conversion function F (X) used for calculation of an extended relevance degree. It is a figure which shows the 2nd modification of the screen which displays a recommendation list. It is a figure which shows the example of the screen which displays item information in the 2nd modification of the screen which displays a recommendation list.

Hereinafter, specific embodiments will be described in detail with reference to the drawings. In each drawing, the same or corresponding elements are denoted by the same reference numerals, and redundant description is omitted as necessary for clarification of the description.

First, definitions of terms used in this specification will be described. As used herein, the term “item” refers to various objects that are utilized (eg, purchased, viewed, viewed, rated, selected, or searched) by a user. In other words, the term item means a generic concept that includes goods, information, and services, whether tangible or intangible. Item is
For example, at least one of the following (a) to (d) is included.
(A) Digital contents as intangibles (for example, text data, audio data, music data, photo data, video data, computer programs, web pages), or information related thereto (for example, product information, advertisements)
(B) Non-electronic products (for example, goods, financial products) as tangible or intangible objects, or information related thereto (for example, product information, advertisements)
(C) Information (for example, ticket sales information, reservation information, TV program information) regarding services (for example, sports performances, concert performances, movie performances, television broadcasting, network services)
(D) Search keywords and search results

The term “category” in this specification means an item for classifying items. Categories can also be referred to as groups or classes. The correspondence between the item and the category (for example, the category to which the item belongs) may be explicitly specified by metadata attached to the item, a correspondence table, or the like. Further, the correspondence between items and categories may be determined by evaluating the relevance, similarity, or correlation between items and categories.

In this specification, the term “relevance” means between elements (eg, between items, between categories,
Or a measure of strength of association, similarity, or correlation between items and categories.

In addition, the term “reference item” in this specification means an item that serves as a reference when determining a recommended category or a recommended item. The reference item is typically an item selected by the user for reasons such as “I want to know information about the item in detail”. In one example, the reference item may be an item included in some history of the user (for example, usage history, purchase history, browsing history, viewing history, evaluation history, selection history, or search history).

<First Embodiment>
The present embodiment includes an information selection device 10. FIG. 1 shows an information selection apparatus 10 according to this embodiment.
It is a block diagram which shows the structural example of the system 1 containing this. The information selection device 10 is used in combination with the item providing server 10 or at least one terminal device 30 and operates to select a recommended category recommended to the user. The information selection device 10 may further determine recommended items recommended for the user. Thereby, recommendation information indicating a recommendation category or a recommendation category and a recommendation item is provided to the user via the terminal device 30.

The information selection device 10 includes at least one processor (for example, a microprocessor, M
You may comprise using the computer system which has PU (Micro Processing Unit) and CPU (Central Processing Unit). The computer system can function as the information selection device 10 by executing one or a plurality of computer programs including an instruction group for performing an algorithm related to selection of a recommendation category, which will be described later.

This program can be used for various types of non-transitory computer-readable media (non-transitory).
computer readable medium) and can be supplied to a computer. Non-transitory computer readable media are tangible storage media of various types.
torage medium). Examples of non-transitory computer readable media include magnetic recording media (eg, flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (
For example, a magneto-optical disk), an optical disk (for example, CD-ROM (Read Only Memory), C
D-R, CD-R / W, DVD-ROM, DVD-R), semiconductor memory (for example, mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash R)
OM, random access memory (RAM)). The program may also be supplied to the computer by various types of transitory computer readable media. Examples of transitory computer readable media include electrical signals, optical signals, and electromagnetic waves. The temporary computer-readable medium can supply the program to the computer via a wired communication path such as an electric wire and an optical fiber, or a wireless communication path.

The information selection device 10 may be configured using a plurality of computers. For example, a plurality of computers each responsible for the same processing block of the information selection device 10 may be used for load distribution. Further, a certain computer may be in charge of a part of processing blocks of the information selection apparatus 10, and another computer may be in charge of another part of processing blocks.

The item providing server 20 transmits an item (for example, digital content, a computer program) or information about the item (for example, information about electronic or non-electronic products / services) to the terminal device 30 via the network 40. Specific examples of the item providing server 20 include a digital content providing server, a digital content sharing server, a search server, an EC (Electronic Commerce) system, and a television broadcasting station.

The terminal device 30 is used by a user. Specific examples of the terminal device 30 include a mobile phone terminal, a smartphone, a digital audio player, a tablet computer, and a notebook P.
C (Personal Computer), desktop PC, TV broadcast receiver, TV program recorder.

The terminal device 30 receives the item or information related to the item transmitted from the item providing server 20, and outputs an image or sound indicating the item or information related to the item or a combination thereof from an output device (for example, a display or a speaker). Operate. Further, the terminal device 30 receives recommendation information including a recommended category and a recommended item from the information selection device 10 or the item providing server 20, and outputs an image or sound indicating the recommended category and the recommended item, or a combination thereof as an output device (for example, It operates to output from the display and speakers.

The network 40 is a communication network or a combination of a communication network and a broadcast network. The network 40 may be a wired network, a wireless network, or a combination thereof.

Note that the configuration example of FIG. 1 is merely an example relating to the usage mode of the information selection device 10. For example, the system 1 including the information selection device 10 may be configured as shown in FIG. In the example of FIG. 2, the information selection device 10 communicates with the item providing server 20 via a network 42 different from the network 40. The network 42 is, for example, a LAN (Local Area Network)
Direct access from the terminal device 30 to the information selection device 10 may be restricted from the viewpoint of ensuring security.

In the example of FIGS. 1 and 2, the information selection device 10 is physically separated from the item providing server 20 and the terminal device 30. However, the information selection apparatus 10 may be physically integrated with any one of these. That is, the information selection device 10 may be configured as a device (for example, a computer system) that is physically the same as the item providing server 20. The information selection device 10 may be configured as a device (for example, a computer system) that is physically the same as the terminal device 30.

Subsequently, a configuration example of the information selection device 10 and an example of an operation for selecting a recommended category and a recommended item by the information selection device 10 will be described. FIG. 3 is a block diagram illustrating a configuration example of the information selection device 10. The information selection device 10 includes an information selection unit 107. The information selection unit 107 determines a recommended category recommended to the user. The information selection unit 107 may further determine recommended items. In the example of FIG. 3, the information selection unit 107 includes a category selection unit 1071 that selects a recommended category and an item selection unit 1072 that selects a recommended item. However, the selection of the recommended item corresponding to the recommendation category determined by the information selection device 10 is different from the information selection device 10, for example, the item providing server 20.
Alternatively, it may be performed by the terminal device 30.

The category selection unit 1071 operates to select a recommended category from among a plurality of candidate categories based on a plurality of category priorities calculated for each of a plurality of candidate categories. Here, each of the plurality of category priorities is calculated using the item relevance indicating the strength of relevance between the items belonging to each candidate category and the reference item. That is, the category priority is used as a scale for determining the recommended category. Category selection part 1
071 considers the item relevance between the items belonging to the category and the reference item when calculating the category priority.

The category selection unit 1071 determines a recommended category from among a plurality of candidate categories in consideration of the category priority. For example, the category selection unit 1071 may preferentially select a category with a high category priority as a recommended category. For example, the category selection unit 10
71 may select a predetermined number of categories as recommended categories in descending order of category priority. Further, the category selection unit 1071 may select a category having a category priority of a predetermined value or more from among a plurality of candidate categories as a recommended category.

Subsequently, various parameters and information required for calculating the category priority and an example of calculating the category priority will be described below. The item relevance necessary for calculating the category priority may be derived by the information selection device 10 or supplied to the information selection device 10 from the outside. For example, the item relevance determined by the administrator or operator may be supplied to the information selection device 10. The item relevance level may be supplied from the external device to the information selection device 10 as a table in which a pair of item identifiers indicating item combinations and the item relevance level are associated (ie, item relevance level table).

In order to derive the item relevance, the information selection device 10 or an external device may analyze the relevance, similarity, or correlation between items. Specifically, the information selection device 10 or an external device may store a plurality of user histories (for example, usage history, purchase history, browsing history, viewing history,
The item relevance may be calculated using an evaluation history or a search history. For example, the information selection device 10 or an external device calculates the number of users who have used both the reference item x and another item y, and calculates the number of items related to the reference item x and the other item y W [ x
] [Y]. In addition, the information selection device 10 or an external device evaluates the frequency with which the reference item x and other items y are used by the same user by using a co-occurrence measure used in the field of statistics. , This item relevance W [x] [y]
It is good. As a co-occurrence scale, for example, a Jaccard coefficient, a Simpson coefficient, or a cosine scale (cosine distance) can be used.

Further, when the number of item usages by a plurality of users or an evaluation value for an item can be obtained from the user's history, the information selection device 10 or an external device uses these pieces of information to calculate the item relevance W [x] [y]. It may be calculated. At this time, a known measure for evaluating the correlation between the two elements can be used. For example, the information selection device 10 or an external device
Correlation between two items using the item usage count or item evaluation value by the user (
That is, the Pearson product moment correlation coefficient or the cosine measure (cosine distance) can be used to evaluate the item relevance).

The correspondence between each candidate category and item (hereinafter, item-category correspondence) may be derived by the information selection device 10 or may be supplied to the information selection device 10 from the outside. The item-category correspondence may be defined in advance as, for example, a table in which item identifiers and category identifiers are associated (that is, item-category correspondence table). The item-category correspondence may be defined by defining a category to which the item belongs as one of item attributes in a table or database indicating item information.

The information selection device 10 or an external device may analyze which category each item is similar to and derive an item-category correspondence. For example, the item-category correspondence may be derived using the dissimilarity degree indicating the strength of the relationship between each item and each category. Specifically, for each item, select a category with a heterogeneous relevance level equal to or greater than a predetermined value, or a predetermined number of categories in descending order of the heterogeneous relevance, determine that the item belongs to the selected category, and otherwise What is necessary is just to determine not belonging to the category. Note that the predetermined number may be set to 1, and the correspondence between the category having the highest degree of heterogeneity and the item may be derived.

The heterogeneous degree of association may be supplied from the external device to the information selection device 10 as a table in which the item identifier, the category identifier, and the heterogeneous degree of association are associated with each other (ie, the heterogeneous association degree table). In addition, in order to derive the degree of dissimilarity, it may be derived using a category dictionary describing keywords for each category as described in Patent Document 1. For example, a value obtained by dividing the number of appearances for each category of keywords (for example, product name, product description, program name, performer name) included in each item by the total number of appearances of the keyword included in each item (keyword for each category Relative appearance rate) may be used as the degree of dissimilarity.

As another method, the degree of dissimilarity is based on a usage history in which a user is associated with an item used by the user, and a degree of interest in each category of each user (category interest level).
May be calculated using the category interest data collected by questionnaire survey or the like. Specifically, for users (user group B) using a certain item (item A),
The sum of the category interests of the user group B for each category (total for each category) may be calculated, and a value obtained by dividing the total for each category by the total of the category interests for the user group B may be used as the dissimilarity. For example, the item A is used by three users (U1, U2, U3), and U1 has an interest degree of “1.0”, an interest degree of “category 2” of “0.5”, and U2. Interest level for category 1 is “0.8”, interest level for category 2 is “0.3”, U3
If the degree of interest in category 1 is “0.0” and the degree of interest in category 2 is “0.4”, the total degree of interest in category 1 is “1.0 + 0.8 + 0.0 = 1.8”, Category 2
The total degree of interest is “0.5 + 0.3 + 0.4 = 1.2”. Also, the total sum of the interest levels of the three users is “1.0 + 0.5 + 0.8 + 0.3 + 0.0 + 0.4 = 3.0”.
The dissimilarity between item A and category 1 is “1.8 ÷ 3.0 = 0.6”, and the dissimilarity between item A and category 2 is “1.2 ÷ 3.0 = 0.4”. Become.

Instead of calculating the total value of category interest, the number of users (supporting users) whose category interest is greater than or equal to a predetermined value (eg, “0.5”) is counted for each category, and the number of supporting users Is divided by the total number of users using the item A, and the degree of dissimilarity between the item A and each category may be used. For example, if the number of users with an interest level of 0.5 or more is counted in the above example, the number of support users in category 1 is “2” and the number of support users in category 2 is “1”. The dissimilarity between category 1 is “2 ÷ 3 = 0.667”, and the dissimilarity between item A and category 2 is “1 ÷ 3 = 0.333”. Further, the degree of heterogeneity may be calculated using the number of times the user has used the item.

Each of the plurality of category priorities only needs to reflect the magnitude of the item relevance indicating the strength of relevance between the item belonging to each candidate category and the reference item. Accordingly, each of the plurality of category priorities may be calculated as a function of the item relevance. For example, each of the plurality of category priorities may be a value corresponding to a value obtained by adding the item relevance of each of the plurality of items belonging to each candidate category and the reference item. Each of the plurality of category priorities may be a value corresponding to the number of items whose item relevance with the reference item exceeds a predetermined threshold among a plurality of items belonging to each candidate category. The plurality of category priorities may be calculated by the information selection device 10 (that is, the category selection unit 1071) or may be supplied to the information selection device 10 from the outside.

FIG. 4 is a flowchart showing an example of a recommended category selection procedure by the category selection unit 1071. FIG. 4 shows an example in which the category selection unit 1071 calculates the category priority. In step S10, the category selection unit 1071 determines a reference item. For example, an item selected by the user for a reason such as “I want to know information about the item in detail” is set as the reference item. Further, an item included in a history of a user who receives recommendation information (for example, a usage history, a purchase history, a browsing history, a viewing history, an evaluation history, or a search history) may be used as a reference item. In step S11, the category selection unit 1071 acquires the item relevance between the reference item and other items. As already described, the item relevance may be dynamically calculated by the information selection device 10 or may be set statically by an operator or the like. In step S12, the category selection unit 1071 calculates a category priority for each candidate category. In step S13, the category selection unit 1071 determines a recommended category from among a plurality of categories in consideration of the category priority.

As described above, in the present embodiment, the category priority, which is a scale for determining the recommended category, reflects the magnitude of the item relevance between each item belonging to each candidate category and the reference item. Therefore, there is a high possibility that the recommended category determined based on the category priority is associated with an item having relevance, similarity, or correlation with the reference item. Therefore, according to the information selection device 10 and the recommended category determination method using the information selection device 10 described in the present embodiment, a recommended category that has a high possibility of having a recommended item having a high relationship with the reference item is determined. be able to.

Next, the item selection unit 1072 in FIG. 3 will be described. The item selection unit 1072
It operates to select a recommended item from items belonging to the recommended category determined by the category selection unit 1071. The item selection unit 1072 may select a recommended item based on the item relevance level. Thereby, the possibility that an item having a high relationship with the reference item can be selected as the recommended item is improved.

Specifically, the item selection unit 1072 may preferentially select items belonging to the recommended category that have a high item relevance with the reference item as recommended items.
For example, the item selection unit 1072 may select a predetermined number of items as recommended items in descending order of the items related to the reference item among items belonging to the recommended category. Moreover, the item selection part 1072 is good also as a recommended item that the item relevance degree with a reference | standard item is more than predetermined value among the items which belong to a recommendation category.

However, the item selection unit 1072 may appropriately include an item having a low item relevance among a plurality of recommended items. Thereby, the item selection unit 1072 can select an item having a relatively low item relevance with the reference item as a recommended item compared to other items belonging to the recommended category, and therefore can recommend a highly unexpected item to the user. There is a case. This can potentially create new usage opportunities for items (eg, purchase opportunities, viewing opportunities).

<Second Embodiment>
In the present embodiment, a modified example of the recommended category determination procedure by the information selection apparatus 10 described in the first embodiment will be described. In 1st Embodiment, the example which calculates | requires the category priority of each candidate category using the item relevance degree of each item and reference | standard item which belong to a candidate category was demonstrated. On the other hand, the information selection apparatus 10 according to the present embodiment further uses not only the item relevance but also the heterogeneity relevance between the reference item and the candidate category in order to obtain the category priority of each candidate category.

For example, the category priority can be obtained as follows. The item association degree between the reference item x and the other item y is W [x] [y], and the heterogeneous association degree between the reference item x and the candidate category z is S [x] [z]. In this case, the product of W [x] [y] and S [x] [z] (W [x] [y] × S [y] [z]) is set to all items y belonging to the candidate category z (where A value V [x] [z] added for (excluding item x) may be calculated, and V [x] [z] may be set as the category priority of the candidate category z. In addition, among items belonging to the candidate category z, a predetermined number of items in the descending order of W [x] [y] or items having W [x] [y] greater than or equal to a predetermined value are targeted (W [x ] [Y] × S [x] [z]) added value V [x
] [Z] may be calculated and the value may be used as the category priority of the candidate category z. W [
A value WA obtained by adding x] [y] to all items y (excluding the item x) belonging to the candidate category z is calculated, and the weighted addition value of WA and S [x] [z] is used as the category priority. It may be a degree.

Furthermore, when the dissimilarity S [y ′] [z] between the candidate category z and all the items y ′ (excluding the item x) can be used, the item is not limited to the items belonging to the candidate category z, The category priority calculation process may be performed for all items y ′ (excluding the item x). For example, the product of W [x] [y ′], S [x] [z] and S [y ′] [z] (W [x] [y ′] × S [x] [z] × S [ y ′] [z]) is added to all items y ′ (excluding the item x) to calculate a value V ′ [x] [z], and V ′ [x] [z]
] May be the category priority of the candidate category z. W [x] [y ′] and S [y ′]
A value V ′ [x] [z] obtained by adding the product (W [x] [y ′] × S [y ′] [z]) to [z] for all items y ′ (excluding the item x) ] And V ′ [x] [z] may be set as the category priority of the candidate category z. Further, in such a calculation method, a binary determination such as whether or not an item other than the reference item (item y ′) belongs to the candidate category z is not performed, and the heterogeneity relevance S [y ′] [Z] is used as correspondence information between the item y ′ and the candidate category z. That is, the item relevance W [x] [y ′] between the reference item x and other items (item y ′), and the correspondence information S [y ′] [z between the item y ′ and the candidate category z ] Is used to calculate the category priority. S [y '] [
If z] is greater than 0, item y ′ may correspond to candidate category z. Further, when S [y ′] [z] is equal to or greater than a predetermined value, it may be determined that the item y ′ belongs to the candidate category. That is, it can be said that the information on whether an item belongs to a category is one of the correspondence information.

The configuration example of the information selection apparatus 10 according to the present embodiment is the configuration example shown in the first embodiment (FIG. 3).
). FIG. 5 is a flowchart showing an example of a recommended category selection procedure by the information selection apparatus 10 (ie, the category selection unit 1071) according to the present embodiment. The processes in steps S20 and S21 in FIG. 5 are the same as the processes in steps S10 and S11 in FIG. In step S22 of FIG. 5, the degree of heterogeneity between the reference item and each candidate category is acquired. This dissimilarity degree may be dynamically calculated by the information selection device 10 or may be set statically by an operator or the like. Note that a specific example of the method for calculating the degree of heterogeneity has been described in the first embodiment, and thus a duplicate description is omitted here. In step S23, the category selection unit 1071 calculates a category priority for each candidate category. In step S24, the category selection unit 1071 determines a recommended category from among a plurality of categories in consideration of the category priority.

As described above, the category priority used in this embodiment reflects the magnitude of the degree of item relevance between each of the items corresponding to each candidate category and the reference item, as in the first embodiment. . For this reason, this embodiment can reduce possibility that the item which has high relationship with a reference | standard item does not exist in a recommendation category.

Furthermore, in this embodiment, the example which calculates | requires a category priority as a function of the dissimilarity degree of a reference | standard item and a candidate category was shown. As a result, it is possible to determine a recommendation category that has a strong relationship, similarity, or correlation with the reference item and that is highly likely to be sufficiently associated with an item that has a high relationship with the reference item. it can.

Note that the category priority of the candidate category may be determined so as to increase as the degree of dissimilarity between the reference item and the candidate category increases. Thereby, the category priority of a candidate category with strong relevance, similarity, or correlation with a reference item can be increased. However, the relationship between the category priority of the candidate category and the heterogeneous relevance is such that the category priority of the candidate category having a low heterogeneous relevance or a medium candidate category is lower than that of the candidate category having a very high dissimilar relevance with the reference item. May be set to be high. Thereby, there is a possibility that highly unexpected items can be recommended to the user. For example, the category priority of the candidate category may be calculated as a function of the extended relevance obtained by inputting the dissimilarity with the reference item into a predetermined conversion rule. A specific example of the expanded relevance will be described in detail in a third embodiment described below.

<Third Embodiment>
In the present embodiment, a specific example of the recommended category determination procedure by the information selection device 10 described in the second embodiment will be described. In this embodiment, a specific example of calculation of category priority and item relevance necessary for determining a recommended category will also be described. Furthermore, in this embodiment, some variations regarding the category priority calculation method will be described.
In some of these variations, the reference category corresponding to the reference item is specified, and the category priority is calculated using the category relevance indicating the strength of relevance between the reference category and each candidate category. There may be a plurality of reference categories corresponding to one reference item.
Here, the reference category is a category having high relevance, similarity, or correlation with the reference item. The reference category can also be said to be a category corresponding to the reference item, a category to which the reference item belongs, or a category related to the reference item.

Further, by treating the strength of the relationship between the reference item and the reference category as always constant (for example, “1.0”), the category relevance between the reference category and the candidate category can be increased between the reference item and the candidate category. It becomes the same value as the dissimilarity. That is, the category relevance described in the present embodiment corresponds to one of the specific examples of “the dissimilar relevance between the reference item and the candidate category” described in the second embodiment.

<Overall system configuration>
A configuration example of the system 1 including the information selection apparatus 10 according to the present embodiment is the same as the example described in the first embodiment (for example, FIGS. 1 and 2). Hereinafter, the configuration example illustrated in FIG. 1 will be described as an example.

<Information selection device>
FIG. 6 is a block diagram illustrating a configuration example of the information selection device 10 according to the second embodiment.
The information selection device 10 includes the information selection unit 107 as described in the first embodiment. Further, in the configuration example of FIG. 6, the information selection device 10 includes a metadata storage unit 101, a usage history storage unit 1.
02, category relevance storage unit 103, relevance calculation unit 104, item relevance storage unit 105
, Display control information creation unit 106, recommendation information storage unit 108, transmission / reception unit 109, and control unit 11
0. 6 includes a display device 120 (for example, a liquid crystal display (LCD)) for displaying information necessary for the administrator of the information selection device 10.
An OELD (Organic Electroluminescent Display) and a CRT (Cathode Ray Tube) display) and an input device 130 (for example, a keyboard, a mouse, and a touch panel) for operation by an administrator are connected.

As described above, the information selection apparatus 10 may be configured using a general computer system. For example, the information selection device 10 can be configured using a general computer including a CPU, RAM, ROM, nonvolatile storage device (for example, HDD (Hard Disc Drive), flash memory), network interface, and the like. That is, a general computer functions as the information selection device 10 according to the present embodiment by executing a computer program for performing processing / algorithms related to selection of recommended categories and recommended items as described below. Will be able to.

The metadata storage unit 101 stores item information, category information, and information indicating the correspondence between items and categories. Item information includes an item identifier and an item attribute (
For example, item name, item description, category to which the item belongs). The category information includes a category identifier and category attributes (category name, category type, category description). The correspondence relationship between items and categories may be defined as an item-category correspondence table as described in the first embodiment. Further, the correspondence between items and categories may be defined by defining the category to which the item belongs as one of the item attributes in the item information.

FIG. 7A shows an example (101) of an item information table 101A for recording item information.
A-1) is shown. As shown in the figure, the item information table 101A is a table that associates item identifiers (item IDs) with item attribute information. In the example of FIG. 7A, the item attribute information includes “title (item name)”, “description”, and “item time” of the item. The item attribute information “description” is an outline or summary of the item,
The background explanation etc. produced are shown. The “item time” indicates an item creation time (time), a time when an item is registered in the item providing server 20, or an item supply start time. In the example of FIG. 7A, a date such as “January 1, 2010” is used as an expression format of time (time), but other expression formats may be used. For example, “January 1, 2010 1
The date and time up to a second unit such as “0:15:20” or the date up to a millisecond unit may be used. Alternatively, a monthly expression format such as “January 2010”, a quarterly expression format such as “2010 1Q”, an annual expression format such as “2010”, “200
It may be an expression format of a rough age than the year unit such as “0's”.

FIG. 7B shows another example (101A-2) of the item information table 101A. The difference between the tables 101A-2 and 101A-1 is that category information (for example, category name, category identifier) is stored as one item attribute information. In the example shown in FIG. 7B, a maximum of two categories are associated with one item. of course,
Three or more categories may correspond to one item, or one item
Only one category may be associated. The item identifier “I” in FIG.
Category 2 of “temID-2” is “NUL” which means that there is no corresponding data.
L ”. In the following description of the present embodiment, the item information table 101A-1 is used. However, when the item information table 101A-2 is used instead, the item-category correspondence table 101C described later is omitted. Can do.

FIG. 7C shows an example of a category information table 101B that records category information. As shown in the figure, the category information table 101B is a table that associates category identifiers (category IDs) with category attribute information. Categories have already been mentioned,
An index for classifying items. At least one category is set for one item. In other words, one item is associated with at least one category.

In the example of FIG. 7C, the category attribute information includes “category type”, “category name”, and “category description”. The category type (category type) represents the type of category (major classification). For example, an item “creator” can be a category type. The category belonging to the category type = “Creator” includes item producers, directors, producers, writers, composers, songwriters, performers, performers, and the like. That is, the names of directors and producers correspond to “category names”.

Further, “genre” may be used as the category type. For example, when the item is music content, the category (category name) belonging to the category type = “genre” includes rock, jazz, classic, folk, blues, and the like. When the item is movie content, the category (category name) belonging to the category type = “genre” is SF, action,
Includes comedy, anime and suspense.

Further, “region” may be used as the category type. For example, category type = "region"
The category names belonging to include country or region names such as Japan, the United States, the United Kingdom, France, and Tokyo.

Further, as the category type, “mood” representing an atmosphere of an item or an atmosphere suitable for using the item may be used. In this case, the category (category name) belonging to the category type = “mood” may be terms such as healing, exciting, dramatic, simple, and astringent.

A “situation” indicating a situation suitable for using an item may be a category type. For example, the category (category name) belonging to the category type = “situation” includes before sleeping, jogging, train, driving, studying, and the like. When the item is music content and “mood” or “situation” is used as a category type, the information selection device 10 or other device is disclosed in JP 2007-322598 (JP 2).
007-322598 A) Music may be classified into categories using the technique disclosed in A).

Further, the category group related to the price range and discount rate of the item may be classified into one category type. For example, the category (category name) belonging to the category type includes high-priced products, middle range price ranges, bargains, 20% OFF, 50% OFF, and the like. Further, a category group related to the size and color of the item may be classified into one category type. Of course, the item attribute information and the category attribute information given here are merely examples, and are not limited to these examples. For example, the item attribute may include item price information.

FIG. 7D shows an example of the item-category correspondence table 101C. The item-category correspondence table 101C is a table for associating item identifiers with category identifiers, and can associate any number of category identifiers with one item identifier. The item-category correspondence table 101C can associate any number of item identifiers with one category identifier. In the example shown in FIG. 7D, three category identifiers correspond to “ItemID-1”, one “ItemID-2”, and two “ItemID-3”. When a plurality of categories correspond to one item, the category types may be the same or different.

As described in the first embodiment, the information selection device 10 may acquire information (that is, metadata) related to an item or a category from an external device as necessary. In this case, the metadata storage unit 101 may be omitted. For example, the information selection apparatus 10 may acquire metadata from the item providing server 20 (eg, an item storage unit 202 described later).

The transmission / reception unit 109 is connected to the network 40 (in the case of the configuration example of FIG.
Data is transmitted to and received from the item providing server 20 or the terminal device 30 via 2).

The control unit 110 performs various processes for performing overall control of the information selection device 10.
For example, as will be described later, the control unit 110 includes the item providing server 20 or the terminal device 30.
The usage request transmitted from the user is received via the transmission / reception unit 109, and usage history information that associates the user identifier and the item identifier included in the usage request is stored in the usage history storage unit 102.

The usage history storage unit 102 stores information indicating an item usage history by the user. The item usage history may be stored, for example, in a table format (referred to as item usage history table 102). In the present embodiment, item use is executed by the item providing server 20 providing an item in response to a use request from a user. In this embodiment, an example in which a user is identified using a user identifier (user ID) will be mainly described.
For example, when a mobile phone is used as the terminal device 30, a terminal identifier (terminal ID) may be used instead of the user identifier. The terminal identifier can be obtained, for example, when the information selection device 10 or the item providing server 20 and the terminal device 30 are connected. For example, when a personal computer is used as the terminal device 30, C is used instead of the user identifier.
The web browser or the terminal device 30 may be identified using cookie or identification information similar thereto. In the following, the user identifier and the terminal identifier are combined to form a “user entity identifier”.
Called.

The item usage history table 102A can adopt various storage forms for storing usage history information. For example, as shown in the item usage history table 102A-1 in FIG. 8A, the user identifier and the item identifier can be stored in association with each other. In this example, one usage request corresponds to one row of the table 102A-1. As can be seen from the combination of “UserID-1” and “ItemID-3” in both the first and fourth rows of the table, even if the combination of the user identifier and the item identifier is the same, each usage request Table row data is added. For this reason, other processing units (for example, the relevance calculation unit 104) can easily count the number of uses for each item indicated by the item identifier and the number of users used for each item. When a plurality of item identifiers are included in one use request, as many table rows as the number of item identifiers may be allocated and stored.

The item use history table 102A-2 illustrated in FIG. 8B is an example in which a user identifier, an item identifier, and an item use time are stored in association with each other. Similar to the item usage history table 102A-1 shown in FIG. 8A, one usage request is sent to the table 102A.
-2 corresponds to one line. When the usage request indicates the item usage time, the control unit 11
0 stores the item use time information extracted from the use request in the table 102A-2. When the item use time information is not included in the use request, the control unit 11
For example, the time (time) when the information selection device 10 receives the use request may be used as the use time information by using, for example, a clock built in the information selection device 10.

In the example of FIG. 8B, the expression format of the item usage time is “January 1, 2010 10
Date and time up to a second unit such as “hour 15 minute 20 seconds” is used. However, the item use time may be expressed in an arbitrary format such as a date and time up to a millisecond unit, a date up to a day unit, a month unit, and a year unit. Note that the usage request may include an evaluation value (for example, a numerical value indicating the degree of likes and dislikes such as like = 3, neither is = 2, and dislike = 1) by the user. In this case, the item usage history table 102A-2 may store the user identifier, the item identifier, the item usage time, and the item evaluation value in association with each other.

The item usage history table 102A-3 illustrated in FIG. 8C is an example in which a user identifier, an item identifier, and an item usage count are stored in association with each other. As will be described later, when the relevance calculation unit 104 does not use the item use time, the item use history table 102A
By using -3, the storage capacity of the usage history storage unit 102 can be reduced. When an evaluation value (item evaluation value) for an item by the user is included in the usage request, the item usage history table 102A-3 associates the user identifier, the item identifier, the item usage count, and the latest item evaluation value. It may be stored.

The category relevance storage unit 103 stores the category relevance level for each combination in a plurality of categories. The category relevance may be stored in a table format (referred to as a category relevance table 103A), for example. FIG. 9A shows a category relevance table 103.
The table 103A-1 which is an example is shown. The table 103A-1 associates the category identifiers of two categories (that is, the reference category identifier and the related category identifier) with their category relevance. The category relevance can be represented by a numerical value of 0 or more and 1 or less, for example. Here, when the value of the category relevance is 1, it indicates that the relevance, similarity, or correlation between the two categories is the highest, and when it is 0, it indicates the lowest. For example, FIG. 9A shows “CategoryID-1” and “CategoryID”.
-2 "indicates that the category relevance is" 0.85 ".

FIG. 9B shows a table 103 that is another example of the category relevance table 103A.
A-2 is shown. The table 103A-2 is a matrix format table. This matrix is a symmetric matrix in which relevances of the same category are arranged as “1” on a diagonal line. The category relevance table 103A may be created by other methods. In the present embodiment, as will be described later, the relevance calculation unit 104 calculates the category relevance using the data stored in the usage history storage unit 102, and creates the category relevance table 103A. However, the category relevance may be calculated using other methods.

The recommendation information storage unit 108 records recommendation information including the recommendation category and the recommended item selected by the information selection unit 107. The recommendation information is, for example, a table format (recommendation information table 1
May be stored as “08A”. The recommendation information table 108A associates a reference item identifier, a recommendation category identifier, and a recommended item identifier. Table 108A
In addition to these identifiers, at least one of the recommendation rank of the recommendation category and the recommendation rank of the recommended item may be recorded.

FIG. 10 shows an example of the recommendation information table 108A. The reference item identifier corresponds to the item identifier included in the recommendation request that serves as a trigger for outputting recommendation information. The recommendation request will be described later. The recommended category identifier is an identifier of a category recommended corresponding to the reference item identifier. The recommended item identifier is an identifier of an item having a high degree of association with the reference item among the items belonging to the recommended category. That is, the recommended category identifier and the recommended item identifier associated in the recommendation information table 108A are also associated in the item-category correspondence table 101C.

As shown in the example of FIG. 10, the number of recommended category identifiers (recommended categories) may be different for each reference item identifier (reference item). The number of recommended item identifiers (recommended items) corresponding to each recommended category identifier (recommended category) may be different. There may be only one recommended category identifier corresponding to a certain reference item identifier. However, in the present embodiment, the recommendation information table 108A desirably includes one or more reference item identifiers having a plurality of recommended category identifiers.

The category ranking in FIG. 10 indicates the recommendation ranking of the recommended category for each reference item identifier. In the example of FIG. 10, a category with a lower category rank number has a higher priority,
Preferentially presented to the user. The item ranking indicates the recommendation ranking of the recommended item for each combination of the reference item identifier and the recommendation category identifier. In the example of FIG. 10, it is assumed that an item with a smaller item rank number has a higher priority rank and is presented to the user with priority. In FIG. 10, for each reference item identifier, recommended category identifiers are stored in descending order of category ranking. However, when storing the recommended category identifier in association with the category order, the recommended categories may be stored in an appropriate order. The same applies to the storage order of recommended item identifiers.

Instead of the category ranking and the item ranking, a recommendation level indicating that the priority is higher as the numerical value is larger may be used. That is, a category or item having a higher recommendation level is preferentially presented to the user. In addition, the recommendation information table 108A may be created according to a rule for storing recommendation category identifiers in order of recommendation recommendation (or order of recommendation recommendation) for each reference item identifier. Thereby, the storage of the category recommendation order in the table 108A can be omitted. Further, the item recommendation order may be omitted by applying the same rule to the recommended item identifier. Alternatively, all recommended category identifiers (or all recommended item identifiers) recorded in the table 108A may be treated as the same recommendation order, or for the recommended categories (or recommended items) read from the table 108A. The recommendation order may be given at random.

The relevance calculation unit 104 calculates the item relevance for each item combination using the data stored in the usage history storage unit 102 and stores them in the item relevance storage unit 105. As described in the first embodiment, the item relevance may be defined in advance by an administrator or an operator, or may be supplied to the information selection device 10 from an external device. In this case, the degree-of-association calculation unit 104 may be omitted.

Further, the relevance calculation unit 104 may calculate a category relevance level for each category combination, and store the calculated category relevance level in the category relevance storage unit 103.

The item association degree storage unit 105 stores the item association degree of each of the plurality of item combinations calculated by the association degree calculation unit 104. The item association degree may be stored in a table format (referred to as an item association degree table 105A), for example. FIG. 11 shows an example of the item association degree table 105A. In the example of this figure, the reference item identifier “It
L1 related item identifiers corresponding to “emID-1”, reference item identifier “Item”
L2 related item identifiers corresponding to “ID-2” are stored. Here, L1 and L2 may be the same number or different. That is, the table 105A may store the same number of related item identifiers for all reference item identifiers, or may store an arbitrary number of related item identifiers for each reference item identifier. Table 105
A may store the item relevance level for all item combinations calculated by the relevance level calculation unit 104, or only related item identifiers having a relatively high item relevance level with a certain reference item identifier may be stored in the related item set. May be stored as By storing only a part, the storage capacity of the item association degree storage unit 105 can be reduced. Also,
As illustrated in FIG. 11, the table 105A may store related item identifiers in descending order of item relevance for each reference item identifier.

The number of related item identifiers corresponding to one reference item identifier is basically plural, but may be one. However, it is desirable that the related item identifier is 2 or more in at least one reference item identifier. It should be noted that the item relevance level calculated by a device other than the information selection device 10 may be recorded in the item relevance level table 105A, and in this case, the relevance level calculation unit 104 may be omitted.

The information selection unit 107 includes a category relevance storage unit 103 and an item relevance storage unit 105.
The recommended category and the recommended item to be presented to the user are selected using the data stored in. Then, the information selection unit 107 creates recommendation information including a recommendation category and a recommended item, and stores it in the recommendation information storage unit 108 (for example, the recommendation information table 108A).

<Item provision server>
The item providing server 20 provides an item or information related to the item in response to a request from the terminal device 30. FIG. 12 is a block diagram illustrating a configuration example of the item providing server 20. In the example of FIG. 12, the item providing server 20 includes a user management unit 201, an item storage unit 202, a data storage unit 203, a transmission / reception unit 204, and a control unit 205.

The item providing server 20 may be configured using a general computer system. For example, the item providing server 20 can be configured using a general computer including a CPU, a RAM, a ROM, a nonvolatile storage device (for example, an HDD (Hard Disc Drive), a flash memory), a network interface, and the like. That is, a general computer executes a program for performing processing / algorithms related to provision of items or item information as described below, thereby providing the item providing server 2.
It can function as 0.

The transmission / reception unit 204 is connected to the network 40 (in the case of the configuration of FIG.
), Data is transmitted and received between the information selection device 10 and the terminal device 30. Control unit 2
05 controls the entire item providing server 20.

The user management unit 201 stores a user identifier for identifying a user who uses the terminal device 30 or a terminal identifier for identifying the terminal device 30, that is, a use subject identifier.
The item providing server 20 performs, for example, an admission process before the user starts using the item, and stores a use subject identifier related to the user who has completed the admission process in a storage unit (not shown). In addition, the item providing server 20 may store user attribute information such as a login name, password, name, date of birth, contact information, and payment method in the user management unit 201 in association with the use subject identifier.

The item storage unit 202 stores information related to items provided by the item providing server 20. The item storage unit 202 stores the same information as the metadata storage unit 101 of the information selection device 10. However, if the item is digital content and the network 40
When the item can be provided to the terminal device 30 via the terminal (that is, content distribution), the item storage unit 202 includes an item identifier and an item body (digital content) in addition to the data of the metadata storage unit 101. Are stored in association with each other.

Note that the control unit 205 transmits the data in the item storage unit 202 to the information selection device 10 via the transmission / reception unit 204 according to the update of the item storage unit 202 or according to a predetermined schedule, and the metadata. You may make it store in the storage part 101. FIG. On the contrary, the control unit 205 receives the data in the metadata storage unit 101 from the information selection device 10, and
You may make it store in the item storage part 202. FIG. Alternatively, the information selection device 10
A message requesting item attribute information may be transmitted to the item providing server 20. Then, the control unit 205 may read data corresponding to the request message from the item storage unit 202 and transmit the data to the information selection device 10 via the transmission / reception unit 204.

The data storage unit 203 can store various data. For example, the data stored in the recommended information storage unit 108 of the information selection device 10 can be copied and stored in the data storage unit 203. In this case, since the terminal device 30 can receive the recommendation information from the item providing server 20, the processing load on the information selection device 10 can be reduced. The data storage unit 203 may store data similar to the usage history storage unit 102 of the information selection device 10. In this case, it is possible to refer to the data storage unit 203 from the information selection device 10 and omit the usage history storage unit 102 of the information selection device 10.

<Terminal device>
The terminal device 30 is a device used by a user. FIG. 13 is a block diagram illustrating a configuration example of the terminal device 30. In the example of FIG. 13, the terminal device 30 includes a control unit 301 and a transmission / reception unit 302.
A browser unit 303 and an application unit 304. The terminal device 30 is a CPU
, RAM, ROM, non-volatile storage device (for example, HDD (Hard Disc Drive), flash memory), network interface, etc. That is, a general computer can function as the terminal device 30 by executing a program for performing an algorithm and processing as described below. Further, the terminal device 30 as a computer system includes:
A mobile phone, a smartphone, a tablet computer, or the like having a Web browser function may be used.

The control unit 301 performs overall control of the terminal device 30. The transmission / reception unit 302 transmits / receives data to / from the terminal device 30 or the information selection device 10 via the network 40 (in the case of the configuration of FIG. 2, the network 42 is further).

The browser unit 303 is an HTTP (Hypertext Transfer Protocol) protocol, HTTP
Using the PS (HTTP over Secure Socket Layer) protocol, the server (for example, WW
A Web page including an HTML (Hypertext Markup Language) document, an image, and the like is acquired from a W (World Wide Web) server), and information based on the Web page is output to the display device 320. The browser unit 303 may further have a function of interpreting and executing a program described in a script language or the like. For example, the function of the browser unit 303 can be realized by the terminal device 30 as a computer system executing a Web browser program.

The application unit 304 performs various processes in the application layer according to the usage of the terminal device 30 or a user instruction. The function of the application unit 304 can be realized by the terminal device 30 as a computer system executing various application programs.

When the terminal device 30 is, for example, a personal computer, a display device 320 (for example, LCD, OELD, CRT display) and an input device 330 (for example, keyboard, mouse, touch panel, track) for receiving operation instructions from the user Ball, remote control) may be connected. When the terminal device 30 is a mobile phone terminal, a smartphone, a tablet computer, or the like, generally, the display device and the input device are integrally arranged on the terminal device 30. In the following description, it is assumed that the display device 320 and the input device 330 are connected to the terminal device 30 for convenience.

<System operation>
<Operation of the entire system>
With reference to the flowchart of FIG. 14, an example of operation | movement of the system 1 whole containing the information selection apparatus 10, the item provision server 20, and the terminal device 30 is demonstrated. First, step S100
The terminal device 30 uses the browser unit 303 to execute the UR of the item providing server 20.
L (Uniform Resource Locator) is accessed. Specifically, the item providing server 20
A request to a predetermined Web page provided by the user (use start request) is transmitted to the item providing server 20.

When the terminal device 30 is, for example, a personal computer, the terminal device 30 may accept input of a login name (user ID) and a password by the user and transmit a use start request including these. In this case, HTML (Hyper Text Markup Language) data and the like for accepting the input of the login name and password may be transmitted from the item providing server 20 to the terminal device 30 before step S100. Alternatively, the terminal device 3
In order to omit the transmission of the login name and password, 0 may transmit a use start request including data that can identify a user who uses the terminal device 30 by using Cookie or a similar technique. Further, when the terminal device 30 is a mobile phone terminal, a smartphone, a tablet computer, or the like, a use start request including a terminal identifier may be transmitted in order to omit transmission of a login name and a password.

In step S <b> 110, the control unit 205 of the item providing server 20 performs the terminal device 30.
A usage start request is received via the transmission / reception unit 204, and the user management unit 201 is referred to determine whether or not the user is a registered user. Specifically, when the use start request includes a login name and a password, the control unit 205 converts them into the user management unit 20.
1 may be compared with the login name and password stored in 1. When the use start request includes the terminal identifier, the control unit 205 may determine whether or not the use start request matches the use subject identifier stored in the user management unit 201. If it is a registered user or terminal (Yes in S110), the process proceeds to step S130, and if not (No in S110).
Advances to step S120.

In step S <b> 120, the control unit 205 of the item providing server 20 transmits a Web page (HTML) for membership processing to the terminal device 30 via the transmission / reception unit 204. Although not shown in the figure, a user who uses the terminal device 30 uses the input device 330 to input necessary information on the Web page for the membership process, and transmits it to the item providing server.
Thereby, the item providing server 20 performs a membership process including storing the information in the user management unit 201. The terminal device 30 may transmit a use start request again after the membership processing is completed.

In step S <b> 130, the control unit 205 of the item providing server 20 creates Web page response data corresponding to the use start request while referring to the item storage unit 202, and transmits the response data to the terminal device 30 via the reception unit 204. . This response data includes information introducing at least one of a category and an item. The response data is composed of HTML data, image data, video data, audio data, and the like, and may be transmitted to the terminal device 30 in a plurality of times. The response data may include information for identifying the user or the terminal device 30 using a technique such as Cookie.

In step S <b> 140, the terminal device 30 receives the response data from the item providing server 20 and displays the information on the display device 320. An example of the display screen is shown in FIG. The example of this figure is a display screen for introducing “new arrival items” that the item providing server 20 has recently started providing. Information for introducing an item as shown in the figure can be transmitted from the item providing server 20 to the terminal device 30 at various timings.

In FIG. 15, “Item ABC” is the title of the first item, “SF” is the category name to which the first item belongs, and the display “This item is a movie produced in 2001…” It is description (item description) regarding the first item. Further, the example of FIG. 15 includes a display element 1401 indicating a button, a link, or the like (referred to as a related item link or related link) for providing item information related to the first item. The example of FIG. 15 includes a display element 1402 that indicates a button, a link, or the like (hereinafter referred to as a usage link) for using the first item.

The user can select a related link (display element 1401) or a usage link (display element 1402) by an operation such as a click using the input device 330. Although not displayed on the display screen, the response data includes an item identifier of each item, and each related link and usage link is associated with the item identifier of the item to be selected. .

Returning to the flowchart of FIG. In step S <b> 150, the terminal device 30 determines whether a related link has been selected by the user via the input device 330.
When the related link is designated (Yes), the process proceeds to step S160, and when the related link is not designated (No), the process proceeds to step S190.

In step S160, the terminal device 30 transmits a request (recommendation request) to the URL corresponding to the related link. In the present embodiment, a case in which the related link corresponds to a predetermined URL of the information selection device 10 will be described. However, the related link may correspond to a predetermined URL of the item providing server 20. The recommendation request includes the item identifier of the item selected on the display screen shown in FIG. For example, when the user clicks a related link corresponding to the first item “item ABC” shown in FIG. 15, the recommendation request includes the item identifier of “item ABC”. Hereinafter, the item identifier included in the recommendation request is referred to as a “request criterion identifier”. Further, the recommendation request may include information regarding the number of necessary recommendation information (for example, the number of recommended categories, the number of recommended items of each recommended category, etc.). In addition, the recommendation request includes a use subject identifier (i.e.,
A user identifier or a terminal identifier of a user who uses the terminal device 30 may be included.

In step S170, the control unit 110 of the information selection device 10 receives a recommendation request via the transmission / reception unit 109, and performs display control of display recommendation data (that is, display control information) corresponding to the request reference identifier included therein. The information creating unit 106 creates the information. Then, the control unit 110 transmits the created display recommendation data to the terminal device 30.

The display control information creating unit 106 refers to the recommendation information table 108A of the recommendation information storage unit 108 shown in FIG. And read. In addition, the display control information creation unit 106 reads out a corresponding recommended item identifier and item order for each recommended category identifier. Furthermore, the display control information creation unit 106 reads out category attribute information (for example, category type, category name, category description) corresponding to the read recommended category identifier from the category information table 101B. Furthermore, the display control information creation unit 106 reads item attribute information (eg, title, item description, item time) corresponding to the read recommended item identifier from the item information table 101A of the metadata storage unit 101. Then, the display control information creating unit 106 creates display recommendation data using the read data.

The display recommendation data (that is, display control information) is data described in HTML (Hyper Text Markup Language) that can be interpreted by the browser unit 303 of the terminal device 30. The creation unit 106 may create the recommended display data so that the display position of the category attribute information of the recommended category changes in accordance with the category order on the display device 320 connected to the terminal device 30. Since the category ranking is determined by the category priority, it can be said that the display position of the category attribute information is determined according to the category priority. The creation unit 106 may create the display recommendation data so that the display position of the item attribute information of the recommended item changes according to the item rank. The display recommendation data may include a request standard identifier and attribute information such as a title corresponding to the request standard identifier.

For reference, description will be made using the example shown in FIG. The request criteria identifier is "ItemI
Consider the case of “D-10”. The creation unit 106 generates a request criterion identifier (ItemID
−10) The three recommended category identifiers “Cat” associated with the same reference item identifier
"egoID-100", "CategoryID-102", and "Categor
yID-110 ”and the category rankings“ 1 ”,“ 2 ”, and“ 3 ”are read out. Furthermore, the creation unit 106 has three recommended item identifiers “ItemID-20”, “ItemID-25”, “ItemID-1” associated with “CategoryID-100”.
1 ”and the item rankings“ 1 ”,“ 2 ”, and“ 3 ”are read out. The same applies to other recommended category identifiers. However, the creation unit 106 may read all recommended category identifiers and recommended item identifiers corresponding to the identified reference item identifier, or may read a predetermined number in descending order of category ranking and item ranking. When the recommendation request specifies the number of recommendation information, the creation unit 106 may read the recommended category identifier or the recommended item identifier in the order from the highest recommendation order.

Further, the recommendation request (step S160) may include a use subject identifier. In this case, the creating unit 106 may exclude items used in the past by the use subject identifier included in the recommendation request from the recommended items. Specifically, the creation unit 106 refers to the item usage history table 102A of the usage history storage unit 102 to identify an item identifier (used item identifier) used in the past by the usage subject identifier included in the recommendation request. Then, the used item identifier (used item) may be excluded from the recommendation information. By performing such processing, the user uses the same item only once (for example, purchase, browsing)
In an item providing service that is often performed, highly accurate recommendation is possible. For example,
Digital content once purchased is suitable for a service that can be repeatedly used (reproduced) by the terminal device 30.

Returning to the flowchart of FIG. In step S <b> 180, the terminal device 30 receives display recommendation data from the information selection device 10. The browser unit 303 interprets the display recommendation data and displays the recommendation list on the display device 320 in the format shown in FIG. 16, for example. FIG. 16 is an example of a screen displaying “AAA related items”. In the “AAA” portion, the title of the item corresponding to the request criterion identifier is displayed. The numbers including “1)”, “2)”, and “3)” in FIG. 16 indicate the category rank (that is, the recommendation rank). The text display including “suspense”, “SF”, and “healing” in FIG. 16 indicates the category name of the recommended category. The display order (or display position) of the recommended categories in FIG. 16 is determined according to the category order. Specifically, the category with the higher category ranking may be displayed at a position that is more easily noticed by the user. For example, when information on a plurality of categories is arranged in the vertical direction as shown in FIG. In addition, when information on a plurality of categories is arranged in the left-right direction, a category having a higher category order may be displayed on the left side of the display screen. The text display including “genre” and “mood” in FIG. 16 indicates the category type name. As shown in the example of FIG. 16, the display screen based on the display recommendation data may include a plurality of category types.

The recommended item information may be displayed so that the correspondence with the recommended category can be understood. FIG.
In the example of 6, the information of three items is displayed as recommended items corresponding to the recommended category “suspense”. The numbers 1 to 3 surrounded by circles in FIG. 16 indicate item ranks (that is, recommended ranks). The text display “Item OPQ” indicates the title of the item having the first recommendation order. The text display “This item ca n’t take your eyes off” indicates the explanation of the first item. In the example of FIG. 16, as in the example of FIG. 15, for each item, a “related item display” button associated with the related item link and an “item use” associated with the usage link. Button. For example, the user operates the “Related Item Display” button of “Item OPQ”,
Recommendation information including a recommendation category or a recommendation item related to the “item OPQ” can be further browsed. The same display is performed for the second and subsequent items. The display order (display position) of the attribute information of each item is determined according to the item order. Item ranking is
Since it is determined according to the strength of the item relevance, it can be said that the display order of the item attribute information is determined according to the item relevance.

Although not shown in FIG. 16, display elements (for example, buttons) for displaying further recommended items (related items) for each recommended category may be arranged. For example, a display element including a text such as “See more recommended items of this genre (category)” may be placed near the display of “If you like the suspense (genre)” in FIG. Then, when the user operates the display element, more recommended items of “suspense” corresponding to the request criterion identifier may be displayed.

Returning to the flowchart of FIG. In step S <b> 190, the terminal device 30 determines whether a use link has been selected by the user using the input device 330. This usage link can typically be an item purchase request, but various items such as item playback, item preview, item detailed information display, or registration of evaluation information (evaluation value) for the item, etc. May be requested. If the usage link is selected (Yes), the process proceeds to step S200, and if not (No), the process proceeds to step S250.

In step S200, the terminal device 30 transmits a request (use request) to the URL corresponding to the use link. In the present embodiment, the usage link is the item providing server 2.
A case corresponding to a predetermined URL of 0 will be described. Note that the terminal device 30 may directly transmit a use request to the information selection device 10 in addition to the item providing server 20.

Each use link is given an item identifier of an item to be selected.
Thus, the usage request includes the item identifier (usage reference item identifier) of the item selected by the user and the user entity identifier that identifies the user or the terminal device 30.
When the user uses a plurality of items at a time, the terminal device 30 may transmit one usage request including item identifiers of the plurality of items, or a plurality of usages each including one item identifier. You may send a request.

In step S <b> 210, the transmission / reception unit 204 of the item providing server 20 performs the terminal device 3.
The usage request received from 0 is relayed (transferred) to the information selection device 10. At this time, the control unit 205 of the item providing server 20 may extract information such as a usage reference item identifier and a usage subject identifier from the usage request and store them in the data storage unit 203 as usage information.

In step S220, the control unit 110 of the information selection apparatus 10 receives the usage request via the transmission / reception unit 109, extracts information such as the usage reference item identifier and the usage subject identifier from the usage request, and uses the usage history as the usage history. It is stored in the storage unit 102. Then, the control unit 110 transmits a message indicating that the use history information has been stored to the item providing server 20 via the transmission / reception unit 109.

In step S <b> 230, after receiving the storage end message from the information selection device 10, the control unit 205 of the item providing server 20 performs processing for providing the item to the terminal device 30. For example, when the item to be provided is digital content, the control unit 205 displays the item body corresponding to the item identifier indicated in the use request as the item storage unit 2.
The data is read from 02 and transmitted to the terminal device 30 via the transmitting / receiving unit 204. When the item is an article as a tangible object, a delivery process including sending delivery request information to the delivery company system is performed. At this time, the control unit 205 may perform billing processing.
When the detailed information of the item is requested, the control unit 205 displays the item storage unit 20.
The additional information such as “item description” is read from 2 and transmitted to the terminal device 30.

In step S <b> 240, the terminal device 30 performs a process related to the use of the item provided from the item providing server 20. For example, when the item is digital content, the item is reproduced and displayed. If the item is an article, a message indicating that the delivery process has been accepted is displayed on the screen.

In step S250, the terminal device 30 determines whether or not there is an operation end instruction such as the user closing the browser. When there is an operation end instruction (Yes), the process of the terminal device 30 is ended, and when there is no operation end instruction (No), the process returns to Step S150 and the process is continued.

The above is the description of the operation of the entire system. In step S160 in FIG. 14, the terminal device 30 transmits a recommendation request to the information selection device 10, but other methods may be used. For example, the terminal device 30 transmits a recommendation request to the item providing server 20,
The item providing server 20 may relay (transfer) the recommendation request to the information selection device 10. In addition, the control unit 110 of the information selection device 10 may transmit the data stored in the recommendation information storage unit 108 to the item providing server 20 via the transmission / reception unit 109 at a periodic or aperiodic timing. . Then, the control unit 205 of the item providing server 20 may receive the data via the transmission / reception unit 204 and store the data in the data storage unit 203 in advance.
And the control part 205 of the item provision server 20 reads recommendation data from the data storage part 203 as a process equivalent to step S170 for responding to a recommendation request,
Display recommendation data may be created and transmitted to the terminal device 30. in this case,
It is possible to reduce the processing load on the information selection device 10 associated with the creation and transmission of recommended display data.

Further, in step S210 of FIG. 14, the item providing server 20 relays the usage request from the terminal device 30 to the information selecting device 10, but other methods may be used.
For example, the usage request may be transmitted directly from the terminal device 30 to the information selection device 10 at the same time as the usage request transmission in step S200 or at an appropriate timing.

Further, in step S220 of FIG. 14, in addition to storing the usage history information, the information selection apparatus 10 displays the display recommendation data corresponding to the usage reference item identifier included in the usage request in the same manner as in step S170. The recommendation data for display may be created and transmitted to the item providing server 20. In step S230, the item providing server 20 may transmit the display recommendation data to the terminal device 30 in addition to performing the item providing process. That is, in this case, the terminal device 30 can receive recommendation information corresponding to the item identifier included in the usage request every time the usage request is transmitted.

In addition, in the item providing service that does not require special user registration processing because a terminal identifier such as a mobile phone terminal can be used, the use request transmitted in step S200 may include a terminal identifier as a use subject identifier. Good. Thereby, step S11.
It is also possible to omit the registered user confirmation process of 0 and the transmission of data necessary for the membership process in step S120.

<Operation of information selection device>
A specific example of the processing operation of the information selection apparatus 10 in the second embodiment will be described. First, a specific example of the recommendation information creation operation will be described with reference to the flowchart of FIG.

The control unit 110 of the information selection device 10 issues an instruction to each processing unit of the information selection device 10 at a predetermined timing, and starts a process of creating recommendation information. The timing of creating the recommendation information may be periodic or aperiodic. For example, at least one of the following three types can be used as the recommendation information creation timing.

The first timing for creating the recommendation information is a predetermined date and time or a predetermined time interval. For example, “every day at 6 am and 6 pm”, “every Monday at 10:30 am”, “every 12 hours”
For example, “every 24 hours”. At this time, the time interval fluctuates, such as “6 am on weekdays, 6 am and 6 pm on Saturdays and Sundays”, “every 3 hours on weekdays, every 6 hours on Saturdays, and every 12 hours on Sundays”. Also good. Also, the time interval may be changed according to the season, such as shortening the time interval in summer and increasing the time interval in winter. When this first timing is used, the processing load of the information selection device 10 can be reduced as compared with the case where other timings are used. In particular, if setting is made so that recommendation information is created in a time zone where the number of recommendation requests is small, it is effective in reducing the processing load of the information selection device 10.

The second timing for creating the recommendation information is the recommendation request transmission process of the terminal device 30 (FIG. 14).
Every time a recommendation request based on step S160) is received a predetermined number of times. In this case, the information selection device 10 may create recommendation information first, and then perform display recommendation data creation / transmission processing (step S170). By adjusting the predetermined number of times (that is, the number of receptions of the recommendation request until the recommendation information is updated), the information selection device 1
The balance between the size of the zero processing load and the new recommendation information can be adjusted. For example, if recommendation information is created every time a recommendation request is received at a predetermined number of times, the processing load of the information selection device 10 increases, but the latest recommendation information can be provided. In the following, for the sake of simplicity, when the second timing is used, it is assumed that recommendation information is created every time a recommendation request is received (a predetermined number of times is 1).

The third timing for creating the recommendation information is the use request transmission of the terminal device 30 (step 2).
00) every time a use request based on (00) is received. When the usage request is generated less frequently than the recommendation request, the use of the third timing is more suitable than the second timing. By adjusting the predetermined number of times, the information selection device 10
It is possible to adjust the balance between the size of the processing load and the newness of the recommendation information. If the predetermined number of times is set to one and recommendation information is created each time a use request is received, the processing load of the information selection device 10 increases, but the latest recommendation information can be provided. In the following, in order to simplify the explanation, when the third timing is used, the usage request is 1
It is assumed that the recommendation information is created every time it is received (predetermined number is 1).

In the following description, a set of reference item identifiers for which recommendation information is to be created is referred to as a “recommended reference set”. When the recommendation information is created at the first timing, the number of elements (reference item number) of the recommendation reference set is basically large. When creating recommendation information at the second and third timings, if the predetermined number of times described above is 1, the number of elements of the recommendation reference set is basically 1
However, there may be more than one.

First, in step S <b> 400, the relevance calculation unit 104 that receives an instruction from the control unit 110 calculates an item relevance level for each item combination, and stores the calculated item relevance level in the item relevance level storage unit 105.

In step S410, the degree-of-association calculation unit 104 that has received an instruction from the control unit 110 calculates the degree of association between categories, associates the two categories with the degree of association, and stores them in the category-relevance degree storage unit 103. .

In step S420, the information selection unit 107 that has received an instruction from the control unit 110 creates recommendation information. Specifically, the information selection unit 107 selects a recommended category and a recommended item for the reference item while referring to the category relevance storage unit 103 and the item relevance storage unit 105, and stores recommended information including these recommended information. The data is stored in the unit 108. And
The controller 110 is notified that the recommendation information creation operation has been completed.

<Item relevance calculation (step S400)>
Next, a specific example of the item relevance calculation process (step S400) will be described with reference to the flowchart of FIG.

In step S <b> 500, the relevance calculation unit 104 reads item usage histories by a plurality of users from the item usage history table 102 </ b> A of the usage history storage unit 102. Here, all item usage histories may be read, or usage histories that satisfy a predetermined condition may be read. For example, the association degree calculation unit 104 uses the usage history that satisfies the condition that “the item usage time is within a predetermined range” based on the usage history table 102A including the item usage time information as in the example of FIG. May be read out. The condition regarding the item use time can be specified as, for example, “the use time is within the past 4 months” or “the difference between the use time and the current time is 3 days or more and less than 30 days”.

The degree-of-association calculation unit 104 may read out usage histories within a predetermined number in order of new usage time for each item. For example, when the predetermined number is set to 20, the usage history of 20 items is read out in order from the newest usage time for items with a usage count of 20 or more, and for items with a usage count of less than 20 , Read all usage history for the item. In this way, it is possible to efficiently create a related item set even for items that are not frequently used and have not been used recently.

Then, the items (that is, items included in the usage history read out in step S500) (that is,
A set σ of item identifiers) is created. Below, the number of items in the set σ (ie
The number of item identifier types) is Ms, and the number of users (that is, the number of user identifier types) is U.
Let s.

In step S510, the relevance calculation unit 104 creates a recommended reference set (that is, a reference item set) K1. As described above, when the recommendation information is created at the second timing, the relevance calculation unit 104 may add the item identifier (request criterion identifier) included in the recommendation request to the recommendation criterion set K1.

When creating recommendation information at the third timing, the degree-of-association calculation unit 104 may enter the use reference item identifier included in the use request into the recommendation reference set K1.

When the recommendation information is created at the first timing, the item set σ created in step S500 may be set as the recommendation reference set K1. In this case, a related item set is created for each item identifier included in the usage history that satisfies the predetermined condition. The recommendation standard set K1 created here may be referred to by other processing units such as the information selection unit 107 and the control unit 110.

In step S520, the relevance calculation unit 104 selects one unprocessed item from the recommendation criterion set K1 created in step S510. The item to be processed is set as a reference item x.

In step S530, the relevance calculation unit 104 uses the usage history read in step S500, and the reference item x and the other item y (yεσ,
The degree of association with x ≠ y) is calculated.

Specifically, the number | I [x] ∩I [y] | of the users who have used the reference item x and the other items y together is calculated, and this is calculated as the item relevance W of the items x and y. [X] [
y] may be used. Further, the item relevance W [x] [y] may be calculated using a Jacquard coefficient shown in the equation (1). In the equation (1), I [x] indicates a set of users using the item x, I [y] indicates a set of users using the item y, and | I [x] ∩I [y] | Indicates the number of users who have used items x and y together, | I
[X] ∪I [y] | indicates the number of users who use at least one of the items x and y.

In addition, when the item usage history includes additional information such as information on the number of times the item has been used, or information on evaluation performed by the user on the item (evaluation value), the item relevance is obtained by using a cosine measure or a Pearson product moment correlation coefficient. May be used to calculate. For example, item relevance W
[X] [y] may be calculated using a cosine scale as shown in equation (2). Here, E [x] [u] indicates the use count or evaluation value of the item x by the user u, and E [y
] [U] indicates the number of use or evaluation value of the item y by the user u. Us is the total number of users (the number of types of user identifiers) included in the item usage history read in step S500.

Further, the relevance W [x] [y] may be calculated using a Pearson product moment correlation coefficient, as shown in the equation (3). Here, Ic [x] [y] is a set of users who used or evaluated the items x and y together. Ea [x] is an average value of the number of times the item x is used by users belonging to Ic [x] [y], or an average value of the evaluated evaluation values. Ea [
y] is an average value of the number of times the item y is used by users belonging to Ic [x] [y] or an average value of evaluation values.

Further, the degree of association W [x] [y] may be calculated using the Euclidean distance between E [x] [u] and E [y] [u] or other distances.

When item usage time information is stored in the usage history storage unit 102, E [x
] [U] or the like may be calculated by increasing the weight of the new usage history from the usage history with the old usage time. Specifically, first, the difference between the time (such as date and time) when the item relevance calculation process is executed and the item use time is calculated. This difference becomes a small value in the new item usage history, and this difference becomes a large value in the old usage history. Then, the output value when this difference is input to the monotonically decreasing function is used as a weighting coefficient, and a value obtained by multiplying E [x] [u] and E [y] [u] by the weighting coefficient is used to Or, item relevance W [x] [y according to equation (3)
] May be calculated.

Further, when item time information is stored in the item information table 101A (for example, 101A-1 or 101A-2), the item related information is used by using a weight that becomes larger as the item time information of the item y is newer. The degree W [x] [y] may be calculated. Specifically, first, the difference between the time (such as date and time) when the item relevance calculation process is executed and the item time information is calculated. If the production or registration of item y is new, this difference will be small,
This difference is large when it is old. Then, the output value when this difference is input to the monotonically decreasing function is used as a weighting factor, and a new item is obtained by multiplying the item relevance W [x] [y] calculated by any of the above methods by the weighting factor. The degree of association W [x] [y] may be used.

Further, E [x] [u] (the number of uses or evaluation value for the item x of the user u)
The item relevance may be calculated by applying multivariate analysis such as principal component analysis or quantification type 3 to a matrix having x = 1 to Ms, u = 1 to Us) as matrix elements. By applying multivariate analysis, it is possible to generate a vector that corresponds to each item constituting a matrix and has a reduced number of dimensions. Therefore, a cosine scale or Euclidean in the vector space of two vectors corresponding to any two items The item relevance W [x] [y] may be calculated using the distance or the like. In addition to the above, any method may be used as long as it is an index representing the relationship between two items.

In step S540, the relevance calculation unit 104 creates a related item set Ω [x] related to the reference item x based on the item relevance W [x] [y], and stores the related item set Ω [x] in the item relevance storage 105. The related item set Ω [x] is a set including item identifiers of items that are highly related to the reference item x.

The first method for creating the related item set is a method of putting all items whose degree of relevance with the reference item x in step S530 is calculated into the related item set Ω [x]. This method is suitable for generating recommendation information as accurately as possible.

The second method for creating the related item set is a method of selecting an item having a relatively high relevance W [x] [y] with the reference item x and putting it in the related item set Ω [x]. Specifically, other items (that is, items other than the item x) whose degree of association W [x] [y] with the reference item x is greater than or equal to a threshold are selected from the item set σ. Moreover, you may select another item in the range which does not exceed a predetermined value in order with large relevance W [x] [y] with the reference | standard item x. For example, if the number of items for which the degree of association W [x] [y] with the reference item x is less than a predetermined number, all items for which the degree of association W [x] [y] has been calculated are selected. And
Otherwise, a predetermined number of items may be selected in descending order of relevance W [x] [y]. According to the second method, the storage capacity required for the item relevance storage unit 105 can be reduced.

Then, the relevance calculation unit 104 associates the item identifier of the reference item x with each item identifier included in the related item set Ω [x] and the item relevance W [x] [y]. This is recorded in the item association degree table 105 </ b> A of the association degree storage unit 105. Specifically, the item identifier of the reference item x corresponds to the reference item identifier (reference item ID) of the item association degree table 105A shown in FIG. Further, each item identifier included in the related item set Ω [x] corresponds to a related item identifier (related item ID) in the related degree table 105A. When reference item identifiers that match any one of the identifiers included in the recommended reference set K1 are already stored in the item association degree table 105A, the association degree calculation unit 104 may update (overwrite) them. On the other hand, a reference item identifier that does not match any identifier included in the recommended reference set K1 is a relevance table 1
If stored in 05, the relevance calculation unit 104 does not change them.

In step S550, the relevance calculation unit 104 determines whether another reference item can be selected. The degree-of-association calculation unit 104 determines “Yes” when an unprocessed item exists in the recommendation reference set K1 created in step S510, and determines “No” when an unprocessed item does not exist. . The relevance calculation unit 104 returns to step S520 when it determines “Yes” and repeats the process, and ends the item relevance calculation process when it determines “No”.

<Category relevance calculation (step S410)>
Next, a specific example of the category relevance calculation process (step S410) will be described with reference to the flowchart of FIG.

In step S600, the degree-of-association calculation unit 104 first performs an item usage history reading process as in step S500. The relevance calculation unit 104 may use the item usage history read in step S500 as it is, or may read the usage history under different conditions. Then, the degree-of-relevance calculation unit 104 uses the item-category correspondence table 101C.
, The category identifier corresponding to the item identifier included in the read item usage history is specified, and a category set ρ which is a set of the specified category identifiers is created.

In step S610, the degree of association calculation unit 104 creates a reference category set K2 based on the correspondence between items and categories. Specifically, the relevance calculation unit 104 refers to the item-category correspondence table 101C to perform step S51 in FIG.
The category identifier corresponding to each item identifier included in the recommended reference set K1 created in 0 is specified, and the reference category set K2 is created by adjusting these category identifiers so that the same category identifier does not overlap. When a plurality of category identifiers correspond to one item identifier, all the corresponding category identifiers may be put in K2.

When creating recommendation information at the third timing, the relevance calculation unit 104 specifies a category identifier corresponding to the usage reference item identifier included in the usage request while referring to the item-category correspondence table 101C. Then, this may be included in the reference category set K2.

When the recommendation information is created at the first timing, the category set ρ created in step S600 may be set as the reference category set K2. The reference category set K2 created here may be referred to by other processing units such as the information selection unit 107 and the control unit 110.

In step S620, the relevance calculation unit 104 selects one unprocessed category from the reference category set K2. The category to be processed is set as a reference category p.

In step S630, the relevance calculation unit 104 uses the usage history read in step S600, and the reference category p and another category q (qερ,
The category relevance with p ≠ q) is calculated. For calculating the category relevance, step S530 is performed.
As in step S530, various calculation methods are used by substituting x with p, replacing y with q, and replacing the number of uses and the number of users for each item with the number of uses and the number of users for each category. be able to. Further, when the evaluation value of the item by the user is obtained, the category corresponding to the item is identified while referring to the item-category correspondence table 101C, and the average value of the evaluation values may be calculated and used for each category. Good.

In step S <b> 640, the relevance calculation unit 104 creates a related category set に 関 す る [p] related to the reference category p and stores it in the category relevance storage unit 103. Standard category p
Corresponds to the reference category identifier of the category relevance table 103A shown in FIG. 9, and each category identifier included in the related category set Π [p] corresponds to the related category identifier of the table 103A. In order to create the related category set Π [p], the relevance calculation unit 104
A similar process may be performed by replacing the item in step S540 with a category. Specifically, the relevance calculation unit 104 may create the related category set Π [p] using the first or second method described in step S540.

In step S650, the relevance calculation unit 104 determines whether another reference category can be selected. The degree-of-association calculation unit 104 determines “Yes” when an unprocessed category exists in the reference category set K2 created in step S610, and determines “No” when no unprocessed category exists. To do. When determining “Yes”, the relevance calculation unit 104 returns to step 620 to repeat the process, and when determining “No”, the relevance calculation unit ends the category relevance calculation process.

Note that in the item relevance calculation step described above, normalization processing may be performed so that the maximum value or total value of the item relevance becomes a predetermined value. For example, the normalization process may be performed so that the maximum value of the item relevance level is “1” and the minimum value is “0”. The same applies to the category relevance.

18 and 19 show a method of calculating the item relevance level and the category relevance level using the data stored in the usage history storage unit 102. However, the item relevance level and the category relevance level may be calculated by other methods. It may be calculated. For example, if the item is music content,
When “genre”, “mood”, “situation”, or the like is used as the category type, the information selection device 10 or another device can extract the feature amount extracted from the acoustic signal of the music,
The item relevance level and the category relevance level may be calculated based on the result of classifying the music pieces using such feature amounts. That is, the item relevance and category relevance can be calculated without using the usage history. For example, Japanese Patent Application Laid-Open No. 2007-322598 discloses a technique for extracting feature amounts from music signals of music and a technique for classifying music. Then, the difference (difference) or similarity between the feature amounts of the two pieces of music (items) may be calculated, and the item relevance may be calculated using them. Further, the difference between the classification results of the two music pieces (items) may be digitized to obtain the item relevance level. Further, correspondence information between items and categories (such as an item-category correspondence table 101C) may be created based on a result (category) classified from the viewpoint of mood or the like based on an acoustic signal of a music piece (item). Further, a representative value (an average value or the like) of the feature amount of the item corresponding to each category may be calculated, and the degree of association between categories may be calculated using a difference or similarity between these representative values. Moreover, an expert may create the item association degree and the category association degree in advance, and store them in the item association degree table 105A and the category association degree table 103A.

<Recommendation information creation (step S420)>
Next, a specific example of the recommendation information selection process included in the recommendation information creation (step S420) will be described below. FIG. 20 is a flowchart showing a first method of recommendation information selection processing.

In step S710, the information selection unit 107 selects one unprocessed item from the recommendation reference set K1 created in step S510. The item to be processed is set as a reference item x.

In step S720, the information selection unit 107 initializes the buffer B for calculating the category priority. The category priority is an index for determining a recommended category corresponding to the reference item x. Specifically, the information selection unit 107 refers to the category relevance table 103A and specifies the total number Nc of category types stored therein. Then, Nc buffers B [k] corresponding to the category k (category identifier k) are prepared, and their values are initialized to “0”.

In step S730, the information selection unit 107 selects the item-category correspondence table 1
The category identifier corresponding to the reference item x is acquired with reference to 01C. When there are a plurality of corresponding category identifiers, the information selection unit 107 may acquire all of them. The set of category identifiers thus obtained is set as a reference category set α, and the number of elements of the set α is set to Nα
And In the following, the category that is the i-th element of α is expressed as α [i].

In step S740, the information selection unit 107 refers to the item relevance table 105A, and selects an unprocessed related item y from the related items corresponding to the reference item x.
And the item relevance W [x] [y] is acquired.

In step S750, the information selection unit 107 selects the item-category correspondence table 1
A candidate category (candidate category identifier) that is a category corresponding to the related item y is acquired with reference to 01C. When there are a plurality of candidate category identifiers, the information selection unit 10
7 may acquire all of them. A set of the obtained candidate category identifiers is set as a candidate category set β, and the number of elements of the set β is set as Nβ.

In step S760, the information selection unit 107 initializes a control variable j for designating elements of the candidate category set β to “1”. Hereinafter, the category that is the j-th element of β is denoted as β [j].

In step S770, the information selection unit 107 updates the category priority B [β [j]] corresponding to the category β [j]. Specifically, any one of the following first to fourth methods may be used.

The first method for updating the category priority is a method using equation (4). Here, H [α [
i]] [β [j]] is the category relevance between the category that is the i-th element of the set α and the category that is the j-th element of the set β. The category relevance level is obtained by referring to the category relevance level table 103A. γ1 and γ2 are each a positive constant. When the number of elements Nα of the set α is 1 (Nα = 1), the parentheses in the formula (4) indicate the category relevance between the reference category and the candidate category. That is, the first method uses the value using the category relevance H [α [i]] [β [j]] between the reference category and the candidate category as a radix, and the first predetermined value γ1.
The category priority is calculated using a multiplication value of the power value with the exponent as the exponent and the power value with the item relevance W [x] [y] as the base and the second predetermined value γ2 as the exponent. I can say that. By changing the values of γ1 and γ2, the balance between the item relevance and the category relevance in the category priority calculation can be changed.

The second method for updating category priority is a method using equation (5). Where ω1, ω
Each of 2 is a positive constant. As is clear from equation (5), ω1 is a weighting factor for a value based on the category relevance H [α [i]] [β [j]] between the reference category and the candidate category. Further, ω2 is a weighting factor for the item relevance W [x] [y].
That is, the second method uses the weighted addition value of the value using the category relevance H [α [i]] [β [j]] and the item relevance W [x] [y] to It can be said that the priority is calculated. By changing the values of ω1 and ω2, the balance between the item relevance and the category relevance in the category priority calculation can be changed.

A third method for updating the category priority is a method using the equation (6). The difference from the first method is that the category relevance is converted into the extended relevance using the function F (X).
The output when the category relevance is input to the function F (X) is the extended relevance. This function F (
An example of the characteristic of X) is shown in FIGS. 22 (a) and 22 (b). In the example shown in FIG. 22B, the input X
In the interval from 0 to X1, the output Y increases with respect to the input, but when the input X is X1, the output Y
Becomes the maximum value Y1, the output Y decreases in the section where the input X is larger than X1, and the output value becomes Y2 when the input X is the maximum value X2. That is, X1 <X2 and Y1> Y2. By using such a function of characteristics, when the category relevance is very large, the expansion relevance can be lowered.

In some cases, it may be effective to recommend the same category as the reference category or a category with a very high degree of relevance. However, depending on the nature of the item or service, the user may not be surprised by the recommendation information. There may be a problem that usage is not promoted. In contrast, by calculating the category priority using the extended relevance based on the conversion function F (x) as shown in FIG. 22B, it becomes difficult to recommend the same obvious category as the reference category. Can solve such problems.

Note that Y = 0 may be set when X = 0, but as shown in FIGS. 22A and 22B, the output value Y0 when X = 0 is a relatively small positive value. It may be. In this way, a category that is very low in relevance with the reference category can be easily included in the recommendation information to some extent. Of course, the characteristics of the function F (X) shown in FIGS. 22A and 22B are merely examples, and functions having various characteristics can be used according to the properties of the items and services.
Further, if a monotonically increasing linear function passing through the origin is used as the conversion function F (x), the same result as in the first method can be obtained.

A fourth method for updating the category priority is a method using the equation (7). The difference from the second method is that the category relevance is converted into the extended relevance using the function F (X) described in the third method. Even when the fourth method is used, the same effect as that described in the third method can be obtained.

In Expressions (4) to (7), since Nα addition processing is performed in parentheses, the category priority can be appropriately calculated regardless of the number of elements in the set α. . Nα
A value obtained by dividing the value by Nα after the addition process of times may be used. In that case, it corresponds to using the average value of the category relevance or the extended relevance between each element of the reference category set α and β [j]. Moreover, you may use a median instead of such an average value. If the range of the category relevance value and the range of the item relevance value are significantly different, adjust the range of the category relevance level or the item relevance level or both using a logarithmic function. After that, the equations (4) to (7) may be applied.

In step S780, the information selection unit 107 determines whether or not the value of the control variable j is smaller than Nβ. If smaller, the process proceeds to step S790; otherwise, the process proceeds to step S80.
Go to 0.

In step S790, the information selection unit 107 increases the value of the control variable j by “1” and updates it. Then, the process returns to step S770.

In step S800, the information selection unit 107 determines whether other unprocessed related items can be selected with reference to the item relevance table 105A. If it can be selected, the process returns to step S740; otherwise, the process proceeds to step S810.

In step S810, the information selection unit 107 determines a recommended category for the reference item based on the value of the category priority (category priority buffer B). Specifically, the information selection unit 107 may select a number of categories that do not exceed a predetermined number in the order of higher category priority and set it as a recommended category. For example, when the number of categories for which the category priority is calculated is equal to or less than a predetermined number, the information selection unit 107 may set all the categories for which the category priority is calculated as recommended categories. On the other hand, if the number is larger than the predetermined number, the information selection unit 107 may select the predetermined number in descending order of the category priority and set these as recommended categories. Further, the information selection unit 107 may select a category having a category priority level equal to or higher than a predetermined value and set these as recommended categories. Further, the information selection unit 107 may select a number of categories that do not exceed a predetermined number in descending order of category priority from categories having a category priority of a predetermined value or higher.

Furthermore, the information selection unit 107 assigns category rankings to the recommended categories in descending order of category priority. Then, the information selection unit 107 stores the reference item identifier, the category order, and the recommended category identifier in association with each other in the recommended information table 108A. In one process of step S810, the recommended category identifier corresponding to one reference item and the category ranking thereof are stored in the recommended information table 108A.

In step S820, the information selection unit 107 determines a recommended item for each recommended category determined in step S810. Specifically, the information selection unit 107 may combine the two tables by associating the related item identifier of the item relevance degree table 105A with the item identifier of the item-category correspondence table 101C. Then, the information selection unit 107 selects a row in which the “reference item identifier” column (item) matches the reference item x in the combined table, and has a high degree of item relevance for each category identifier of the recommended category. A number of items that do not exceed a predetermined number may be selected in order and used as recommended items. For example, if the number of items corresponding to the recommended category is less than or equal to a predetermined number,
All these items may be recommended items. On the other hand, if there are more items than the predetermined number, the predetermined number may be selected in descending order of the degree of item relevance and set as recommended items. Note that this predetermined number (that is, the upper limit number of recommended categories) may be set according to the category priority. For example, in a category having a high category priority, this predetermined number may be increased so that many items are selected as recommended items. On the other hand, in a category with a low category priority, this predetermined number may be reduced and a small number of items may be selected as recommended items.

Further, the information selection unit 107 may select items having an item relevance level equal to or higher than a predetermined value for each category identifier of the recommended category, and may use them as recommended items. In this case, the predetermined value may be set according to the category priority. For example, in a category with a high category priority, this predetermined value may be reduced and many items may be selected as recommended items. On the other hand, in a category with low category priority, this predetermined number may be increased and a small number of items may be selected as recommended items.

Further, the information selection unit 107 may select, for each category identifier of the recommended category, a number of items that do not exceed a predetermined number in descending order of the item relevance from items having an item relevance of a predetermined value or higher. Good.

Then, the information selection unit 107 assigns item rankings to the combinations of the recommended category and the recommended item in descending order of the item relevance. Then, the information selection unit 107 stores the reference item identifier, the item order, and the recommended item identifier in association with each other in the recommended information table 108A. In one process of step S820, the recommended item identifier corresponding to one reference item and the item ranking thereof are stored in the recommended information table 108A.

A specific example of the process in step S820 will be described with reference to FIG. FIG. 23 schematically shows a state (internal table) in which the two tables are combined by associating the related item identifier in the item relevance table 105A with the item identifier in the item-category correspondence table 101C. For example, as can be seen from the fact that there are a total of three rows corresponding to the related item “ItemID-52”, when a plurality of categories correspond to one related item, there are as many rows in the internal table as the number of corresponding categories. Created. In the example of this figure, the item is music content, and “genre” and “mood” are used as category types. In step S810, “rock (genre)”, “jazz (genre)”, and “astringent” It is assumed that three categories of (mood) are determined as recommended categories. In addition, the reference item x is “ItemID-10”, and a selection condition is used in which two items are selected in descending order of item relevance for each recommended category.

Items corresponding to the recommended category “lock” are “ItemID-50” and “Item”.
“ID-52” and “ItemID-54”, which are recommended candidate items. The item relevance levels are “0.90”, “0.80”, and “0.60”, respectively. For this reason, “ItemID-50” is selected as the item ranking first, and “ItemID-52” is selected as the item ranking second.

Since there is only one item corresponding to the recommended category “Jazz” of “ItemID-58”, this is selected as the item ranking first place.

The categories corresponding to the recommended category “Astringent” are “ItemID-52”, “ItemI”.
D-55 ”and“ ItemID-25 ”, and the item relevance is“ 0 ”.
. 80 "," 0.72 ", and" 0.40 ". For this reason, “ItemID-52”
Is selected as the item ranking first, and “ItemID-55” is selected as the item ranking second.

In this example, “ItemID-52” is selected redundantly in the two categories of “lock” and “astringent”. In this way, the same item may be selected redundantly among a plurality of categories. Or you may select an item so that it may not overlap between several categories. For example, when selecting an item corresponding to “Astringent”, “ItemID-5
Check that “2” is already selected for “Lock”, exclude this,
“ID-55” may be selected as the first item ranking, and “Item ID-25” may be selected as the second item ranking. Also, if a recommendation candidate item corresponds to multiple recommendation categories,
Depending on the category priority or the category relevance, a recommended category to be finally associated may be determined. For example, in the example shown in FIG. 23, the category priority of “lock” is “0.
When the category priority of “9” and “Sweet” is “0.7”, “ItemID-52” is associated with “Lock” having a relatively high category priority, and “ For example, it may be possible not to deal with “astringent”. In addition, when the category priorities of the two categories are approximately the same, a category with a low category relevance may be prioritized to be a recommended category. Such processing can provide unexpected recommendation information to the user.

In step S830, the information selection unit 107 determines whether other unprocessed reference items can be selected with reference to the item association degree table 105A. If it can be selected, the process returns to step S710, and if not, the recommendation information selection process ends.

Next, the second method of the recommended information selection process in step S420 will be described with reference to the flowchart of FIG.

In step S910, the information selection unit 107 selects one unprocessed item from the recommendation reference set K1 created in step S510. The item to be processed is set as a reference item x.

In step S920, the information selection unit 107 acquires a reference category set α that is a set of category identifiers corresponding to the reference item x, by the same processing as in step S730. The number of elements in the set α is Nα.

In step S930, the information selection unit 107 collects related items corresponding to the reference item x for each category corresponding to the related item, and creates a temporary table that is sorted in descending order of item relevance. When a temporary table corresponding to another reference item already exists, the information selection unit 107 deletes it and then creates a new temporary table. Specifically, as in the description of step S820, the item relevance table 105
The two tables may be joined by associating the related item identifier of A with the item identifier of the item-category correspondence table 101C. Then, the information selection unit 107 sets the “reference item identifier” column (item) to the reference item x in the combined table.
The rows that match are selected as targets, and the rows of the table are sorted in descending order of the degree of item relevance with the reference item for each category.

An example of this temporary table is shown in FIG. As shown in this figure, in the temporary table, related items are sorted in the descending order of item relevance by category such as “Rock”, “Jazz”, “Blues”, “Astringent”, etc. Has been granted. In addition,
Since the item rank is known at the storage position of the related item identifier, the item rank column may be omitted. Further, when providing an item ranking column, it is not always necessary to sort the rows of the table. In the example shown in this figure, “ItemID-101” is registered in a total of three categories of “Lock”, “Blues”, and “Sweet”, and “ItemID-110” is registered.
"Is registered in two categories," Rock "and" Blues ". As can be seen from this, items corresponding to a plurality of categories are registered in a plurality of table rows for each category. In FIG. 24, “Rock”, “Jazz”, “Blues”, and “Astringent”.
“C1”, “C2”, “C3”, and “C4” items are registered respectively. When creating a temporary table, not all the related items registered in the item relevance table 105A are entered in the temporary table, but registered so that the number of related items in each category is the same. An upper limit may be set for the number of registered related items. For example, for one category, the maximum number of related items may be set to 20. In addition, only related items whose item relevance is equal to or greater than a predetermined value may be registered in the temporary table.

In step S940, the information selection unit 107 calculates a category priority using the temporary table created in step S930. In the following, the category priority of category k registered in the temporary table is represented by P [k].

The first method for calculating category priority is a method using equation (8). Here, H [α [
i]] [k] is the category relevance between the category that is the i-th element of the set α and the category k. The category relevance level is obtained by referring to the category relevance level table 103A. γ3 and γ4 are positive constants, respectively. The score Q [k] is a numerical value of the category k related to the related item registered in the temporary table, and reflects the degree of item relevance with the reference item x. In other words, the score Q [k] is a value using the degree of item relevance with the reference item x. A specific example of a method for calculating the score Q [k] will be described later. Therefore, the first method uses a power value having a value using the category relevance H [α [i]] [β [k]] as a base and a third predetermined value γ3 as an index, and an item relevance. It can be said that the category priority is calculated using a value obtained by multiplying the value (Q [k]) by the power and the fourth predetermined value γ4 as an exponent.

The second method for calculating category priority is a method using equation (9). Where ω3, ω
Each of 4 is a positive constant. The second method is a category relevance H [α [i]] [β [k
]] And a value obtained by adding a weighted value (Q [k]) using the item relevance level, it can be said that the category priority is calculated.

A third method for calculating category priority is a method using equation (10). The difference from the first method is that the extended relevance using the function F (X) is used as in the description of step S770. When the category k is the same as the reference category set α or when the relevance is very high, the category priority can be lowered to prevent the obvious category from being recommended.

A fourth method for calculating category priority is a method using equation (11). The difference from the second method is that the extended relevance degree using the function F (X) is used as in the description of step S770.

Here, a method of calculating the score Q [k] will be described. The first method for calculating the score Q [k] is a method using the number of items whose item relevance with the reference item x is a predetermined value or more among related items corresponding to the category k. For example, in the temporary table, the number of related items whose item relevance is “0.5” or more may be counted for each category.
In addition, when an item whose degree of item relevance with the reference item x is greater than or equal to a threshold is entered into the related item set using the second method of related item set creation in step S540, the number of items registered in the temporary table “C1”, “C2”, “C3],“ C4], and the like can be used as the score Q [k] of each category.

The second method for calculating the score Q [k] is the sum of the item relevance when a predetermined number (NQ) of related items corresponding to the category k are selected in descending order of the item relevance with the reference item x, or This is a method using a representative value of item relevance. As the representative value, an average value, a median value, a mode value, or the like can be used. In the example shown in FIG. 24, NQ = 3,
When the average value is used, the score Q [k] corresponding to the category “lock” is “(0.95+
0.92 + 0.86) ÷ 3 = 0.91 ”.

Regardless of which of the first and second methods described above is used, the score Q [k] can be said to be a value calculated using the item relevance with the reference item x of the related item corresponding to the category k. In each method of calculating the score Q [k], normalization processing such as setting the maximum value to “1” may be performed. The score Q [k] may be calculated by combining the first and second methods described above. For example, when the value obtained by the first method is Q1 [k] and the value obtained by the second method is Q2 [k], the product Qa [k] of Q1 [k] and Q2 [k]
Or a weighted addition value Qb [k] of Q1 [k] and Q2 [k] is calculated, and the expressions (8) to (8)
11) The category priority may be calculated by substituting Qa [k] or Qb [k] into Q [k] in the equation.

In step S950, the information selection unit 107 uses the same method as in step S810.
The recommended category is determined, and the recommended category and the category ranking are stored in the recommended information table 108A. Similar processing may be performed using the category priority P instead of the category priority buffer B in step S810.

In step S960, the information selection unit 107 determines a recommended item for the reference item x. Specifically, using the temporary table created in step S930, for each recommended category determined in step S950, select a number of items that does not exceed a predetermined number in descending order of item relevance with the reference item x. It can be used as a recommended item. In addition,
The predetermined number used here is the predetermined number NQ used in the second method of calculating the score Q in step S940.
May be the same. In addition, an item having an item relevance with the reference item x of a predetermined value or more may be selected as a recommended item. Furthermore, you may select the item of the number which does not exceed a predetermined number in order with the high item relevance degree with the reference | standard item x from the item whose item relevance degree with the reference | standard item x is more than predetermined value.

In step S970, the information selection unit 107 determines whether other unprocessed reference items can be selected while referring to the item association degree table 105A. If it can be selected, the process returns to step S910. If not, the recommendation information selection process ends. The above is the description regarding the specific example of the recommendation information selection process included in the recommendation information creation process (step S420).

As already described, the technique disclosed in Patent Document 1 uses a program (in a recording reservation history of a TV program by a user) to determine a recommended program category (that is, equivalent to a recommended item of the present embodiment). That is, the user preference level for each program category calculated using the reference item of this embodiment is used. However, in the technique disclosed in Patent Document 1, when determining a recommended program category, television programs belonging to each program category (that is,
Information corresponding to the item of the present embodiment) is not fully considered. For this reason, Patent Document 1
In the technology disclosed in the above, there is not always a recommended program associated with the recommended program category (that is, equivalent to the recommended item of the present embodiment). Therefore, the technique disclosed in Patent Document 1 is a program (that is, a program included in a recording reservation history of a TV program by a user (ie,
Even if the recommended program category (ie, equivalent to the recommended category of this embodiment) can be presented based on the recommended item of this embodiment), the recommended program (ie, equivalent to the recommended item of this embodiment) can be sufficiently displayed. There is a possibility that it cannot be presented.

The main purpose of information recommendation is to increase the use of items by the user by presenting the user with information regarding items that the user is interested in. By presenting information on recommended items divided into recommended categories, it is expected that the ease of understanding the recommended information is improved and the degree of interest of the user is increased. However, if recommendation categories that do not contain items with high relevance with the reference item are mixed in the recommendation information, the degree of user satisfaction and interest in the recommendation information will be reduced, which is rather counterproductive. . In addition, a display space or a display page increases in order to display a recommendation category that is not useful to the user, and an operation for browsing recommended information increases. Especially for devices such as mobile phone terminals and smartphones that have limited operability, such as a small display screen, limited information that can be displayed at one time, and a small operation unit such as buttons, etc., it is recommended compactly with as few display pages and display areas as possible Since it is necessary to present information, such a problem is likely to become apparent.

On the other hand, in the first method for selecting recommended information according to the present embodiment, the category priority is calculated (updated) using the item relevance in step S770, and based on the category priority in step S810. The recommended category is determined. In the second method of selecting recommended information, the category priority is calculated using the item relevance in step S940, and the recommended category is determined based on the category priority in step S950.

That is, the category priority used in the present embodiment reflects the magnitude of the item relevance between each of the items belonging to each candidate category and the reference item, as in the first embodiment. For this reason, this embodiment can reduce possibility that the item which has high relationship with a reference | standard item does not exist in a recommendation category. As a result, it is possible to prevent an unnecessary increase in the space and page necessary for displaying the recommendation information and the operation necessary for browsing the recommendation information. For this reason, this embodiment can create recommended information that is easy to use and understandable for the user, and can present it to the user.

Furthermore, in this embodiment, the example which calculates a category priority using both a category relevance degree and an item relevance degree like (4) Formula-(11) was shown. As a result, it is possible to determine a recommendation category that has a strong relationship, similarity, or correlation with the reference item and that is highly likely to be sufficiently associated with an item that has a high relationship with the reference item. it can.

<Fourth Embodiment>
In the present embodiment, a modified example of the recommendation list displayed in step S180 of the third embodiment will be described. FIG. 25 shows a modification of the recommendation list display according to the present embodiment.
The difference from the example shown in FIG. 16 is that an “adjustment of expansion relevance” button is displayed at the upper right of the screen. The user adjusts the characteristic of the conversion function F (x) for calculating the extended relevance by pressing the “adjustment of extended relevance” button, and then creates the new function characteristic by pressing the “related item” button. Recommended information can be obtained. Note that the “adjustment of extended relevance” button may be displayed on another screen such as the new item introduction screen shown in FIG. The user
When the “adjustment of expanded relevance” button is not pressed, the expanded relevance is calculated using the standard function characteristic or the function characteristic set immediately before.

In response to the user pressing the “adjustment of extended relevance” button, the terminal device 30 performs FIG.
An expansion relevance adjustment screen as shown in (a) or (b) may be displayed. The adjustment screen is generated by the terminal device 30 according to the display control information transmitted from the information selection device 10 or the item providing server 20. For example, the display control information creation unit 106 of the information selection device 10 receives a screen for adjusting the characteristics of the conversion function F (x) (for example, FIG. 26 (a) or (b)).
The display control information for outputting “” may be generated and transmitted to the terminal device 30.

FIG. 26A is an example of a setting screen for the user to select one of predetermined options and set the characteristics of the function F (x). Function F corresponding to each option in the example of FIG.
An example of (x) is shown in FIG. Options 1) to 5) in FIG. 26 (a) correspond to 1) in FIG.
This corresponds to the characteristics (5) to (5). Further, as shown in FIG. 26B, the user may directly specify the characteristics of the function F (x). In the example shown in the figure, the user inputs the input device 330.
Using a mouse (for example, a mouse, a trackball, or a touch panel), a function curve can be directly input, or a function curve prepared in advance can be corrected. Note that FIG.
The examples shown in a) and (b) and FIG. 27 are only examples, and the user may adjust the characteristics of the function F (X) using other methods. For example, as in the example shown in FIG. 26A, options relating to a category having a low degree of association or options relating to a category having an intermediate degree of association may be prepared and selected by the user. In addition, based on the characteristics of the conversion function used in the recommendation list presented to the user, relative changes to the characteristics (for example, “more highly relevant categories”, “less relevant categories are given higher priority”). Etc.) may be designated by the user to set a new conversion function characteristic.

In the present embodiment, after the user adjusts the characteristics of the function F (X), the recommendation information can be created using the function F (X), so that the user can easily obtain the recommendation information that suits his / her preference. it can. Also, by changing the function characteristics as appropriate, it is possible to obtain a variety of recommended information.

<Fifth Embodiment>
In the present embodiment, another modification of the recommendation list displayed in step S180 of the third embodiment will be described. 28A to 28C show modified examples of the recommendation list display according to the present embodiment. The display screen shown in FIGS. 28A to 28C includes a plurality of graphic objects corresponding to a plurality of recommended categories. Furthermore, the arrangement of each graphic object in the display screen is determined according to the category priority level of the recommended category represented by each graphic object.

More specifically, in FIGS. 28A to 28C, the recommended category is displayed as a circular object. And the coordinate position of the vertical direction of the screen of each circular object is determined based on the category priority of the recommendation category which each circular object represents. For example, FIG.
In (a), the higher the category priority, the higher the position in the screen. That is,
Since “category A”, “category C”, and “category B” are arranged in this order from the top of the screen,
“Category A” has the highest category priority, and “Category B” has the lowest category priority. In this way, the user can intuitively grasp the category priority of the recommended category by using the graphic object associated with the recommended category and determining the display position according to the category priority.

The numbers shown in the circular objects in FIGS. 28A to 28C are the number of recommended items corresponding to the recommended category. In FIG. 28B, the size of the circular object is set according to the number of recommended items. Since the number of recommended items in “Category C” is “20”, the diameter of the circle is the largest. By displaying in this way, the user can intuitively grasp the number of recommended items corresponding to the recommended category.

FIG. 28C shows an example in which the display color and the display pattern (pattern) are changed for each category. For example, when the category type is “mood”, a low-saturation color such as pastel color may be used for a category such as “healing”, and a primary color system is used for a category such as “violent”. Use highly saturated colors. Colors, patterns, icons, and the like that match each category type such as “genre”, “mood”, and “situation” may be set using knowledge such as color psychology.

The display methods shown in FIGS. 28A to 28C are merely examples, and other display methods may be used. For example, an object shape other than a circle may be used, the object shape may be changed for each category, and a combination of the object shape, size, color, etc. may be changed for each category. In the two-dimensional graphs shown in FIGS. 28A to 28C, the horizontal coordinate value may be determined based on some index. For example, a category that is frequently used by the user may be displayed on the left side of the screen, a category with more new item time information may be displayed on the left side, or a representative value or total item price for each category. The coordinate value in the horizontal direction may be determined based on the value or the like. The user may be able to specify display characteristics such as the display position, shape, size, and color of the graphic object associated with the recommended category according to his / her preference. Specifically, the terminal device 30
The control section 301 causes the user to input a desired display characteristic using the input device 330, and transmits the data corresponding to the category (category ID, category name) and the display characteristic via the transmission / reception section 302. Send to. The control unit 110 of the information selection device 10 includes a transmission / reception unit 109.
The received data is stored in the internal memory of the display control information creating unit 106 in association with the use subject identifier. The display control information creation unit 106 may create display control information for the use subject identifier stored in the internal memory according to the display characteristics corresponding thereto.

When the user selects a desired category from among the categories shown in FIGS. 28A to 28C using the input device 330, recommended items corresponding to the selected category are displayed as shown in FIG. Also good. In this figure, only the name (title) of the recommended item is displayed, but of course, appropriate item attribute information may be displayed together. The user can use the item by selecting the desired item.

The information selection device 10 may operate as follows in order to display the display screen shown in FIGS. 28 and 29 on the display device 320 of the terminal device 30. The display control information creation unit 106 of the information selection device 10 creates display control information that enables the display screen shown in FIGS. 28 and 29 to be displayed on the display device 320 in step S170 of FIG. It transmits to the terminal device 30. In order to efficiently realize the user's interactive operation in the display control information,
A program described in a script language or the like that can be executed by the browser unit 303 of the terminal device 30 may be included. The display control information creation unit 106 associates a graphic object with each of the plurality of recommended categories, and based on information on each recommended category and information on recommended items corresponding to each recommended category, the position, size, and shape of the graphic object Display control information is created by determining display characteristics such as colors.

Note that the display control information creation unit 106 does not necessarily have to create display control information. For example, in step S170 of FIG. 14, the display control information creation unit 106 obtains information (such as category priority) related to each recommended category, which is the original information of the display control information, and information on recommended items corresponding to each recommended category. You may transmit to the terminal device 30 instead of display control information. And the terminal device 30 is based on these information, the display position of a graphic object,
Display characteristics such as shape, size, and color may be determined. Further, if necessary, after transmitting data such as item time information and item price information from the information selection device 10 to the terminal device 30, the terminal device 30 may determine display characteristics using the information. That is, it is arbitrary how the information selection device 10 and the terminal device 30 share the processing related to the display of the recommendation list.

In the above description, an example is shown in which the arrangement of graphic objects corresponding to the recommended category is determined according to the category priority level. However, other display forms (for example, color, size, or shape) of the graphic object corresponding to the recommended category may be determined according to the category priority. Further, it may be possible to display the recommended category as text. In this case, the color, font size, or font type of the text indicating the recommended category may be determined according to the category priority. That is, the display object corresponding to the recommended category (
For example, graphic or text, or a combination thereof)
Color, size, shape, font, or font type) may be determined according to category priority. Also in these modified examples, the user can intuitively grasp the category priority of the recommended category.

<Other embodiments>
In the third to fifth embodiments described above, the information selection apparatus 10 performs step S420 (FIG. 17).
The recommendation information creation process is performed on the terminal device 30 side. In this case, the application unit 304 of the terminal device 30 may perform an operation corresponding to the information selection unit 107. For example, at an appropriate timing prior to step S160 (FIG. 14), the application unit 304 is connected to the item information table 101A and the category information table 101 via the item providing server 20 or directly from the information selection device 10.
B, data of the item-category correspondence table 101C, the category association degree table 103A, and the item association degree table 105A are acquired. At this time, all data in each table may be acquired, or only data necessary for creating recommendation information for one request reference identifier in step S420 may be acquired. In step S160, the application unit 304 may execute a process corresponding to step S420 instead of transmitting a recommendation request. In this case, the application unit 30
4 is responsible for the function of the information selection unit 107.

The technical idea of converting the category relevance to the extended relevance using the conversion function F (x) described in the third to fifth embodiments is an item for calculating the category priority for determining the recommended category. It can be applied even when the relevance is not used. In other words, the technical idea regarding the degree of expansion relevance can be implemented independently of the “technical idea using the item relevance level for calculating category priority” described in the first to fourth embodiments. The technical idea related to the expanded relevance can be widely applied when the category relevance is used for determining the recommended category.
In particular, as shown in FIG. 22B, by using a conversion function F (x) having a section in which the output value decreases as the input value increases, the same obvious category as the reference category is recommended. It becomes difficult to be done. For this reason, it is possible to provide a recommended category that is surprising to the user.

Furthermore, the above-described embodiment is merely an example relating to application of the technical idea obtained by the present inventors. That is, the technical idea is not limited to the above-described embodiment,
Of course, various modifications are possible.

For example, the technical idea obtained by the present inventor includes the following embodiments A1 to A33.
(Embodiment A1)
Based on multiple category priorities calculated for each of multiple candidate categories,
A category selection unit for selecting a recommended category recommended for the user from the plurality of candidate categories;
Each of the plurality of category priorities is calculated using an item relevance indicating the strength of relevance between an item belonging to each candidate category and a reference item.
Information selection device.
(Embodiment A2)
Each of the plurality of category priorities is calculated using the item relevance and a heterogeneous relevance indicating the strength of relevance between the reference item and each candidate category.
The information selection device according to embodiment A1.
(Embodiment A3)
The heterogeneous relevance is calculated using a category relevance indicating the strength of relevance between a reference category corresponding to the reference item and each candidate category.
The information selection device according to embodiment A2.
(Embodiment A4)
Each of the plurality of category priorities is calculated to be higher as the dissimilarity degree is higher and the item relation degree is higher.
The information selection device according to Embodiment A2 or Embodiment A3.
(Embodiment A5)
Each of the plurality of category priorities is
(A) a power value in which the value using the heterogeneous relevance is a radix and a first predetermined value is an exponent; and a power value in which the value using the item relevance is a radix and a second predetermined value is an exponent; Multiplication value of
(B) a weighted addition value between a value using the category relevance and a value using the item relevance;
Calculated using either
The information selection device according to any one of Embodiment A2 to Embodiment A4.
(Embodiment A6)
Each of the plurality of category priorities is calculated as a function of the extended relevance obtained by inputting the heterogeneous relevance into a predetermined conversion rule.
The information selection device according to any one of Embodiment A2 to Embodiment A4.
(Embodiment A7)
The conversion rule is a conversion function having a section in which the output value decreases as the input value increases.
The information selection device according to embodiment A6.
(Embodiment A8)
Each of the plurality of category priorities is
(A) a power value in which the value using the expanded relevance is a radix and a third predetermined value is an exponent; and a power value in which the value using the item relevance is a radix and a fourth predetermined value is an index; Multiplication value of
(B) a weighted addition value between the value using the expanded relevance and the value using the item relevance;
Calculated using either
The information selection device according to Embodiment A6 or Embodiment A7.
(Embodiment A9)
The category selection unit includes:
For each of the plurality of candidate categories, an item group whose item relevance is a fifth predetermined value or more, or a number of item groups not exceeding the sixth predetermined value in descending order of the item relevance, is an upper item set. Select as
Calculating a total value or a representative value of a plurality of item relevance levels of the plurality of items included in the upper item set and the reference item;
The category priority is calculated using the total value or the representative value.
The information selection device according to any one of Embodiment A1 to Embodiment A8.
(Embodiment A10)
The category selection unit calculates the category priority for each of the plurality of candidate categories using the number of items whose item relevance is equal to or greater than a seventh predetermined value.
The information selection device according to any one of Embodiment A1 to Embodiment A9.
(Embodiment A11)
A control information creating unit for creating display control information for outputting a display screen showing information on the recommended category;
The category selection unit selects a plurality of categories as recommended categories,
The display screen includes a plurality of display objects corresponding to the plurality of recommended categories,
The display state of each display object in the display screen is determined according to the category priority level of the recommended category represented by each display object.
The information selection device according to any one of Embodiment A1 to Embodiment A10.
(Embodiment A12)
A control information creating unit that creates display control information for outputting a display screen that accepts adjustment of characteristics of the conversion rule;
The information selection device according to any one of Embodiment A6 to Embodiment A8.
(Embodiment A13)
An item selection unit that selects a recommended item from items belonging to the recommended category based on the item relevance level;
The information selection device according to any one of Embodiment A1 to Embodiment A12.
(Embodiment A14)
The item selection unit sets a selection condition for the recommended item according to the category priority.
The information selection device according to embodiment A13.
(Embodiment A15)
An operation for outputting the display screen to a display unit based on the display control information received from the information selection device according to Embodiment A12 via a network, and information for adjusting the characteristics of the conversion rule is the information. A terminal device comprising a processing unit that operates to transmit to a selection device.
(Embodiment A16)
A receiving unit for receiving a category priority indicating a recommendation category and a priority of the recommendation category;
A control information creating unit for creating display control information for outputting a display screen showing information on the recommended category;
The recommendation category is plural,
The display screen includes a plurality of display objects corresponding to the plurality of recommended categories,
The display state of each display object in the display screen is determined according to the category priority level of the recommended category represented by each display object.
Terminal device.
(Embodiment A17)
For each of a plurality of candidate categories, calculate the category priority using the item relevance indicating the strength of relevance between the items belonging to each candidate category and the reference item, and calculate the plurality of category priorities. Selecting a recommended category recommended to the user from among the plurality of candidate categories,
An information selection method comprising:
(Embodiment A18)
The calculating includes calculating the plurality of category priorities using a heterogeneous degree of association indicating a strength of association between the reference item and each candidate category.
The information selection method described in Embodiment A17.
(Embodiment A19)
The heterogeneous relevance is calculated using a category relevance indicating the strength of relevance between a reference category corresponding to the reference item and each candidate category.
The information selection method described in Embodiment A18.
(Embodiment A20)
A computer program for causing a computer to perform the information selection method according to any one of Embodiment A17 to Embodiment A19.
(Embodiment A21)
The category selection unit sets characteristics of the conversion rule based on information designated from outside.
The information selection device according to any one of Embodiment A6 to Embodiment A8.
(Embodiment A22)
Embodiment A wherein the reference item is an item included in the history for the user
1-The information selection apparatus of any one of Embodiment A14.
(Embodiment A23)
The item relevance is determined based on usage histories of a plurality of users including the user regarding a plurality of items including the reference item.
The information selection device according to any one of Embodiment A1 to Embodiment A14, Embodiment A21, and Embodiment A22.
(Embodiment A24)
The item relevance is derived by weight calculation using a weight determined so that the newer the use time by the plurality of users is,
The information selection device according to embodiment A23.
(Embodiment A25)
The item relevance is derived by weight calculation using a weight determined so that the production time or registration time of each item becomes larger as it is newer.
The information selection device according to any one of Embodiment A1 to Embodiment A14 and Embodiment A22 to Embodiment A24.
(Embodiment A26)
The display size of each display object in the display screen is determined according to the number of recommended items belonging to the recommendation category represented by each display object.
The information selection device according to embodiment A11.
(Embodiment A27)
A control information creating unit for creating display control information for outputting a display screen showing information on the recommended category;
The recommendation category is plural,
The display screen based on the display control information includes a plurality of display objects corresponding to each of the plurality of recommended categories,
The display size of each display object in the display screen is determined according to the number of recommended items belonging to the recommendation category represented by each display object.
The information selection device according to any one of Embodiment A1 to Embodiment A10.
(Embodiment A28)
The information selection apparatus according to Embodiment A2, wherein the dissimilarity degree is calculated based on correspondence information between items and keywords and correspondence information between keywords and categories.
(Embodiment A29)
The information selection apparatus according to Embodiment A2, wherein the dissimilarity degree is calculated based on correspondence information between a user and an item used by the user and information indicating a degree of interest in the user's category.
(Embodiment A30)
Based on multiple category priorities calculated for each of multiple candidate categories,
A category selection unit for selecting a recommended category recommended for the user from the plurality of candidate categories;
Each of the plurality of category priorities is calculated using an item relevance indicating the strength of relevance between the reference item and another item, and correspondence information between the other item and each candidate category.
Information selection device.
(Embodiment A31)
Each of the plurality of category priorities is calculated using the item relevance and a heterogeneous relevance indicating the strength of relevance between the reference item and each candidate category.
The information selection device according to embodiment A30.
(Embodiment A32)
The information selection apparatus according to Embodiment A30 or A31, wherein the correspondence information is a second heterogeneous relevance degree that indicates the strength of relevance between the other item and each candidate category.
(Embodiment A33)
Each of the plurality of category priorities is
Calculated using the product of the item relevance and the second heterogeneity relevance;
The information selection device according to embodiment A32.

1 System 10 Information Selection Device 20 Item Providing Server 30 Terminal Device 40 Network 42 Network 101 Metadata Storage Unit 101A Item Information Table 101B Category Information Table 101C Item-Category Correspondence Table 102 Usage History Storage Unit 102A Item Usage History Table 103 Category Relevance Storage unit 103A Category association degree table 104 Association degree calculation unit 105 Association set storage unit 105A Association degree table 106 Display control information creation unit 107 Information selection unit 108 Recommendation information storage unit 108A Recommendation information table 109 Transmission / reception unit 110 Control unit 120 Display device 130 Input device 201 User management unit 202 Item storage unit 203 Data storage unit 204 Transmission / reception unit 205 Control unit 301 Control unit 302 Transmission / reception unit 303 Browser unit 304 Application unit 320 Display device 330 Input device

Claims (15)

Based on a plurality of priorities calculated for each of a plurality of candidate information, the information selecting unit for selecting recommended information recommended for a user from among the plurality of candidate information,
Each of the plurality of priorities is calculated as an extended relevance obtained by inputting a relevance indicating the strength of relevance between the reference information and each candidate information into a conversion rule having nonlinear characteristics,
According to the conversion rule, the output increases as the input increases from the lower limit value to the predetermined value, the output decreases as the input increases from the predetermined value to the upper limit value, and the input is the predetermined value. An information selection device with the characteristic that the output is maximum in some cases. The predetermined value is a value larger than a value that bisects a section from a lower limit value to an upper limit value of the input,
The information selection device according to claim 1. The conversion rule has a characteristic that the second output value corresponding to the upper limit value of the input is larger than the first output value corresponding to the lower limit value of the input.
The information selection device according to claim 1 or 2. A first control information creating unit that creates first display control information for outputting a display screen that accepts adjustment of the characteristics of the conversion rule;
The information selection device according to any one of claims 1 to 3. The first control information creating unit creates a plurality of options related to the characteristics of the conversion rule, or first display control information for designating the shape of the characteristics of the conversion rule.
The information selection device according to claim 4. The first control information creating unit creates first display control information for designating a relative change with the conversion rule used to select the recommendation information;
The information selection device according to claim 4. The first control information creation unit creates first display control information for causing the input value of the conversion rule to specify a characteristic in a section that is equal to or greater than the predetermined value.
The information selection device according to any one of claims 4 to 6. A second control information creating unit for creating second display control information for outputting a display screen related to the recommendation information;
The information selection unit selects a plurality of recommended information,
The display screen includes a plurality of display objects corresponding to each of the plurality of recommendation information,
The display state of each display object in the display screen is determined according to the priority level of recommendation information represented by each display object.
The information selection device according to any one of claims 1 to 7. The second control information creation unit determines at least one of display positions, colors, sizes, and shapes of the plurality of display objects according to the priority level.
The information selection device according to claim 8.   An operation is performed to output the display screen to a display unit based on the first display control information received from the information selection device according to any one of claims 4 to 7 via a network, and the conversion A terminal device comprising a processing unit that operates to transmit information for adjusting a characteristic of a rule to the information selection device. A receiving unit that receives the recommendation information and the priority of the recommendation information;
A second control information creating unit for creating second display control information for outputting a display screen related to the recommendation information;
The recommendation information is plural,
The priority of the degree of relation indicating relevance strength of the information serving as a selection criterion and each candidate information, which is calculated as an expansion relevance obtained by inputting to the conversion rule with the non-linear characteristics,
According to the conversion rule, the output increases as the input increases from the lower limit value to the predetermined value, the output decreases as the input increases from the predetermined value to the upper limit value, and the input is the predetermined value. In some cases, the output has the maximum value,
The display screen includes a plurality of display objects corresponding to each of the plurality of recommendation information,
The display state of each display object in the display screen is determined according to the high priority of the recommendation information represented by each display object.
Terminal device. An information selection method executed by the information selection device,
For each of a plurality of candidate information, a plurality of priorities are set as extended relevance obtained by inputting the relevance indicating the strength of relevance between the reference information and each candidate information into a conversion rule having nonlinear characteristics. Calculating steps,
Selecting recommendation information recommended to the user from among the plurality of candidate information based on the plurality of calculated priorities;
Including
According to the conversion rule, the output increases as the input increases from the lower limit value to the predetermined value, the output decreases as the input increases from the predetermined value to the upper limit value, and the input is the predetermined value. In some cases, the output has the maximum value,
Information selection method. A control method executed by a terminal device,
A receiving step of receiving a plurality of pieces of recommendation information and the priority of each of the plurality of pieces of recommendation information;
Creating a display control information for outputting a display screen including a plurality of display objects corresponding to each of the plurality of recommended information;
Including
The priority received in the receiving step is obtained by inputting, for each of a plurality of candidate information, a relevance level indicating the strength of relevance between the reference information and each candidate information into a conversion rule having nonlinear characteristics. Calculated as the degree of expansion relevance
According to the conversion rule, the output increases as the input increases from the lower limit value to the predetermined value, the output decreases as the input increases from the predetermined value to the upper limit value, and the input is the predetermined value. In some cases, the output has the maximum value,
In the creating step, the display state of each display object in the display screen is determined according to the priority level of the recommendation information represented by each display object.
Control method. On one or more computers,
For each of a plurality of candidate information, a plurality of priorities are set as extended relevance obtained by inputting the relevance indicating the strength of relevance between the reference information and each candidate information into a conversion rule having nonlinear characteristics. Calculating steps,
Selecting recommendation information recommended to the user from among the plurality of candidate information based on the plurality of calculated priorities;
Is a program for executing
According to the conversion rule, the output increases as the input increases from the lower limit value to the predetermined value, the output decreases as the input increases from the predetermined value to the upper limit value, and the input is the predetermined value. In some cases, the output has the maximum value,
program. On the computer,
A receiving step of receiving a plurality of pieces of recommendation information and the priority of each of the plurality of pieces of recommendation information;
Creating a display control information for outputting a display screen including a plurality of display objects corresponding to each of the plurality of recommended information;
Is a program for executing
The priority received in the receiving step is obtained by inputting, for each of a plurality of candidate information, a relevance level indicating the strength of relevance between the reference information and each candidate information into a conversion rule having nonlinear characteristics. Calculated as the degree of expansion relevance
According to the conversion rule, the output increases as the input increases from the lower limit value to the predetermined value, the output decreases as the input increases from the predetermined value to the upper limit value, and the input is the predetermined value. In some cases, the output has the maximum value,
In the creating step, the display state of each display object in the display screen is determined according to the priority level of the recommendation information represented by each display object.
program.

Images