JP2017037580A - Apparatus, method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an apparatus for calculating a degree of matching between a specific item and a customer, a method, and a program.SOLUTION: An apparatus includes: an individual attribute acquisition unit 120 which acquires individual attribute data including a plurality of attribute values on a plurality of attributes of an individual; an item attribute acquisition unit 130 which acquires item attribute data indicating multiple pieces of attribute data on a plurality of attributes of items, for each of the items; an instruction acquisition unit 110 which acquires a user selection instruction for selecting some of the items among the plurality of items; and an output processing unit 150 which outputs the items selected by the selection instruction from among the plurality of items, with priority, on the basis of a result of comparing the individual attribute data with the item attribute data.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an apparatus, a method, and a program.

A method has been proposed in which customer attribute data stored in a database is converted into a waveform, and the degree of approximation of a plurality of waveforms is calculated and used for market research (see Patent Document 1).
[Patent Document 1] Japanese Patent Application Laid-Open No. 2014-219785

  However, there is no known method for optimizing and outputting the result of the user selecting an item (for example, a product) based on the user attribute and the item attribute. In addition, according to the above method, the degree of matching between a specific item and a customer cannot be calculated with high accuracy.

  In the first aspect of the present invention, a personal attribute acquisition unit that acquires personal attribute data including a plurality of attribute values possessed by an individual for a plurality of attributes, and a plurality of attributes corresponding to each of a plurality of items An item attribute acquisition unit that acquires item attribute data indicating a plurality of attribute data of an item, an instruction acquisition unit that acquires a selection instruction of a user who selects some items from the plurality of items, and a plurality of items There is provided an apparatus including an output processing unit that prioritizes and outputs each item selected by a selection instruction based on a comparison result obtained by comparing personal attribute data and item attribute data.

  The summary of the invention does not enumerate all the features of the present invention. In addition, a sub-combination of these feature groups can also be an invention.

1 shows a system 10 according to the present embodiment. An example of the personal attribute data in this embodiment is shown. An example of the item attribute data in this embodiment is shown. The 1st processing flow of the apparatus 100 which concerns on this embodiment is shown. An example of personal attribute data and item attribute data is shown. Another example of personal attribute data and item attribute data is shown. The 2nd processing flow of the apparatus 100 which concerns on this embodiment is shown. An example of a selection instruction from the user and an output of the apparatus 100 is shown. An example of prioritizing items by the item output unit 156 in FIG. 8 is shown. The 3rd processing flow of the apparatus 100 which concerns on this embodiment is shown. An example of the hierarchical structure of a selection item is shown. An example of the display screen in a 1st hierarchy is shown. An example of the display screen after inputting a selection instruction is shown. The distribution of attributes relating to item 1 and item 2 is shown. An example of the attribute conformity of item 1 and item 2 is shown. 2 shows an exemplary configuration of a computer 1900 according to the present embodiment.

  Hereinafter, the present invention will be described through embodiments of the invention, but the following embodiments do not limit the invention according to the claims. In addition, not all the combinations of features described in the embodiments are essential for the solving means of the invention.

  FIG. 1 shows a system 10 according to this embodiment. The system 10 selects an item according to a user's selection instruction from a plurality of items, and prioritizes and outputs the selected item based on the result of comparing the user and the item attributes. The system 10 includes a user terminal 20, a search server 30, a store terminal 40, a history management server 46, a personal attribute database 52, an item attribute database 54, and an apparatus 100.

  One or a plurality of user terminals 20 are connected to the apparatus 100 via the network 22, and a selection instruction for selecting an item from a user is input by a button, a keyboard, a touch panel, and / or voice input, and the selection instruction is input to the apparatus. To 100. Here, the item may be information that can be selected by the user via the user terminal 20, and may be, for example, a product, a service, a person, a work, and / or an item on a website. The user terminal 20 receives the processing result corresponding to the selection instruction from the device 100 and displays it on the display screen. The user terminal 20 may be a personal computer, a notebook computer, a mobile phone, a smartphone, or other terminal device capable of data input / output.

  The network 22 includes various networks such as the Internet, a wide area network (WAN), a local area network, or a combination thereof. The network 22 may include a connection by at least one of a wired network and a wireless network at each connection point. For example, the network 22 may be realized by the Internet.

  The search server 30 is connected to the apparatus 100 via the network 22, receives a search key from the apparatus 100, extracts one or a plurality of items that hit the search key from the search database 32, and extracts the extracted result as the apparatus 100. Send to. The search server 30 may be realized by a generally available search engine or the like.

  One or a plurality of store terminals 40 are connected to the apparatus 100 via the network 42, and are POS terminals provided at various stores or computers having equivalent functions. The store terminal 40 transmits purchase data of the target person to the history management server 46 when the target person purchases a product or service (hereinafter may be collectively referred to as “product or the like”).

  The network 42 includes various networks such as the Internet, a wide area network (WAN), a local area network, or a combination thereof. The network 42 may include a connection by at least one of a wired network and a wireless network at each connection point. For example, the network 42 may be realized by a dedicated line separated from the network 22. The network 22 and the network 42 may be the same network (for example, the Internet).

  The history management server 46 is a computer such as a server computer, and is connected to the store terminal 40 and the device 100 via the network 42 and manages one or a plurality of store terminals 40 provided in each store. The history management server 46 may be a distributed system including a plurality of computers. The history management server 46 performs purchase processing (sales processing) based on purchase data received from the store terminal 40 and records user purchase data in the purchase history database 44. Further, the history management server 46 provides user purchase data to the device 100 in response to a request from the device 100.

  The apparatus 100 is, for example, a computer such as a server computer, and receives a selection instruction from the user terminal 20 and outputs an item corresponding to the selection instruction based on a comparison result comparing the item and the attribute of the user. The apparatus 100 includes an instruction acquisition unit 110, a personal attribute acquisition unit 120, an item attribute acquisition unit 130, and an output processing unit 150 that are connected to each other via a bus. The apparatus 100 may be a distributed system that processes each of these configurations or functions, or a part of each configuration or function by a plurality of computers.

  The instruction acquisition unit 110 acquires, from the user terminal 20, a user's selection instruction for selecting some items from a plurality of items. For example, the instruction acquisition unit 110 acquires a search key (for example, a search keyword) for searching for some items from a plurality of items as a selection instruction. In addition, for example, the instruction acquisition unit 110 may input, as a selection instruction, a selection item selected by the user from two or more selection items included in the screen presented to the user (for example, a text with a hyperlink) Clicks, etc.). The instruction acquisition unit 110 may separately acquire personal identification information (for example, a login ID) for identifying the user in order to specify the user who has input the selection instruction.

  The personal attribute acquisition unit 120 acquires personal attribute data including a plurality of attribute values possessed by an individual for a plurality of attributes. For example, the personal attribute acquisition unit 120 acquires personal identification information of a user who has input a selection instruction such as a search key from the instruction acquisition unit 110, and converts the personal attribute data including the personal identification information into personal attribute data of a plurality of individuals. Is obtained by searching from the personal attribute database 52 in which is stored. Thereby, the personal attribute acquisition unit 120 acquires the personal attribute data of the user who has input the selection instruction. The personal attribute acquisition unit 120 may generate personal attribute data of a plurality of users from the purchase histories of the plurality of users stored in the purchase history database 44 and store the personal attribute data in the personal attribute database 52. Details of the personal attribute data will be described later.

  The item attribute acquisition unit 130 acquires item attribute data indicating a plurality of attribute data of each item for a plurality of attributes, corresponding to each of the plurality of items. For example, the item attribute acquisition unit 130 acquires item attribute data of an item that matches a selection instruction such as a search key acquired by the instruction acquisition unit 110 from the item attribute database 54 that stores item attribute data of a plurality of items. Further, the item attribute acquisition unit 130 may generate item attribute data for each item based on a plurality of personal attribute data for a plurality of individuals who prefer each item, and store the generated item attribute data in the item attribute database 54. Details of the item attribute data will be described later.

  The output processing unit 150 prioritizes and outputs each item selected by a selection instruction among a plurality of items based on the comparison result obtained by comparing the personal attribute data and the item attribute data. Here, “each item selected by the selection instruction” may be an item directly selected by the selection instruction. For example, the output processing unit 150 prioritizes and outputs each item that hits the search key among a plurality of items based on the comparison result. The “each item selected by the selection instruction” may be another item derived from the item selected by the selection instruction. For example, the output processing unit 150 may prioritize and output each item included in the next screen to be presented to the user according to the item selected by the selection instruction based on the comparison result. The output processing unit 150 includes an item suitability calculation unit 152, an attribute suitability calculation unit 154, and an item output unit 156.

  The item suitability calculation unit 152 calculates an item suitability indicating the suitability between the selection instruction and the item selected by the selection instruction. For example, the item suitability calculation unit 152 requests the search server 30 to search for an item that matches at least a part of the search key, receives a search result including one or more items from the search server 30, and receives each received item. The degree of fitness for the search key is calculated.

  The attribute suitability calculation unit 154 calculates the attribute suitability based on a comparison result obtained by comparing the personal attribute data and the item attribute data of the item. For example, the attribute suitability calculation unit 154 compares the personal attribute data of the user who has input the search key with the item attribute data of each item included in the search result received from the search server 30 by the item suitability calculation unit 152. Calculate the attribute fitness.

  The item output unit 156 selects, prioritizes and outputs some items based on the item suitability and attribute suitability from among a plurality of items. For example, the item output unit 156 ranks a plurality of items selected by the selection instruction based on the item suitability and the attribute suitability, and the items having the highest item suitability and attribute suitability are displayed first. As described above, the output result is supplied to the user terminal 20 that has input the selection instruction.

  As described above, the apparatus 100 according to the present embodiment receives a selection instruction such as a search key from the user, selects an item according to the selection instruction, and prioritizes an item selected based on the user's personal attribute data and item attribute data. The result is output. As a result, the apparatus 100 can provide the user with a result that takes into account the degree of matching between the user and the item attribute in addition to the degree of matching between the selection instruction and the selection result. Thereby, the apparatus 100 can provide a more appropriate search result or the like according to the attribute of the user who has instructed the selection.

  Note that a plurality of elements of the system 10 may be combined to form one element. For example, the personal attribute database 52 and the item attribute database 54 may be realized by a single database. The device 100 may include elements other than the device 100 (for example, the personal attribute database 52), or the elements in the device 100 (for example, the personal attribute acquisition unit 120) may be implemented by an external server device or the like. Good.

FIG. 2 shows an example of personal attribute data in the present embodiment. As shown in the figure, personal attribute data includes a plurality of attribute values possessed by each individual for a plurality of attributes, and is represented as a waveform on a graph. FIG. 2 shows the personal attribute data of the person 1 represented by a solid line and the personal attribute data of the person 2 represented by a broken line. For example, the attribute x ₁ (marriage) of the individual 1 is 100%, and the attribute x ₂ (child) is 59%. This may mean that the statistical probability that the individual 1 is married is 100% (ie, surely married) and the probability that the individual 1 has children is estimated to be 60%. Further, for example, the attribute x ₃ (driving) of the individual 1 is 48%, and the attribute x ₄ (smoking) is 20%. This is about 48 on a scale of 0-100 for the driving of the individual 1 (for example, driving frequency of about twice a month), and about 20 on a scale of 0-100 for the smoking preference of the individual 1 ( For example, it means smoking amount of about one box per month).

  The apparatus 100 generates the personal attribute data of the individual 1 that forms the waveform as shown in FIG. 2 by specifying the attribute values of the N attributes of the individual 1. In this way, the apparatus 100 generates personal attribute data for each of a plurality of individuals. In addition to / instead of generating the personal attribute data for each individual, the apparatus 100 also represents personal attribute data representing a plurality of individuals belonging to the same group or similar groups (for example, the personal attribute data of all family members belonging to a specific household) Alternatively, personal attribute data corresponding to all members of a specific group may be generated.

FIG. 3 shows an example of item attribute data in the present embodiment. The item attribute data may be generated based on personal attribute data of a plurality of individuals who prefer each item, and may include a distribution of attribute values of each attribute of the individual who selected each item 1. For example, FIG. 3 shows item attribute data generated based on personal attribute data of a plurality of individuals who have purchased item 1 which is one product. Distribution ratio of individual attribute values of 90% or more attributes x ₁ among a plurality of individuals who purchase items 1 as shown is 10%, the distribution ratio of the individual attribute values less than 80% 90% 5%, the distribution ratio of individuals whose attribute value is 70% or more and less than 80% is 0%,... The distribution ratio of individuals whose attribute value is 0% or more and less than 10% is 5%. The average of the attribute values of the attribute x ₁ of a plurality of individuals who purchase an item 1 is 41%. Thus, item attribute data is represented as distribution data of each attribute value of an individual who likes the item. The item attribute data may be represented not by the distribution ratio (%) of individuals who prefer items but by the total number of individuals who prefer items.

  FIG. 4 shows a first processing flow of the apparatus 100 according to the present embodiment. The apparatus 100 generates personal attribute data and item attribute data by executing the processes of S110 to S150.

  First, the personal attribute acquisition unit 120 acquires basic attribute data for a plurality of individuals (S110). For example, the personal attribute acquisition unit 120 provides basic attribute information (for example, age, gender, address, residence area, occupation, etc.) provided to each store by a plurality of individuals from the store terminal 40 at the time of membership registration. Married and unmarried, and / or commuting school area, etc.) are acquired as basic attribute data in association with the individual identification information of each individual. The personal attribute acquisition unit 120 may acquire basic attribute data directly from the user via the user terminal 20 in addition to / in place of the store terminal 40.

  Next, the personal attribute acquisition unit 120 acquires purchase history data for a plurality of individuals (S120). For example, the personal attribute acquisition unit 120 purchases a plurality of individuals linked with personal identification information from the history management server 46 (purchased product / service name, purchased product / service category, purchased product / service characteristics, Purchase price / price range, purchase date / time, purchase place / store, and / or payment means, etc.). The personal attribute acquisition unit 120 may acquire the behavior history of each individual on a communication network such as the Internet in addition to / in place of the purchase history. For example, the personal attribute acquisition unit 120 may acquire the name of the website accessed by each individual, the category of the website, the estimated staying time, the migratory behavior style, and the like as the action history.

  Next, the personal attribute acquisition unit 120 acquires research data about a plurality of individuals (S130). For example, the personal attribute acquisition unit 120 obtains questionnaire response results obtained from a plurality of individuals (for example, whether married or unmarried, presence of children, residence status, income, presence or absence of a driver's license, credit status, hobby preference, Behavioral style, luxury orientation, price orientation, traditional orientation, and / or innovation orientation, etc.) may be associated with personal identification information and acquired as research data. The research data may be data in which at least a part of information overlaps with the basic attribute data.

Next, the personal attribute acquisition unit 120 generates personal attribute data (S140). The personal attribute acquisition unit 120 assigns attribute values of each attribute to a plurality of individuals based on at least one of the acquired basic attribute data, purchase history data, behavior history data, and research data. For example, the personal attribute acquiring unit 120, to indicate that the basic attribute data is married, may allocate 100% of the attribute value of the attribute x ₁ regarding Marriage. Further, for example, if 5 out of upscale of value 0 included in the research system data, may allocate 50% of the attribute value of the attribute x _N indicating the upscale properties.

  The personal attribute acquisition unit 120 may infer attribute values of at least some attributes included in the personal attribute data. For example, if some of the items such as a registration form for collecting basic attribute data or a questionnaire for collecting research data are missing and attribute values of attributes corresponding to the items cannot be obtained, personal attributes The acquisition unit 120 may predict the attribute value of the attribute from the attribute values of other attributes. For example, even if information on the presence or absence of a user's driver's license in the research data is not obtained, the personal attribute acquisition unit 120 has a high probability if the purchase history data includes car purchase information ( For example, 95%) can estimate that the user has a driver's license.

  The personal attribute acquisition unit 120 may generate a probability inference algorithm from learning data from which attribute value data of all attributes is obtained, and predict a missing attribute value based on the probability inference algorithm. The personal attribute acquisition unit 120 may use various methods such as a Bayesian network, a neural network, and a random forest as a probability inference algorithm.

  Here, the personal attribute acquisition unit 120 stores the personal attribute data generated for each individual in the personal attribute database 52 in association with the personal identification information.

  Next, the item attribute acquisition part 130 produces | generates item attribute data based on the some personal attribute data about the some individual who likes each item (S150). For example, for the plurality of items, the item attribute acquisition unit 130 purchases, sells, uses, rents, bookmarks, or personal identification information of an individual who has transmitted / posted a message about the item, etc. The personal identification information of the individual who purchased each item is acquired from the history management server 46. Thereafter, the item attribute acquisition unit 130 acquires personal attribute data of a plurality of individuals who purchased each item from the personal attribute database 52, aggregates attribute values of each attribute, and creates a distribution of attribute values of each attribute. The item attribute acquisition unit 130 generates the attribute value distribution itself of each attribute and / or a numerical value representing the distribution as item attribute data.

  For example, the item attribute acquisition unit 130 generates attribute data of each attribute in the item attribute data of each item based on the result of averaging the plurality of individual attribute data for each of the plurality of attributes. Thereby, the item attribute acquisition part 130 produces | generates the item attribute data which contain the average value of the attribute value of the person who likes the said item as an attribute value of each attribute, as shown in the average line of FIG. Further, in addition to the average, the item attribute acquisition unit 130 uses numerical values (distribution, deviation, kurtosis, skewness, type of distribution, KLD, JSD, etc.) related to the distribution of each attribute of the item attribute data as item attribute data. It may be calculated as at least a part of

  In addition, for example, the item attribute acquisition unit 130 determines each attribute in the item attribute data of each item based on the distribution of each of the plurality of attributes in the plurality of individual attribute data for the plurality of individuals who prefer each item. Generate attribute data. Thereby, the item attribute acquisition unit 130 can generate item attribute data representing the characteristics of each attribute instead of one attribute value such as an average value, and the attribute value distribution itself. The attribute state can be reflected in the item attribute data.

  As described above, the apparatus 100 generates personal attribute data and item attribute data through the processes of S110 to S150. Here, the apparatus 100 may execute all or a part of the processing of S110 to S150 in advance before the instruction acquisition unit 110 acquires a selection instruction from the user, or the instruction acquisition unit 110 issues a selection instruction from the user. You may perform all or one part of the process of S110-S150 whenever it acquires.

  Note that the apparatus 100 may omit any of the processes of S110 to S130 as necessary. For example, the apparatus 100 may omit the process of S <b> 130 when research data is not necessary for generating personal attribute data. Moreover, if there exists a process required for the production | generation of personal attribute data and item attribute data, it can perform suitably. For example, the apparatus 100 can generate personal attribute data and item attribute data using the technique described in Patent Document 1.

  FIG. 5 shows an example of personal attribute data and item attribute data. FIG. 5 shows an example in which the apparatus 100 generates the waveform of the personal attribute data of the individual 1 and the waveform of the item attribute data of the item 1 and the item 2 by the first processing flow. In this figure, the waveform of the item attribute data of item 1 and item 2 is formed from the average of the attribute values of each attribute of the personal attribute data of the individual who likes each item. The distance between waveforms and the similarity in shape represent the affinity between individuals and items, or between multiple items. For example, FIG. 5 shows that the waveform of item 1 is closer to the waveform of individual 1 than the waveform of item 2, and that individual 1 is more likely to like item 1 than item 2.

  FIG. 6 shows another example of personal attribute data and item attribute data. FIG. 6 shows a case where the apparatus 100 generates the waveform of the personal attribute data of the individual 1 and the distribution of the item attribute data of the item 1 by the first processing flow. The personal attribute data of the individual 1 indicated by the dotted line and the item attribute data of the item 1 indicated by the solid line are the same as those shown in FIG. The distribution of item attribute data indicated by halftone dots corresponds to item 1 in FIGS. The darkness of the halftone dots corresponds to the distribution ratio of the item attribute data.

The distribution ratio on each attribute of the item attribute data corresponding to the waveform of the personal attribute data represents the degree of approximation between the individual and the item. The apparatus 100 can more strictly evaluate the affinity with an individual by representing the item attribute data as a distribution. For example, as assessed by the mean degree of approximation of the attribute value of the personal attribute data of the attribute value and individual 1 item attribute data for the item 1 in the attribute x ₃ without consideration of the distribution, indicated by a dotted line graph and the solid line graph in FIG. 6 As such, both are evaluated as being relatively approximate.

However, the distribution of the item attribute data in the attribute x ₃ is sparse compared with the surrounding area at a value (around 50%) in the vicinity of the graph of the personal attribute data, and actually the individual 1 and the item 1 the attribute value of the attribute x ₃ is likely you do not have too much approximation. For example, the individual 1 is an individual who performs driving at a moderate frequency (for example, twice a month), whereas the group of individuals who prefer the item 1 is driven by a high frequency of individuals (for example, every day) and the driving frequency. Since they are composed of individuals with low (for example, once in February), it is highly possible that they are different in nature. Since the apparatus 100 of this embodiment handles item attribute data as a distribution, it is possible to estimate the attribute suitability between an individual and an item and the attribute suitability between items with higher accuracy.

  FIG. 7 shows a second processing flow of the apparatus 100 according to the present embodiment. By executing the processing of S210 to S280, the apparatus 100 performs output with prioritized items so that the item attribute data matches the user's personal attribute data in accordance with the selection instruction input by the user. Here, a case where the user inputs a search key as a selection instruction will be described.

  First, the personal attribute acquisition unit 120 acquires personal attribute data for operating the user terminal 20 (S210). For example, the instruction acquisition unit 110 supplies a login screen to the user terminal 20 via the network 22, and personal identification information (for example, login ID, member ID, name, or email address) is given to the user on the login screen. The personal identification information of the user who operates the user terminal 20 is acquired. The personal attribute acquisition unit 120 receives the user's personal identification information from the instruction acquisition unit 110 and acquires the personal attribute data associated with the personal identification information from the personal attribute database 52 to operate the user terminal 20. Get the personal attribute data of the user. The personal attribute acquisition unit 120 supplies the acquired personal attribute data to the attribute suitability calculation unit 154 of the output processing unit 150.

  Next, the instruction acquisition unit 110 acquires a search key as a selection instruction from the user (S220). The search key is used to select only some items related to the search key from the countless items. For example, the instruction acquisition unit 110 supplies a search screen to the user terminal 20 and causes the user terminal 20 to acquire a keyword such as a general name or abbreviation of a product / service related to an item that the user wants to select. The instruction acquisition unit 110 supplies the acquired search key to the item suitability calculation unit 152 of the output processing unit 150.

  Next, the item suitability calculation unit 152 executes a search using the search key, and acquires an item that hits the search key (S230). For example, the item suitability calculation unit 152 requests the search server 30 to execute search using the search key. In response to this, the search server 30 executes a search by using an item that matches a part or all of the search key, a synonym, a synonym, or a related word of a part or all of the search key, and is the same as the search key or Data related to items that are more than a predetermined degree are extracted from the search database 32. For example, the search server 30 may extract a web page that includes a keyword corresponding to the search key in the title and / or content. As an example, when the search key is a specific product, the search server extracts a plurality of web pages related to the product as information related to the item. The search server 30 supplies one or more items hit by the search to the item attribute acquisition unit 130 and the item suitability calculation unit 152.

  Next, the item attribute acquisition unit 130 acquires item attribute data of each item hit in the search of S230 (S240). For example, the item attribute acquisition unit 130 searches each item (for example, a product / service name included in the web page) hit in the search from the item attribute database 54, and acquires item attribute data corresponding to each item. Thereby, the item attribute acquisition part 130 acquires the item attribute data of each item produced | generated based on the personal attribute data of the some individual who likes each item in S150. The item attribute acquisition unit 130 supplies the acquired item attribute data of each item to the attribute suitability calculation unit 154.

  Next, the item suitability calculation unit 152 calculates an item suitability that is the suitability of each item with respect to the search key (S250). For example, the item suitability calculation unit 152 matches the character string of the search key with the name of each web page hit with the search key, the number of occurrences of the same or related character string as the search key in the meta tag, and the character string match The item suitability is calculated based on the ratio, the arrangement position, and / or the usage mode. As an example, the item suitability calculation unit 152 has a higher degree of matching between a search key and a character string in a web page corresponding to each item, a higher use frequency, and / or a more forward use. You may calculate the item suitability of each item highly. The item suitability calculation unit 152 may calculate the item suitability by various keyword matching methods that are already used. Here, the item suitability calculation unit 152 calculates the item suitability of each item on a scale of 0 to 1. Instead of the calculation by the item suitability calculation unit 152, the search server 30 may calculate the item suitability when searching for an item, and supply the calculated item suitability to the item suitability calculation unit 152. The item suitability calculation unit 152 supplies the item suitability of each item to the item output unit 156.

  Based on the comparison result obtained by comparing the personal attribute data of the user acquired in S210 and the item attribute data of the item acquired in S240, the attribute suitability calculation unit 154 determines whether the user who entered the search key and each item hit in S230 Is calculated (S260). The attribute suitability calculation unit 154 may calculate the attribute suitability based on the degree of approximation of the waveform of the user's personal attribute data and the waveform of the item attribute data of each item (also referred to as “both waveforms”). Here, it is assumed that the attribute suitability calculation unit 154 calculates the attribute suitability on a scale of 0 to 1.

  First, (1) an attribute fitness calculation method in the case where an average value of attribute values of a plurality of individuals is used as an attribute value of each attribute of item attribute data will be described.

  The attribute suitability calculation unit 154 may calculate the attribute suitability according to the distance between both waveforms. For example, the attribute suitability calculation unit 154 determines the difference between the attribute value of each attribute of the personal attribute data and the attribute value (average value) of the corresponding attribute of the item attribute data within a predetermined threshold. Calculate the attribute fitness. As an example, when the difference between the attribute values of 50 attributes out of N = 100 attributes is within the threshold of 10%, 0.50 is calculated as the attribute suitability.

Further, the attribute suitability calculation unit 154 may calculate the attribute suitability according to the closeness of the line segment angles of both waveforms. For example, the attribute suitability calculation unit 154 is configured on the graph by the line segment angles of two adjacent attributes in the personal attribute data (for example, the attribute value of the attribute x _{1 and} the attribute value of the attribute x ₂ of the personal attribute data). that the angle of a line segment), the segment angle of the corresponding attribute in the item attribute data (e.g., a segment) constructed on the graph by the attribute value of the attribute x ₁ and the attribute value of the attribute x ₂ item attribute data The attribute suitability may be calculated based on a ratio in which the difference is within a predetermined threshold. Specifically, when 99 adjacent sections are formed from N = 100 attributes, and the line segment angle difference in 50 sections is within the threshold of 10 degrees, about 0.51 is set as the attribute. Calculated as fitness.

  The attribute suitability calculation unit 154 may calculate the attribute suitability based on both the distance between both waveforms and the proximity of the line segment angles of both waveforms. For example, the attribute suitability calculation unit 154 may calculate an average or a weighted average of the distance between both waveforms and the closeness of the line segment angles of both waveforms as the attribute suitability. As described above, in (1), the attribute suitability calculation unit 154 represents the item attribute data as an averaged waveform, and calculates the attribute suitability based on the degree of approximation between the waveform of the item attribute data and the position and shape of the personal attribute data. To do. The attribute suitability calculation unit 154 uses the mode value of the attribute value of each attribute of the item attribute data instead of using the attribute value (average value) of each attribute of the item attribute data in the method (1). Also good.

  Next, (2) a method for calculating the attribute suitability when a distribution of attribute values of a plurality of individuals is used as the attribute value of each attribute of the item attribute data will be described.

The attribute suitability calculation unit 154 may calculate the attribute suitability according to the degree of approximation between the waveform of the personal attribute data and the distribution of the item attribute data. For example, the attribute suitability calculation unit 154 acquires distribution data of a plurality of attributes of item attribute data, calculates the distribution ratio of item attribute data in a predetermined section including the attribute value of personal attribute data for each attribute, This may be used as the fitness of each attribute. As an example, an attribute value of the attribute _{x 1} personal attribute data 100% (corresponding to the person 1 in FIG. 2), the distribution ratio of attributes _{x 1} 90% or more sections of the item attribute data (including 100%) If is 10% (corresponding to the distribution shown in FIG. 3), the attribute fitness calculating unit 154, the attribute fitness attributes _{x 1} may be calculated to 0.1 (10%). The attribute suitability calculation unit 154 calculates the attribute suitability for a plurality of attributes, and may use the average or the weighted average of the attribute suitability of the plurality of attributes as the attribute suitability between the personal attribute data and the item attribute data.

The attribute suitability calculation unit 154 calculates the attribute suitability between the personal attribute data and the item attribute data based on the numerical value related to the distribution of each attribute of the item attribute data instead of the information of the distribution of each attribute of the item attribute data. It may be calculated. For example, the attribute suitability calculation unit 154 virtually generates the original distribution of each attribute of the item attribute data from data such as the average, distribution, and distribution type of each attribute distribution of the item attribute data. The distribution ratio of the attribute value of the attribute _xn of the personal attribute data in the generated distribution may be calculated, and this may be used as the attribute suitability of each attribute. As described above, in (2), the attribute fitness calculation unit 154 represents the item attribute data as a distribution, and calculates the attribute fitness based on the distribution ratio of the item attribute data and the attribute value of the personal attribute data.

  The attribute suitability calculation unit 154 may calculate the attribute suitability using both the methods (1) and (2) (for example, an average or a weighted average). The attribute suitability calculation unit 154 supplies the calculated attribute suitability to the item output unit 156.

  Next, the item output unit 156 selects and prioritizes some of the items based on the item suitability and the attribute suitability (S270). For example, the item output unit 156 calculates the sum of the item suitability calculated in S250 and the attribute suitability calculated in S260 for a plurality of items hit in the search in S230, and according to the size of the total value. Rank items. Then, the item output unit 156 assigns a higher priority to higher-order items (that is, items with a large total value).

  Next, the item output unit 156 outputs all or some of the plurality of items according to the priority (S280). For example, the item output unit 156 generates a search result in which items are arranged from the top in descending order of priority to the user terminal 20 and supplies the search result to the user terminal 20. Further, the item output unit 156 may execute an emphasis process according to priority and output a plurality of items. For example, the item output unit 156 displays the display size of each item (font or image size, etc.) or modifies the display (font bolding, underlining, color change, etc.) according to the priority. May be changed.

  As described above, the apparatus 100 according to the present embodiment searches the item with the search key input by the user by executing the flow of S210 to S280, and the item suitability of the item selected by the search key and the user. Based on both the attribute suitability of the items, the retrieved items can be provided to the user in an appropriate manner.

  FIG. 8 shows an example of the selection instruction from the user and the output of the apparatus 100. For example, the apparatus 100 receives “vanilla ice” as a search key from the user via the user terminal 20 in S220. Then, the apparatus 100 selects an item “ABC vanilla bar”, an item “vanilla ice soft”, an item “ice vanilla super”, and the like related to “vanilla ice”. Further, the device 100 selects the item “ABC vanilla bar”, the item “vanilla ice software”, and the item “Avanilla ice soft” according to the item suitability of each item selected with the search key “vanilla ice” , Item “Ice Vanilla Super” etc. are prioritized and output together with URL information of each item. Here, the item “ABC vanilla bar” is output with the highest priority, and then the item “vanilla ice soft” is output with the highest priority.

  FIG. 9 shows an example of prioritizing items by the item output unit 156 in FIG. Here, the apparatus 100 calculates the item fitness and the attribute fitness for each of the items hit in S230 (such as “ABC vanilla bar”). As shown in this example, the item output unit 156 may further set an output priority for each item. For example, the device 100 may set the output priority based on the amount of money presented by the manufacturer of each item. As illustrated, the item output unit 156 may calculate the total score of the item suitability, the attribute suitability, and the output priority as a total score. As illustrated in FIG. 8, the item output unit 156 displays each item in the search result in the order of the overall score.

  FIG. 10 shows a third processing flow of the apparatus 100 according to the present embodiment. The apparatus 100 outputs items that match the personal attribute data of the user and the item attribute data of the item selected by the user in accordance with the selection instruction input by the user by executing the processes of S310 to S390. Here, a case will be described in which the user inputs an instruction to select an item from two or more selected items included in the screen presented to the user as a selection instruction.

  First, the personal attribute acquisition unit 120 acquires personal attribute data for operating the user terminal 20 (S310). The personal attribute acquisition unit 120 may acquire personal attribute data by a process similar to S210.

  Next, the instruction acquisition unit 110 supplies a screen including a plurality of selection items to the user terminal 20 (S320). For example, the instruction acquisition unit 110 includes a plurality of selection items that can be selected by the user terminal 20 via the network 22 (for example, items provided with hyperlinks or buttons that jump to the web page of the next hierarchy). Provide a web page screen.

  Next, the instruction acquisition unit 110 acquires a selection instruction indicating a selection item selected by the user from two or more selection items included in the screen presented to the user (S330). For example, the instruction acquisition unit 110 acquires, from the user terminal 20, information corresponding to one item selected by the user from a plurality of selected items (for example, clicking an item hyperlink). The instruction acquisition unit 110 supplies the acquired selection instruction to the item attribute acquisition unit 130.

  Next, the item attribute acquisition unit 130 acquires item attribute data corresponding to each of the plurality of selection items included in the screen presented to the user (S340). That is, the item attribute acquisition unit 130 acquires item attribute data generated based on personal attribute data of a plurality of individuals who prefer each item in S150 for a plurality of selected items presented to the user. Here, the item attribute acquisition unit 130 acquires item attribute data of a plurality of selection items included in all or some of the screens that may be displayed after the screen displayed in S320 by the user's selection in S330. It's okay.

  For example, the item attribute acquisition unit 130 may acquire item attribute data of a plurality of selection items displayed on the next screen after selecting an item related to the selection instruction on the screen displayed in S320. Furthermore, the item attribute acquisition unit 130 is a next screen after selecting each of a plurality of selection items included in the screens after the next screen (for example, the next screen, the next next screen, etc.). You may acquire the item attribute data of all the selection items to display. The item attribute acquisition unit 130 may acquire item attribute data of all selection items that may be displayed on a screen subsequent to the item related to the selection instruction (also referred to as a lower screen), or the lowest Item attribute data of all selection items that may be displayed on a lower screen other than the above screen (a screen that does not include selectable selection items) may be acquired. As described above, the item attribute acquisition unit 130 acquires item attribute data of a plurality of selection items included in a lower hierarchy of the selection item related to the screen being displayed.

  In addition, the item attribute acquisition unit 130 may acquire item attribute data of a plurality of selection items included in a screen that may be displayed after the screen displayed in S320 before the user's selection in S330. The item attribute acquisition unit 130 supplies the acquired item attribute data of each item to the attribute suitability calculation unit 154.

  Next, the attribute suitability calculating unit 154 compares the user who has input the selection instruction with each selected item based on the comparison result obtained by comparing the personal attribute data of the user acquired in S310 and the item attribute data of the selected item acquired in S340. The attribute matching degree is calculated (S360). The attribute suitability calculation unit 154 may calculate the attribute suitability by a method similar to the method described in S260. The attribute suitability calculation unit 154 supplies the calculated attribute suitability to the item output unit 156.

  Next, the item output unit 156 prioritizes each selection item to be included in the next screen to be presented to the user according to the selection item selected by the user based on the comparison result obtained by comparing the personal attribute data and the item attribute data. (S370). For example, the item output unit 156 assigns a high priority to a plurality of selection items displayed on and after the next screen calculated in S360 in descending order of the degree of attribute matching.

  Next, the item output unit 156 outputs the next selected item according to the priority (S380). For example, the item output unit 156 may supply the user terminal 20 with a screen related to the selection item with the highest priority (referred to as the highest priority selection item) as a display screen. The item output unit 156 may display a plurality of selection items (highest priority next hierarchy selection item) next to the highest priority selection item in the screen of the highest priority selection item. Here, the item output unit 156 may calculate a plurality of highest-priority next-tier selection items in order of highest priority of the highest-priority next-tier selection item, or according to the priority of the highest-priority next-tier selection item The display size or display modification of each item may be changed.

  Next, the instruction acquisition unit 110 determines whether or not the current display screen includes a next selectable selection item (S390). If the current display screen includes selectable selection items, the instruction acquisition unit 110 returns the process to S330 and accepts the next selection instruction from the user terminal 20 again. If not, the instruction acquisition unit 110 may end the process.

  As described above, the apparatus 100 according to the present embodiment executes the flow of steps S310 to S390, so that the user selects the next item based on the attribute suitability from the selection items subsequent to the selection item selected from the plurality of selection items. Determine the selection items to be displayed on the screen. Accordingly, the apparatus 100 can support, for example, the user following a link and aiming at a target page using the user's personal attribute data as a hint.

  FIG. 11 shows an example of the hierarchical structure of the selected item. The apparatus 100 may assume a plurality of selection items having a hierarchical structure according to FIG. 11 when executing the third processing flow. In response to the user selecting one of a plurality of selection items on the screen of the user terminal 20 by clicking or tapping, the device 100 is included in any hierarchy below the selected selection item. A screen including the selected item is supplied to the user terminal 20.

  As shown in the figure, there is a selection item “weekday afternoon” on the first level, and selection items “rest”, “search for nearby stations”, and “taxi arrangement” on the second level next to “weekday afternoon”. There is a selection item of “convenience store search”, “coffee shop search” and “news search” in the third hierarchy next to “Holiday break”, and “cafe shop A” in the fourth hierarchy next to “coffee shop search” , “Café Store B” and “Coffee Store C” are selected items. In FIG. 11, there may be selection items other than those illustrated. For example, although not shown, there may be further selected items in one or a plurality of lower layers such as “search for nearby stations” and “arrange a taxi”.

  FIG. 12 shows an example of a display screen in the first hierarchy. For example, the instruction acquisition unit 110 provides the user terminal 20 with a display screen related to “weekday afternoon” in the first hierarchy in the first process of S320. Here, on the display screen, three selection items such as “break” relating to the second hierarchy are displayed as selectable items. Here, for example, when the user clicks on the “Holiday” link and inputs the “Holiday” to the user terminal 20 as a selection instruction, conventionally, “Convenience store search” on the next layer of “Holiday”, etc. The selected item is displayed as a selectable selectable item.

  FIG. 13 shows an example of a display screen after inputting a selection instruction. According to the apparatus 100 of the present embodiment, for example, when the user clicks on the “break” link in FIG. 12 and inputs the “break” to the user terminal 20 as a selection instruction in S330, the apparatus 100 displays “break” in S360. Attribute matching between all selected items in the lower hierarchy ("convenience store search", "coffee shop search", "news search", "cafe store A", "cafe store B", "coffee specialty store C", etc.) The degree is calculated, and “search for a coffee shop” having the highest attribute matching degree is determined as the highest priority selection item in S370. In step S380, the apparatus 100 outputs a display screen related to “coffee shop search” illustrated in FIG. Here, the apparatus 100 includes a plurality of selection items (such as “cafe shop A”) in the next level in a selectable state on the display screen relating to “coffee shop search”.

  Normally, the user cannot display the display screen related to “Search for coffee shop” unless “Search for coffee shop” is selected from a plurality of selection items (such as “Convenience store search”) in the third hierarchy. According to the apparatus 100, the selection can be omitted and a “coffee shop search” screen can be provided to the user as soon as possible. Thereby, even when the user follows a predetermined link hierarchy on the web page, the apparatus 100 moves ahead of the web page suitable for the user according to the degree of matching between the user and each link item (item). Can be displayed.

  In the above description, it has been described that the apparatus 100 calculates the attribute suitability between the personal attribute data of the user who has input the selection instruction and the item attribute data of each item by the attribute suitability calculation unit 154. The degree calculation unit 154 may calculate the attribute suitability between a plurality of items. For example, the attribute suitability calculation unit 154 may calculate the attribute suitability for each attribute according to the degree of overlap of the distribution of each attribute of the plurality of items.

FIG. 14 shows the distribution of attributes relating to item 1 and item 2. Shown here is the distribution and distribution attributes x ₁ item 2 attributes x ₁ item 1. As shown in the figure, the distribution of item 1 and the distribution of item 2 partially overlap. The attribute suitability calculation unit 154 may calculate the overlapping area as the attribute suitability of the attribute x ₁ of the item 1 and the item 2.

FIG. 15 shows an example of the attribute suitability of item 1 and item 2. As shown in the figure, the attribute suitability calculation unit 154 calculates the attribute suitability of item 1 and item 2 for N attributes of attribute x ₁ , attribute x ₂ ,..., Attribute x _N. For example, the attribute fitness calculation unit 154 may calculate the attribute fitness of attribute x ₁ as 25 from the area of the overlapping distribution in FIG. The attribute suitability calculation unit 154 may calculate the final attribute suitability of the item 1 and the item 2 by averaging or weighting average the attribute suitability of a plurality of attributes.

  The apparatus 100 may use the attribute conformity between items for various purposes. For example, an activity (such as display on a web page or direct mail transmission) that recommends or advertises another item 2 having a high degree of attribute conformity to the item 1 may be performed for a user who likes a certain item 1.

  FIG. 16 shows an example of the configuration of a computer 1900 according to this embodiment. A computer 1900 according to this embodiment functions as the apparatus 100. In addition, the computer connected to the network 22 as the search server 30 and / or the user terminal 20 and the computer connected to the network 42 as the store terminal 40 and / or the history management server 46 have the same configuration as the computer 1900. You may take it.

  A computer 1900 according to this embodiment is connected to a CPU peripheral unit having a CPU 2000, a RAM 2020, a graphic controller 2075, and a display device 2080 that are connected to each other by a host controller 2082, and to the host controller 2082 by an input / output controller 2084. An input / output unit having a communication interface 2030, a hard disk drive 2040, and a DVD drive 2060, a ROM 2010 connected to an input / output controller 2084, a flash memory drive 2050, and a legacy input / output unit having an input / output chip 2070. .

  The host controller 2082 connects the RAM 2020 to the CPU 2000 and the graphic controller 2075 that access the RAM 2020 at a high transfer rate. The CPU 2000 operates based on programs stored in the ROM 2010 and the RAM 2020 and controls each unit. The graphic controller 2075 acquires image data generated by the CPU 2000 or the like on a frame buffer provided in the RAM 2020 and displays it on the display device 2080. Instead of this, the graphic controller 2075 may include a frame buffer for storing image data generated by the CPU 2000 or the like.

  The input / output controller 2084 connects the host controller 2082 to the communication interface 2030, the hard disk drive 2040, and the DVD drive 2060, which are relatively high-speed input / output devices. The communication interface 2030 communicates with other devices via a network by wire or wireless. The communication interface functions as hardware that performs communication. The hard disk drive 2040 stores programs and data used by the CPU 2000 in the computer 1900. The DVD drive 2060 reads a program or data from the DVD 2095 and provides it to the hard disk drive 2040 via the RAM 2020.

  The input / output controller 2084 is connected to the ROM 2010, the flash memory drive 2050, and the relatively low-speed input / output device of the input / output chip 2070. The ROM 2010 stores a boot program that the computer 1900 executes at startup and / or a program that depends on the hardware of the computer 1900. The flash memory drive 2050 reads a program or data from the flash memory 2090 and provides it to the hard disk drive 2040 via the RAM 2020. The input / output chip 2070 connects the flash memory drive 2050 to the input / output controller 2084 and inputs / outputs various input / output devices via, for example, a parallel port, a serial port, a keyboard port, a mouse port, and the like. Connect to controller 2084.

  A program provided to the hard disk drive 2040 via the RAM 2020 is stored in a recording medium such as a flash memory 2090, a DVD 2095, or an IC card and provided by a user. The program is read from the recording medium, installed in the hard disk drive 2040 in the computer 1900 via the RAM 2020, and executed by the CPU 2000.

  A program installed in the computer 1900 and causing the computer 1900 to function as at least a part of the apparatus 100 includes an instruction acquisition module, a personal attribute acquisition module, an item attribute acquisition module, an output processing module, an item fitness calculation module, and an attribute fitness calculation. At least one of a module and an item output module is provided. These programs or modules work with the CPU 2000 or the like to change the computer 1900 into the instruction acquisition unit 110, the personal attribute acquisition unit 120, the item attribute acquisition unit 130, the output processing unit 150, the item fitness calculation unit 152, and the attribute fitness calculation. Unit 154 and item output unit 156 may function.

  When the information processing described in these programs is read into the computer 1900, it works on the CPU 2000, which is a specific means in which the software and the various hardware resources described above cooperate, and instructs the computer 1900 to It functions as the acquisition unit 110, the personal attribute acquisition unit 120, the item attribute acquisition unit 130, the output processing unit 150, the item suitability calculation unit 152, the attribute suitability calculation unit 154, and the item output unit 156. And the specific apparatus 100 according to the intended use is constructed | assembled by implement | achieving the calculation or processing of the information according to the intended use of the computer 1900 in this embodiment by these specific means.

  As an example, when communication is performed between the computer 1900 and an external device or the like, the CPU 2000 executes a communication program loaded on the RAM 2020 and executes a communication interface based on the processing content described in the communication program. A communication process is instructed to 2030. Under the control of the CPU 2000, the communication interface 2030 reads transmission data stored in a transmission buffer area or the like provided on a storage device such as the RAM 2020, the hard disk drive 2040, the flash memory 2090, or the DVD 2095, and transmits it to the network. Alternatively, the reception data received from the network is written into a reception buffer area or the like provided on the storage device. As described above, the communication interface 2030 may transfer transmission / reception data to / from the storage device by a DMA (direct memory access) method. Instead, the CPU 2000 transfers the storage device or the communication interface 2030 as a transfer source. The transmission / reception data may be transferred by reading the data from the data and writing the data to the communication interface 2030 or the storage device of the transfer destination.

  Further, the CPU 2000 DMAs all or necessary portions of files or databases stored in an external storage device such as the hard disk drive 2040, the DVD drive 2060 (DVD 2095), the flash memory drive 2050 (flash memory 2090), or the like. The data is read into the RAM 2020 by transfer or the like, and various processes are performed on the data on the RAM 2020. Then, CPU 2000 writes the processed data back to the external storage device by DMA transfer or the like. In such processing, since the RAM 2020 can be regarded as temporarily holding the contents of the external storage device, in the present embodiment, the RAM 2020 and the external storage device are collectively referred to as a memory, a storage unit, or a storage device.

  For example, the storage unit of the apparatus 100 includes an instruction acquisition unit 110, a personal attribute acquisition unit 120, an item attribute acquisition unit 130, an output processing unit 150, an item suitability calculation unit 152, an attribute suitability calculation unit 154, and an item output unit 156. Data received from and / or provided to them, and databases in the personal attribute database 52 and the item attribute database 54 may be stored as appropriate. For example, the storage unit may receive and store personal attribute data that the personal attribute acquisition unit 120 supplies to the output processing unit 150. The storage unit may store the attribute suitability calculated by the attribute suitability calculation unit 154 and the like.

  Various types of information such as various programs, data, tables, and databases in the present embodiment are stored on such a storage device and are subjected to information processing. Note that the CPU 2000 can also store a part of the RAM 2020 in the cache memory and perform reading and writing on the cache memory. Even in such a form, the cache memory bears a part of the function of the RAM 2020. Therefore, in the present embodiment, the cache memory is also included in the RAM 2020, the memory, and / or the storage device unless otherwise indicated. To do.

  In addition, the CPU 2000 performs various operations, such as various operations, information processing, condition determination, information search / replacement, etc., described in the present embodiment, specified for the data read from the RAM 2020 by the instruction sequence of the program. Is written back to the RAM 2020. For example, when performing the condition determination, the CPU 2000 determines whether or not the various variables shown in the present embodiment satisfy the conditions such as large, small, above, below, equal, etc., compared to other variables or constants. If the condition is satisfied (or not satisfied), the program branches to a different instruction sequence or calls a subroutine.

  Further, the CPU 2000 can search for information stored in a file or database in the storage device. For example, in the case where a plurality of entries in which the attribute value of the second attribute is associated with the attribute value of the first attribute are stored in the storage device, the CPU 2000 displays the plurality of entries stored in the storage device. The entry that matches the condition in which the attribute value of the first attribute is specified is retrieved, and the attribute value of the second attribute that is stored in the entry is read, thereby associating with the first attribute that satisfies the predetermined condition The attribute value of the specified second attribute can be obtained.

  In addition, when a plurality of elements are listed in the description of the embodiment, elements other than the listed elements may be used. For example, when “X executes Y using A, B, and C”, X may execute Y using D in addition to A, B, and C.

  As mentioned above, although this invention was demonstrated using embodiment, the technical scope of this invention is not limited to the range as described in the said embodiment. It will be apparent to those skilled in the art that various modifications or improvements can be added to the above-described embodiment. It is apparent from the scope of the claims that the embodiments added with such changes or improvements can be included in the technical scope of the present invention.

  The order of execution of each process such as operations, procedures, steps, and stages in the apparatus, system, program, and method shown in the claims, the description, and the drawings is particularly “before” or “prior to”. It should be noted that the output can be realized in any order unless the output of the previous process is used in the subsequent process. Regarding the operation flow in the claims, the description, and the drawings, even if it is described using “first”, “next”, etc. for convenience, it means that it is essential to carry out in this order. It is not a thing.

DESCRIPTION OF SYMBOLS 10 System 20 User terminal 22 Network 30 Search server 32 Search database 40 Store terminal 42 Network 44 Purchasing history database 46 History management server 52 Personal attribute database 54 Item attribute database 100 Apparatus 110 Instruction acquisition part 120 Personal attribute acquisition part 130 Item attribute acquisition Unit 150 output processing unit 152 item fitness calculation unit 154 attribute fitness calculation unit 156 item output unit 1900 computer 2000 CPU
2010 ROM
2020 RAM
2030 Communication interface 2040 Hard disk drive 2050 Flash memory drive 2060 DVD drive 2070 Input / output chip 2075 Graphic controller 2080 Display device 2082 Host controller 2084 Input / output controller 2090 Flash memory 2095 DVD

Claims (10)

A personal attribute acquisition unit that acquires personal attribute data including a plurality of attribute values possessed by an individual with respect to a plurality of attributes;
In correspondence with each of a plurality of items, an item attribute acquisition unit that acquires item attribute data indicating a plurality of attribute data that each item has for the plurality of attributes,
An instruction acquisition unit that acquires a selection instruction of a user who selects some items from the plurality of items;
An output processing unit that prioritizes and outputs each item selected by the selection instruction among the plurality of items based on a comparison result comparing the personal attribute data and the item attribute data;
A device comprising: The instruction acquisition unit acquires a search key for searching for some items from the plurality of items as the selection instruction,
The apparatus according to claim 1, wherein the output processing unit prioritizes and outputs each item that hits the search key among the plurality of items based on the comparison result. The output processing unit
An item suitability calculator that calculates an item suitability representing the suitability of an item with respect to the search key;
An attribute suitability calculator that calculates an attribute suitability based on a comparison result comparing the item attribute data of the personal attribute data and the item;
An item output unit that selects and prioritizes and outputs some items based on the item fitness and the attribute fitness from the plurality of items;
The apparatus according to claim 2.   4. The item attribute acquisition unit according to claim 1, wherein the item attribute acquisition unit acquires the item attribute data of each item generated based on the personal attribute data of a plurality of individuals who prefer each item. 5. apparatus.   The apparatus according to claim 4, wherein the item attribute acquisition unit generates the item attribute data of each item based on the plurality of personal attribute data for the plurality of individuals who prefer each item.   The item attribute acquisition unit is configured to calculate each attribute in the item attribute data of each item based on a result of averaging the plurality of personal attribute data for the plurality of individuals who prefer each item for each of the plurality of attributes. The apparatus according to claim 5, wherein the attribute data is generated.   The item attribute acquisition unit, based on the distribution of each of the plurality of attributes in the plurality of personal attribute data for the plurality of individuals who prefer each item, of each attribute in the item attribute data of each item The apparatus of claim 5, wherein the apparatus generates attribute data. The item attribute acquisition unit acquires the item attribute data corresponding to each of a plurality of selection items included in a screen presented to a user,
The instruction acquisition unit acquires the selection instruction indicating a selection item selected by the user from two or more selection items included in the screen presented to the user,
The output processing unit prioritizes each selection item to be included in the next screen to be presented to the user according to the selection item selected by the user based on a comparison result comparing the personal attribute data and the item attribute data. The apparatus according to claim 1, wherein the apparatus outputs the output. A method performed by a computer,
A personal attribute acquisition stage for acquiring personal attribute data including a plurality of attribute values that an individual has for a plurality of attributes;
In response to each of a plurality of items, an item attribute acquisition step of acquiring item attribute data indicating a plurality of attribute data of each item for the plurality of attributes,
An instruction acquisition step of acquiring a selection instruction of a user who selects some items from the plurality of items;
An output processing stage that prioritizes and outputs each item selected by the selection instruction among the plurality of items based on a comparison result obtained by comparing the personal attribute data and the item attribute data;
A method comprising: When executed, the computer
A personal attribute acquisition unit that acquires personal attribute data including a plurality of attribute values possessed by an individual with respect to a plurality of attributes;
In correspondence with each of a plurality of items, an item attribute acquisition unit that acquires item attribute data indicating a plurality of attribute data that each item has for the plurality of attributes,
An instruction acquisition unit that acquires a selection instruction of a user who selects some items from the plurality of items;
An output processing unit that prioritizes and outputs each item selected by the selection instruction among the plurality of items based on a comparison result comparing the personal attribute data and the item attribute data;
Program to make it work.

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