JP2013003797A - Interest analytic method and interest analyzer - Google Patents

Abstract

PROBLEM TO BE SOLVED: To estimate interest of a user with high accuracy by using the scarcity of concept appearance.SOLUTION: A feature score calculating part 120 clusters a first content list obtained by browsing a plurality of contents as a list and a second content list obtained by browsing a content body from the first content list, calculates analysis parameters S, N, a, n from the first content list and the second content list in each cluster, calculates a first probability that the number of contents whose concept appears in the second content list is n or more and a second probability that the number of contents is n or less under the condition of S, N, a, and calculates a feature score by an inverse function of a cumulative distribution function of a standard normal distribution on the basis of the first probability and the second probability. A concept system update processing part 130 uses the feature score calculated in each cluster to update a user interest score to a concept.

Description

  The present invention relates to an interest analysis method and an interest analysis device that analyze a user's interest from a content browsing history or the like.

  There is a demand for a technique for recommending appropriate services and contents in accordance with user behavior and situations. For this reason, a technique has been proposed for estimating a user's interest from history information, such as estimating a user's interest from a book information browsing history in the site and recommending a book at a book sales site. In such a method, assuming that meta information for summarizing the contents is given to each content, a method for estimating the user's interest from the frequency of concepts appearing in the user history is a content-based filtering technique (Content Based Filtering (CBF), research is being conducted especially as a memory-based method.

  Specifically, the content-based filtering technique presents products of the same brand (holding the same concept tag) when browsing products of a specific brand (holding information indicating the brand as a concept tag). In this case, the memory-based method presents a product of a specific brand if the specific brand is frequently browsed from the history of browsing in the past. In a simple method, products related to concept tags that appear more frequently in the browsing history are presented. As a method using a taxonomy (ontology) in such a method, for example, there is “Proposal of a multifaceted preference grasping method using an operation history” in Non-Patent Document 1.

"Proposal of multifaceted preference grasp method using operation history", IEICE Technical Report, vol. 110, no. 450, LOIS2010-76, pp. 67-72, March 2011

  However, in the prior art, in the content browsing history of the user, the estimation is performed by a numerical value obtained by indexing the content appearance number of the concept. However, in this case, since only the fact that the content has been selected is input, it has not been possible to take advantage of the feature that the user does not relatively select content having a specific concept.

  The present invention has been made paying attention to the above circumstances, and an object of the present invention is to provide an interest analysis method and an interest analysis device that can estimate the user's interest with high accuracy using the scarcity of concept appearance. It is in.

In order to achieve the above object, the present invention takes the following measures.
According to a first aspect of the present invention, there is provided a method and apparatus for analyzing user interests using a concept system in which user interest scores for a plurality of concepts are systematized by a computer. And a second content list obtained by browsing the main body of content from the first content list, and for each cluster, the total number of contents in the first content list is set to the first total number, The number of contents in which the concept appears in the first content list is the first appearance number, the total number of contents in the second content list is the second total number, and the concept is in the second content list. When the number of appearing contents is the second number of appearances, the first total number, the first number of appearances, and the second number A first probability that the number of contents in which the concept appears in the second content list is greater than or equal to the second occurrence number and a second probability that is less than or equal to the second occurrence number under the condition of the total number And calculating a feature score by an inverse function of a cumulative distribution function of a standard normal distribution based on the first probability and the second probability, and based on the first probability and the second probability The feature score is calculated by an inverse function of the cumulative distribution function of the standard normal distribution, and the user interest score for the concept is updated using the feature score calculated for each cluster.

  That is, according to the first aspect, in the second content list selected by the user from the first content list, the first number of contents in which each concept appears in the first content list that is a user selection candidate By calculating the second content number in which each concept appears and comparing and analyzing the first content number and the second content number, the rareness of the appearance of each concept is considered, and the content of the selection candidate Since the concept given to can utilize not only the relatively “chosen” event but also the relatively “unchosen” event appearance feature, the user's interest is highly accurate. Can be estimated.

According to a second aspect of the present invention, in the updating step of the first aspect, a user operation history related to the second content, a browsing time and a browsing status of the content, and a relationship between the second content and the concept Further, the characteristic score is weighted according to at least one of the degrees.
According to the second aspect, the user interest score is calculated based on the user's behavior and situation by reflecting the user's situation at the time of browsing and features of the browsing operation (favorite registration, long-time browsing, etc.). The user interest score can be obtained more accurately.

According to a third aspect of the present invention, in the calculation step of the first aspect, the first probability and the second probability are obtained by a hypergeometric distribution.
According to the third aspect, by obtaining the second probability by a hypergeometric distribution, not only the feature that the user relatively “selects” content having a specific concept compared to chance, but also the user selects a specific concept. It is possible to obtain the user's interest score with high accuracy by utilizing the feature that the content possessed is relatively “not selected” compared to chance.

  According to a fourth aspect of the present invention, in the first aspect, the first content list and the second content list are obtained when the concept system has relationship information of a superordinate concept and a subordinate concept between the concepts. It is further characterized in that a superordinate concept of appearance concepts appearing in the content is extracted from the concept system, and the user interest score for the superordinate concept is updated assuming that the superordinate concept appears in the content. . The relation information of the concept system can have a graph structure because a large number of the concepts are related to each other. In the case of having a graph structure, a direct superordinate concept of each concept is called a “parent concept”, and a direct subordinate concept is called a “child concept”.

  According to the fourth aspect, in content browsing history analysis using a system that expresses conceptual relationships defined as taxonomies (ontologies) or the like, not only the concepts that appear in the content but also the content that is given a superordinate concept Can be analyzed reasonably and accurately from a statistical point of view.

According to a fifth aspect of the present invention, in the fourth aspect, the subordinate concept of the appearing concept and the subordinate concept of the superordinate concept are extracted from the concept system, and the user interest score of the appearing concept and the user of the superordinate concept It is further characterized in that the user interest score of the subordinate concept is updated using the interest score.
According to the fifth aspect, it is possible to reasonably and accurately analyze not only the concept and superordinate concept that appear in the content, but also these subordinate concepts.

According to a sixth aspect of the present invention, in the first aspect, with respect to content in which one or more concepts appear, a user evaluation score for the content using the user interest score of each concept that appears in the content The method further includes an evaluation step for calculating.
According to the sixth aspect, it is possible to recommend content that matches the user's interest by calculating the user's evaluation score for the content using the user interest score.

In a seventh aspect of the present invention, in the evaluation step of the sixth aspect, the user interest score is weighted according to at least one of the degree of association between the content and each concept and the importance of each concept. Is further characterized.
According to the seventh aspect, when the evaluation score is calculated, the evaluation score can be calculated with higher accuracy by reflecting the degree of association between the content and each concept and the importance of each concept. .

  That is, according to the present invention, it is possible to provide an interest analysis method and an interest analysis device that can estimate the user's interest with high accuracy by utilizing the scarcity of concept appearance.

The whole system figure using the interest analysis device concerning this embodiment. The block diagram which shows the function structure of each apparatus of FIG. The figure which shows the process outline | summary of an interest analyzer. The figure which shows an example of content browsing operation on a client terminal. The figure which shows the data structural example of a list browsing content list. The figure which shows the data structural example of detailed browsing content. The figure which shows the data structural example of a presentation content list. The figure which shows an example of a content database. The figure which shows an example of a concept system / user interest score database. The figure which shows the processing flow of a log | history information receiving part. The figure which shows the processing flow of a characteristic score calculation part. The figure which shows the data structural example of an analysis parameter list. The schematic diagram for demonstrating operation | movement of a characteristic score calculation part. The figure which shows the detail of a characteristic score calculation process. The figure which shows the processing flow of a concept system update process part. The figure which shows the detail of a concept system update process. The figure which shows the processing flow of a content evaluation process part. The figure which shows an example of a content score list. The figure which shows the detail of a content evaluation process. The figure which shows an example of content request data.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
FIG. 1 is an overall system diagram using the interest analysis apparatus according to the present embodiment. This system includes a client terminal 200, a content server 300, and an interest analysis device 100. The client terminal 200 and the content server 300, and the content server 300 and the interest analysis device 100 are connected to each other via a communication network. The user acquires desired content from the content server 300 through a browsing operation on the client terminal 200 and presents the acquired content on the screen of the client terminal 200 for browsing.

  The client terminal 200 collects a content browsing history by a user operation, browses a plurality of content as a list, a list browsing content list (first content list), and a detailed browsing content list that browses the content body from the content list (Second content list) is transmitted to the content server 300. The content server 300 transfers the list browsing content list and the detailed browsing content list to the interest analysis device 100 via the communication network.

  The interest analysis apparatus 100 calculates a feature score and a user interest score for each concept appearing in the content based on the list browsing content list and the detailed browsing content list, and estimates the user's interest. Based on this user interest score, the interest analysis apparatus 100 generates a list of contents sorted according to the user's interest (sorted presented content list) from the “presentation content list” received from the content server 300. To the content server 300.

FIG. 2 is a block diagram showing a functional configuration of each device in FIG.
The interest analysis apparatus 100 includes a history information reception unit 110, a feature score calculation unit 120, a concept system update processing unit 130, a concept system / user interest score database 140, a presented content list reception unit 150, a content database 160, and a content evaluation processing unit 170. , And a sorted content score list transmission unit 180.

FIG. 3 shows an outline of processing of the interest analysis apparatus 100.
The history information receiving unit 110 receives the list browsing content list and the detailed browsing content list from the client terminal 200 via the content server 300. The list browsing content list is, for example, a list of content in which the user browses only the content titles in a list. The detailed browsing content list is a list of content that the user has viewed the content (details) of the content body. For example, in FIG. 3, the list browsing content list includes contents 1 to 8, and the detailed browsing content list includes contents 1, 3, and 4. In FIG. 3, the content indicated by the hatched pattern indicates that the concept B appears in the content 1, 6, 7, and 8.

The feature score calculation unit 120 calculates a feature score (Z value to be described later) of each concept by a statistical model of concept selection using the list browsing content list and the detailed browsing content list.
The concept system update processing unit 130 updates the user interest score for each concept based on the relationship information (superordinate concept and subordinate concept) in the concept system using the feature score. Nodes included in the graph of the concept system represent concepts, and links represent relationships between concepts. The user interest score is held as a value of a node corresponding to each concept in the concept system. In the concept system, the nodes located at the higher level represent the abstract concept, and the nodes located at the lower level represent the specific concept. The concept system and concept ID (identifier assigned to each node) are designed and defined in advance by a service operator or the like.

  The content evaluation processing unit 170 calculates the user's evaluation score for the content by probability combining using the user interest score of each concept appearing in the evaluation content. In the example of FIG. 3, the evaluation score of the content 1 is obtained using the user interest scores of the concepts E, F, and D that appear in the content 1. Detailed operation description of the interest analysis apparatus 100 will be described later.

2, the client terminal 200 includes a history collection unit 210, a history information transmission unit 220, a content presentation unit 230, and a content request transmission unit 240.
The content request transmission unit 240 makes a content presentation request to the content server 300 in accordance with a user instruction (input). Specifically, content request data as shown in FIG. 20 is transmitted to the content server 300. For example, the content request data has a client terminal ID (or user ID) and a request time. The request time may be added in the content server 300. The client terminal ID (or user ID) is a number uniquely assigned to each terminal (or user), and is an ID that matches the user ID of the user interest score table of the conceptual system / user interest score database 140 described later. is there.

FIG. 4 shows an example of a content browsing operation by the user on the client terminal 200.
Based on the sorted presentation content list received from the content server 300, the content presentation unit 230 displays a list from the top of the sort order within the range allowed by the display screen size of the client terminal 200.

  In the example of FIG. 4, ten contents (contents 1 to 10) are displayed in a list. The content in the lower order of the sort order can be displayed by the list by the user's flick, scroll bar operation or the like. The list of contents actually displayed on the client terminal 200 in this way is referred to as a list browsing content list. That is, not all the contents in the sorted presentation content list are displayed on the client terminal 200, and thus are not necessarily included in the list browsing content list. When the user selects a title of each content from this list by clicking or the like, the main body (details) of the content of the selected title (contents 3, 5, and 6 in FIG. 4) can be viewed. The content whose details are browsed is included in the detailed browsing content list.

  As described above, the history collection unit 210 collects user operation histories and creates a list browsing content list and a detailed browsing content list. The history information transmission unit 220 transmits the list browsing content list and the detailed browsing content list created by the history collection unit 210 to the content server 300.

  FIG. 5 shows a data configuration example of the list browsing content list in the case of FIG. The list browsing content list has a cluster ID, a content ID, and a browsing time. The cluster is an identifier ("1" in FIG. 5) uniquely assigned to the list browsing content list and the detailed browsing content list. When the user browses the list browsing content displayed at another time (time zone), another cluster ID is given. The conditions for newly assigning the cluster ID under conditions other than the time include when there is no operation for a certain period of time while the list browsing content list is displayed, or when the browsing user (user ID) is switched, the list browsing content list On the other hand, when a narrow search is performed from the viewpoint of the content genre or the like, the browsing mode may be switched in other browsing applications. The content ID is an identifier uniquely assigned to each content of the list browsing content, and is assumed to match a value held in a content database 160 described later.

  FIG. 6 shows a data configuration example of the detailed browsing content list in the case of FIG. The detailed browsing content list has a cluster ID, a content ID, and a browsing time, like the list browsing content list. The cluster ID is set to the same value as the list browsing content list (“1” in FIG. 6). In the detailed browsing content list, the content ID and browsing time are the identifier of the content (contents 3, 5, and 6 in FIG. 6) that the user has selected from the browsing content and browsed the content and the time when the content was browsed.

  In FIG. 2, the content server 300 includes a content transmission processing unit 310, a sorted presentation content list reception unit 320, a presentation content list transmission unit 330, a presentation content list input unit 340, a history information transfer unit 350, and a content request transfer unit. 360 is provided.

The history information transfer unit 350 transfers the list browsing content list and the detailed browsing content list received from the client terminal 200 to the interest analysis device 100 via the communication network.
The presentation content list input unit 340 receives a presentation content list that lists contents to be presented to the client terminal 200 used by the user by the service operator. The presented content list transmission unit 330 transmits the input presented content list to the interest analysis apparatus 100 via the communication network.

  FIG. 7 shows a data configuration example of the presented content list. The presented content list has a content ID, a concept ID / relevance list, a content body, and a content registration time. The content ID is a unique ID assigned by the content server 300 to each content. The concept ID / relationship degree list stores a concept ID of a concept that appears in content and a set of values indicating the degree of relevance between the concept and the content. The concept ID / relevance degree list is set in advance for each content. As a specific example, content 1 (sports article) includes {“baseball” concept ID = 1, relevance = 0.5}, { A set of concept ID and degree of association is stored as “soccer” concept ID = 2, degree of association = 0.8}, {“golf” concept ID = 3, degree of association = 0.6}. .

  The concept ID matches the value stored in the concept system / user interest score database 140. For example, the relevance is a value from 0 to 1, and the larger the relevance, the stronger the relevance. As the relevance, a value set by the service operator at the time of content registration or a value calculated by another system is used.

The sorted presentation content list receiving unit 320 receives a sorted presentation content list and a client terminal ID (or user ID) obtained by sorting a part or all of the presentation content list from the interest analysis device 100. The content transmission processing unit 310 transmits the sorted presentation content list to the client terminal 200 corresponding to the client terminal ID (or user ID).
The content request transfer unit 360 transfers content request data (FIG. 20), which is a content presentation request from the content request transmission unit 240 of the client terminal 200, to the interest analysis device 100.

Next, the detail of each part of the interest analysis apparatus 100 is demonstrated.
[Content database 160]
FIG. 8 shows an example of the data structure of the content database 160. The content database 160 has a content table and a user history table.

The content table stores a content ID, a concept ID / relevance list, a content body, and a content registration time. The value received by the presented content list receiving unit 150 is stored.
The user history table stores content ID, user ID (client terminal ID), detailed browsing total number, detailed browsing time, list browsing total number, list browsing time, and list non-display flag. The detailed browsing time is null when the total number of detailed browsing is 0, and if it is 1 or more, a list of browsing times in a time series of each browsing is stored. The list browsing time is null when the total number of browsing the list is 0, and if it is 1 or more, a list of browsing times according to the time series of each browsing is stored. The list non-display flag stores false if the user has not yet displayed / viewed as a list on the screen of the client terminal, and stores true if the user has viewed the list once. In the user history table, all content ID values are held for each user ID. As the detailed browsing total number and the list browsing total number, when a large number of list browsing content lists indicated by the cluster ID are received, a cumulative total of past histories is stored.

  For example, by using the data of the user history table, the evaluation score of the content is lowered in the future evaluation (during processing in the content evaluation processing unit 170) according to the number of times the user browses. As a method for reducing the evaluation score, when the number of browsing for a certain content is k, the evaluation score of the content is divided by k + 1, or the evaluation score is multiplied by a weight (for example, 0.9) to the kth power. By this process, it is possible to reduce the repeated presentation of the same content in consideration of the degree of coincidence with the interest, so that the satisfaction with the user's recommendation can be improved.

[Conceptual System / User Interest Score Database 140]
FIG. 9 shows an example of the data structure of the conceptual system / user interest score database 140. The concept system / user interest score 160 includes a root concept node ID, a concept system table, and a user interest score table.

The root concept node ID is a concept node ID at the highest level in the concept system structure. There is only one in the system.
The concept system table stores a self-concept ID, a parent concept ID list, and a child concept ID list. All the self-concept IDs in the concept system are stored in association with a parent concept ID and a child concept ID (however, if the self-concept is the lowest, there is no child concept ID). Defined.
The user interest score table stores values of concept ID, user ID (client terminal ID), TotalZ (user interest score), X, and Y. The definition and calculation method of TotalZ, X, and Y will be described later.

[Presentation content list receiving unit 150]
The presented content list receiving unit 150 receives the presented content list as shown in FIG. 7 from the content server 300 and stores it in the content database 160.

[History information receiving unit 110]
FIG. 10 shows a processing flow of the history information receiving unit 110.
(Step S11: History data reception)
The history information receiving unit 110 receives the client terminal ID (or user ID), the list browsing content list, and the detailed browsing content list from the history information transfer unit 350 of the content server 300 via the communication network, and sends the client information to the feature score calculation unit 120. Output.

[Feature score calculator 120]
FIG. 11 shows a processing flow of the feature score calculation unit 120. A client terminal ID (or user ID), a list browsing content list, and a detailed browsing content list are input from the history information receiving unit 110 to the feature score calculation unit 120.

(Step S12: Appearance concept extraction)
The feature score calculation unit 120 extracts a concept ID that appears in each content in the detailed browsing content list from the content database 160. Specifically, the “concept ID” associated with each content ID in the detailed browsing content list of FIG. 6 is searched from the content table of the content database 160 of FIG. The feature score calculation unit 120 includes cluster data {cluster ID, list browsing content list, detailed browsing content list} and content ID / concept ID association list {{content ID, {related concept ID, ...}}, ... } And the appearance concept list {concept ID}. The “content ID / concept ID association list” is a list of concept IDs searched based on the content ID. The “appearance concept list” is a list of all concept IDs of concepts that appear in each content included in the list browsing content list and the detailed browsing content list.

(Step S13: Appearance concept extraction)
For each concept ID in the “appearance concept list”, the feature score calculation unit 120 extracts a superordinate concept from the concept system / user interest score database 140 of FIG. 9, and sets the superordinate concept IDs as “appearance concept list” and “content”. To the “ID / concept ID association list”.

  Specifically, the feature score calculation unit 120 searches the concept system table in FIG. 9 for the concept ID of the “appearance concept list” that matches the “own concept ID”, and extracts the “parent concept ID”. To do. For example, a search is made for a case where the extracted “parent concept ID” matches the “own concept ID” in the concept system table of FIG. 9, and the “parent concept ID” is also extracted as a superordinate concept. Then, the feature score calculation unit 120 associates the concept ID of the superordinate concept with the content ID having the concept ID of the appearance concept that is the source of extraction. That is, the extracted “superior concept” is regarded as having been assigned a superordinate concept with respect to “content ID having the original concept ID”, and “appearance concept list” “content ID / concept ID association” Add to list. The extraction of the root concept in the concept system hierarchy is excluded.

(Step S14: Analysis parameter extraction)
The feature score calculation unit 120 calculates the number of appearances for each concept in the “appearance concept list”, extracts analysis parameters necessary for calculating the feature score, and generates an analysis parameter list.
FIG. 12 shows a data configuration example of the analysis parameter list. The analysis parameter list includes, for each cluster ID, the total content S (first total) of the list browsing content list, the total content a (second total) of the detailed browsing content list, and the appearance concept list associated with the cluster ID. N and n are calculated for each concept ID. N (first appearance number) is the number of contents to which the concept ID is assigned in the list browsing content list. n (second appearance number) is the number of contents to which the concept ID is assigned in the detailed browsing content list. Note that N and n are calculated for all concept IDs in the appearance concept list including the superordinate concept added in step S13.

  FIG. 13A shows a schematic diagram of the analysis parameter extraction process. For example, a case where the user browses details of 10 (= a) contents from a list of 50 (= S) contents is shown. Here, there are 15 articles (= N) that contain the concept of “baseball” among the 50 contents displayed in a list, and the concept of “baseball” among the 10 contents viewed by the user. This indicates that there are five (= n) contents including “”.

(Step S15: feature score calculation)
The feature score calculation unit 120 calculates a feature score Z for each concept ID using the analysis parameters S, a, N, and n. FIG. 14 shows details of the feature score calculation process. In FIG. 14, i is a concept identifier, and j is a cluster ID. When H1 (first probability) is the total number S of the list browsing contents included in the list browsing content list and the number N of the contents of the list browsing content where the concept i appears, a detailed browsing content is randomly selected and viewed. In this case, the cumulative probability that the number of detailed browsing contents in which the concept i appears is n or more. When H2 (second probability) is the total number S of the list browsing contents included in the list browsing content list and the number of contents N in which the concept i appears in the list browsing contents, a detailed browsing content is randomly selected and viewed. In this case, the cumulative probability that the number of detailed browsing contents in which the concept i appears is n or less. In the present embodiment, the cumulative probabilities H1 and H2 are obtained by the hypergeometric distribution, but are not limited to this method. Examples of other distributions include a binomial distribution and a normal distribution.

  As shown in FIG. 13B, for example, there are five or more contents including the concept of “baseball” among the ten contents viewed by the user using the analysis parameters S, N, a, and n described above. It is shown that the probability of being “0.12”. Here, “0.12” corresponds to the value of the cumulative probability H1.

As an example of using the value of H2, consider the case where n is 0 in the above analysis parameters. In this case, the probability when the number of appearances is 0 or less is calculated. Specifically, in FIG. 13B, the horizontal axis is the value of the item of 0, so “0.02”.
Then, as shown in FIG. 14, the feature score calculation unit 120 calculates the feature score Z by the inverse function of the standard normal distribution cumulative distribution function using the calculated cumulative probabilities H1 and H2. As shown in FIG. 13C, the feature score Z is obtained by the inverse function of the cumulative distribution function of the standard normal distribution with H1 as the cumulative probability. When H2 is used as the cumulative probability, the feature score Z is obtained with the sign of the return value of the inverse function of the standard normal distribution cumulative distribution function being negative. Using this feature score Z, a concept system update processing unit 130, which will be described later, obtains a user interest score (TotalZ) for the concept of “baseball”.

  The feature score calculation unit 120 generates an update target concept list and outputs it to the concept system update processing unit 130. The “update target concept list” is a set of a concept ID, the characteristic score Z calculated above, and a weight w. The concept ID appearing in the update target concept list becomes a node (concept) to be updated in the next concept system update process. The feature score Z and the weight w are calculated for all the concept IDs in the appearance concept list to which the superordinate concept is added. The weight w is a value set for each concept in each cluster ID.

  Note that the weight w can be changed as follows when the initial value w = 1 and a user's characteristic operation is performed. For example, with respect to the content presented to the user at the client terminal 200, the user can register as favorite content, recommend to other users, or input evaluations for the content. When the client terminal 200 can transmit an operation history other than the browsing operation to the interest analysis apparatus 100, the following processing is performed.

  For example, when the content is registered as a favorite, the feature score calculation unit 120 increases the weight w such that w = 1.5 for all the concept IDs included in the content. In addition to the content browsing time, browsing weather, temperature, humidity, season, day of the week, holiday, leisure time, user location information at browsing, scheduler, diary, etc., the value of weight w Can also be changed.

[Concept System Update Processing Unit 130]
FIG. 15 shows a processing flow of the concept system update processing unit 130. The concept system update processing unit 130 receives from the feature score calculation unit 120 the client terminal ID (or user ID) and the update target concept list {cluster ID, {concept ID, feature score = Z, weight = w},. Entered.

(Step S16: Concept node value update)
The concept system update processing unit 130 updates the node value of each concept ID in the “update target concept list”. FIG. 16 shows details of the processing of the conceptual system update processing unit 130. The concept system update processing unit 130 uses the user interest score update formula for each concept i shown in FIG. 16 for the concept that appeared in the content (appearance concept) and the concept ID of the superordinate concept of this appearance concept, and the user interest score. TotalZ _in , X _{i (n−1)} , Y _{i (n−1)} are obtained and input to step S 12 of FIG. 11 corresponding to the cluster ID in the conceptual system / user interest score database 140 of FIG. Each value stored in the user interest score table corresponding to the user ID (client terminal ID) column is updated.

Here, X _{i (n−1)} is the sum of the squares of the weights w of the update target concept list in the past (up to the previous time) for each concept ID (represented by the identifier i here). Similarly, Y _{i (n−1)} is the sum of multiplication of the weight w of the past update target concept list and the feature score Z for each concept ID (represented by identifier i here).

  X and Y hold intermediate results in the user interest score (TotalZ) calculation process. When prioritizing memory saving / storage, at least three real values of TotalZ, X, and Y are used as variables of each node. It can be realized by holding. When priority is not given to memory saving / storage, all the calculated concept scores and feature scores Z of the respective clusters are stored. In this case, storage of X and Y is not necessary.

In FIG. 16, n is an identifier indicating how many times the concept system update process is performed. A series of processes for obtaining the user interest score TotalZ is performed in units of cluster IDs. When the unit in which this series of processes is performed is counted once, n is the number of times this series of processes has been performed. Is an identifier. i is an identifier of a concept ID. Z _in is a Z value used for each update process of concept i. Note that Z _ij is a Z value for each of the list browsing content list and the detailed browsing content list, and has a relationship of Z _ij εZ _in . Weight w _in is the weight to be used in each process of updating the concept i. The weight w is the same as that set by the feature score calculation unit 120.

For example, w _in the favorite registration, if the user a special operation other than the inspection of the public, such as with the other user has performed, and (interval to the end from the viewing start) content viewing time, related the degree of content and concepts, The value is changed in accordance with the content browsing time, weather / temperature / humidity / season / day of the week / holiday / leisure at the time of browsing, user location information at the time of browsing, event information collected from a scheduler / diary and the like. It is also changed when specified by other service users and service operators.

Note that the forgetting of the user interest score, such as to reduce the influence of the history after a certain period, attenuates TotalZ, X, and Y, respectively, when the threshold of the time interval from the last update time to the current time is exceeded. It will be realized.
An example of an attenuation calculation formula is shown. For example, k can be set as an attenuation rate (for example, k = 0 · 8), and can be calculated as follows.
TotalZ (after attenuation) = k × TotalZ (current)
X (after attenuation) = k ² × X (current)
Y (after attenuation) = k ² × Y (current)

(Step S17: Lower concept node value update)
Further, the concept system update processing unit 130 extracts the subordinate concept of each concept ID (appearance concept and superordinate concept) in the “update target concept list”, and updates the node value of the subordinate concept. In the extraction of the subordinate concepts, for each concept ID of the “update target concept list”, the concept system table of the concept system / interest degree database 140 in FIG. 9 is referred to, and a list of concept IDs is extracted from the child concept ID list. The process of extracting the child concept list with reference to the concept system table for the concept ID of each child concept ID list is repeated.

  The feature score Z used to update the interest level of the lower concept is, for example, the value having the largest absolute value of the feature score among the adjacent parent nodes, the value of the closest higher node, the average of the values of the parent nodes Or a probability-coupled value. In the “update target concept list”, the user interest scores of the concepts updated in step S16 (concepts that appear in the content and higher-level concepts) are not updated.

[Content Evaluation Processing Unit 170]
FIG. 17 shows a processing flow of the content evaluation processing unit 170. The content evaluation processing unit 170 receives the notification from the content request transfer unit 360 of the content server 300, reads a presentation content list in the format shown in FIG. 7 from the content table of the content database 160, and performs the following content evaluation processing. Do. The content request transfer unit 360 receives the content request data as shown in FIG. 20 including the client terminal ID (or user ID). In addition, with regard to the above-mentioned presented content list, whether the content registered within the past number of days is to be evaluated based on the prior setting of the service operator or service user (client terminal user) (whether it is included in the presented content list) ) Can be set.

(Step S21: Analysis object concept filtering)
In analysis target concept filtering, when a service operator or a service user designates a concept ID of an analysis target particularly in advance or manually, the concept system / interest degree database 140 is referred to, and the specified analysis target Only concept IDs and subordinate concept IDs are evaluated. The content evaluation processing unit 170 excludes the concept ID held in the input presentation content list from the analysis target according to the conditions set in advance by the service operator or the service user, and generates a “filtered content list”.

  For example, when the user requests a recommendation for content related to baseball, the concept system table in FIG. 9 is referred to, and only the subordinate concepts of the concept ID corresponding to baseball are analyzed. The “filtered content list” is a content list obtained by deleting the concept ID associated with each content ID according to the conditions set in advance by the service operator or the user by the above processing. The “filtered content list” has the same data configuration as the presented content list of FIG.

(Step S22: Score evaluation)
The content evaluation processing unit 170 calculates the evaluation score of the content included in the “filtered content list” and generates a content score list as shown in FIG. The content score list has a content ID, an evaluation score, a content body, and a content registration time.

FIG. 19 shows an example of a method for calculating the evaluation score. For example, according to the content evaluation formula shown in FIG. 19, the evaluation score EntityZ _x for the content x is changed to the user interest score TotalZ _{i of} the concept i, the relevance w _i between the content x and the concept _i (or the importance of the concept i), and It can be calculated using a set p of concept IDs appearing in the content x. The concept identifier i corresponds to the concept ID in the set p.

The user interest score (TotalZ) used in the calculation of FIG. 19 is the client terminal ID (or user) from the user interest score table (FIG. 9) of the concept system / user interest score database 140 for the concept ID related to each content. Read out based on (ID). In FIG. 19, when the content 1 in which the concept K, the concept B, and the concept D appear is the evaluation content, the evaluation score EntityZ _x = 0.18 is calculated using the TotalZ, w of the concept K, the concept B, and the concept D. it can. On the other hand, when the content 2 in which only the concept B appears is set as the evaluation content, the evaluation score EntityZ _x = −0.3 can be calculated using the TotalZ, w of the concept B. Content 1 with a large value of evaluation score EntityZ _x is displayed preferentially.

In addition, the evaluation score EntityZ _x can be obtained by the following methods 1 to 3.
As a first modification, it is obtained by EntityZ _x = MAX (TotalZ _i * w _i ). MAX (TotalZ _i * w _i ) is a function that returns the maximum value of TotalZ _i * w _i for i∈p.

The second modification, the value of EntityZ _x When the value of the MAX (TotalZ _i * _{w i)} exceeds the threshold value, the return value of _{MAX (TotalZ i * w i)} . MAX (TotalZ _i * w _i ) is a function that returns the maximum value of TotalZ _i * w _i for i∈p. If the threshold is not exceeded, the result of the content evaluation formula in FIG. 19 is set to EntityZ _x . MAX () is, is a function that returns the maximum value in TotalZ _i * _{w i} of i∈p. The threshold is a value set by the service operator.

As a third modified example, only the value iεp where TotalZ _i is a positive value is extracted, and the value integrated by the content evaluation formula of FIG. 19 is defined as EntityZ _x .
(Step S23: Sort contents in score order)
The content evaluation processing unit 170 sorts the content included in the content score list in descending order of the evaluation score EntityZ _x and outputs the sorted content score list to the content score list transmission unit 180.

[Sorted content score list transmission unit 180]
The sorted content score list transmission unit 180 transmits the sorted content score list and client terminal ID (or user ID) input from the content evaluation processing unit 170 to the content server 300 via the communication network.

  As described above, according to the above-described embodiment, the list of candidates for user selection is defined, and the number of appearances of concepts in content selection from there is analyzed, thereby taking into account the rarity of appearance of each concept. In addition, since it is possible to use history features of concepts that are not selected from the list, it is possible to estimate the user's interest with high accuracy.

  Further, as shown in steps S15 and S16, when calculating the feature score and the steer user interest score, the weighting factor is used to calculate the user's situation at the time of browsing and the characteristics of browsing operation (favorite registration, long-time browsing, etc.). Since it can reflect via (weight w), it becomes possible to obtain | require a user interest score still more accurately.

  In addition, in the content browsing history analysis in which the concept defined by taxonomy (ontology) etc. was added as a meta tag, it was difficult to rationally reflect the rareness of concept appearance in the analysis. Although it was necessary to adjust the ontology structure side such as unifying the abstraction level of the concept to be given to the content by fixing the thickness, etc., in this embodiment, the user interest score by the ontology structure that takes into account the rarity of concept appearance Because it is possible to integrate and analyze the browsing history of the content with the higher level concept and the content with the lower level level by the update process, the restriction on the taxonomy (ontology) used for analysis is reduced, and the taxonomy (ontology) ) Maintenance, operation, and management costs can be reduced.

Furthermore, by calculating the user's evaluation score for content using the user interest score, it is possible to recommend content that matches the user's interest.
Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. Further, various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, you may combine suitably the component covering different embodiment.

  DESCRIPTION OF SYMBOLS 100 ... Interest analysis apparatus, 200 ... Client terminal, 300 ... Content server, 110 ... History information receiving part, 120 ... Feature score calculation part, 130 ... Concept system update processing part, 140 ... Concept system / user interest score database, 150 ... Presented content list receiving unit, 160 ... content database, 170 ... content evaluation processing unit, 180 ... sorted content score list transmitting unit, 210 ... history information transmitting unit, 220 ... history collecting unit, 230 ... content presenting unit, 240 ... content Request transmission unit, 310 ... content transmission processing unit, 320 ... sorted presentation content list reception unit, 330 ... presentation content list transmission unit, 340 ... presentation content list input unit, 350 ... history information transfer unit, 360 ... content request transfer unit .

Claims (8)

A method of analyzing a user's interest using a concept system in which user interest scores for a plurality of concepts are systematized by a computer,
Clustering a first content list browsing a plurality of contents as a list and a second content list browsing a content body from the first content list;
For each cluster, the total number of contents in the first content list is the first total number, the number of contents in which the concept appears in the first content list is the first appearance number, and the second content When the total number of contents in the list is the second total number and the number of contents in which the concept appears in the second content list is the second appearance number, the first total number, the first appearance number, And under the condition of the second total number, a first probability that the number of contents in which the concept appears in the second content list is greater than or equal to the second occurrence number and less than or equal to the second occurrence number. Calculating a second probability, and calculating a feature score by an inverse function of a cumulative distribution function of a standard normal distribution based on the first probability and the second probability;
And an updating step of updating the user interest score for the concept using a feature score calculated for each cluster.   In the updating step, the feature score is weighted according to at least one of a user operation history related to the content of the second content list, a browsing time and a browsing status of the content, and a degree of association between the content and the concept. The interest analysis method according to claim 1, further comprising:   The interest analysis method according to claim 1, wherein, in the calculating step, the first probability and the second probability are obtained by a hypergeometric distribution.   When the concept system has information related to the superordinate concept and subordinate concept between the concepts, the superordinate concept of the appearing concept that appears in the contents of the first content list and the second content list is extracted from the concept system. The interest analysis method according to claim 1, further comprising: updating a user interest score for the superordinate concept on the assumption that the superordinate concept appears in the content.   In the updating step, the subordinate concept of the appearance concept and the subordinate concept of the superordinate concept are extracted from the concept system, and the user interest score of the subordinate concept is extracted using the user interest score of the appearance concept and the user interest score of the superordinate concept. The interest analysis method according to claim 4, further comprising updating the score.   The content further includes an evaluation step of calculating a user evaluation score for the content using the user interest score of each concept that appears in the content for the content in which one or more concepts appear. The interest analysis method described.   The interest analysis method according to claim 6, wherein, in the evaluation step, the user interest score is weighted according to at least one of a degree of association between the content and each concept and an importance level of each concept. An apparatus for analyzing user interests using a data concept system that systematizes user interest scores for a plurality of concepts,
Means for clustering a first content list in which a plurality of contents are browsed as a list and a second content list in which content bodies are browsed from the first content list;
For each cluster, the total number of contents in the first content list is the first total number, the number of contents in which the concept appears in the first content list is the first appearance number, and the second content When the total number of contents in the list is the second total number and the number of contents in which the concept appears in the second content list is the second appearance number, the first total number, the first appearance number, And under the condition of the second total number, a first probability that the number of contents in which the concept appears in the second content list is greater than or equal to the second occurrence number and less than or equal to the second occurrence number. Calculating means for calculating a feature score by an inverse function of a cumulative distribution function of a standard normal distribution based on the first probability and the second probability;
An interest analysis apparatus comprising: update means for updating the user interest score for the concept using a feature score calculated for each cluster.

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