JP5667959B2 - Impact analysis method, impact analysis apparatus and program thereof - Google Patents

Description

  The present invention relates to an influence analysis method, an influence analysis apparatus, and a program for analyzing an influence from another user from a content browsing history or the like.

  In recent years, there has been a demand for a technique for recommending appropriate services and contents in accordance with user actions and situations. For this reason, a technique for estimating a user's hobbies from history information has been proposed, such as estimating a user's interest from a book information browsing history in the site and recommending a book.

  In such an interest estimation method, assuming that meta information for summarizing the contents is given to each content, the method for estimating the user's interest from the frequency of concepts that appear in the user's operation history is Research is progressing especially as a memory-based technique in content filtering (CBF). Specifically, the content-based filtering technology 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), for example. 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.

  On the other hand, when a service for recommending information to a user is to be realized, an information providing service based on the user's actual social life and selection behavior is required. In addition to being influenced by others while the user is engaged in social life (Non-Patent Document 2), and as an opportunity for selection behavior, in addition to the user's own interests, influence from others is involved. (Non-Patent Document 3) is widely known.

"Proposal of multifaceted preference grasp method using operation history", IEICE Technical Report, vol. 110, no. 450, LOIS2010-76, pp. 67-72, March 2011 Ryuji Inoue, Fumiyo Yamashita, “Illustrative Trivia Social Psychology”, Natsume Inc., April 10, 2010, pp. 12-13 Written by Sina Eiengar, translated by Yuko Sakurai, “Science of Choice”, Bunju Shunju, November 15, 2010, pp. 138-139

As described above, in order to realize an information recommendation service close to a user's actual social life and selection behavior, it is necessary to accurately estimate the influence of the user from other users.
The present invention has been made paying attention to the above circumstances, and the object of the present invention is an influence analysis method and an influence degree which can estimate the influence degree of the user from the other when browsing the content with high accuracy. It is to provide an analyzer and a program thereof.

In order to achieve the above object, the present invention takes the following measures.
A first aspect of the present invention is a method, apparatus, and program for analyzing the degree of influence on a user by using a concept system that systematizes the degree of influence score that the user receives from other users for a plurality of concepts. Then, the first content list in which the user browses a plurality of contents as a list and the second content list in which the content body is browsed from the first content list are clustered, and for each cluster, the first content list The total number of contents in one content list is defined as a first total number, the number of appearances of content to which recommendation information is given by the other user in the first content list is defined as a first appearance number, and the second content The total number of contents in the list is set as the second total number, and the recommended information in the second content list is displayed. When the number of appearances of content to which the number is given is defined as the second appearance number, the second content list includes the first total number, the first appearance number, and the second total number. Calculating a first probability that the number of appearances of the content to which the recommendation information is given is equal to or greater than the second number of occurrences and a second probability that the number of appearances is equal to or less than the second number of occurrences; A feature score is calculated by an inverse function of a cumulative distribution function of a standard normal distribution based on any one of the second probabilities, and the influence score for the concept is calculated using the feature score calculated for each cluster. Update.

  According to the first aspect, the influence analysis method and the influence that can accurately estimate the influence from another user (individual or group) on the user using the scarcity of appearance of the content to which the recommended information is given A degree analyzer can be provided.

According to a second aspect of the present invention, in the calculation step of the first aspect, the number of contents in which the recommendation information is given in the first content list and a predetermined concept appears is the first appearance. The number of contents in which the recommendation information is given in the second content list and the predetermined concept appears is the second appearance number.
According to the second aspect, in the estimation of the degree of influence from others, it is possible to estimate the degree of influence for each concept or each genre to which a plurality of concepts belong.

According to a third aspect of the present invention, in the calculation step of the first aspect, the number of appearances of the content to which the recommendation information is added by the predetermined set to which the other user belongs in the first content list is calculated. The number of appearances is the first appearance number, and the number of appearances of the content to which the recommendation information is given by the predetermined set in the second content list is the second appearance number.
According to the third aspect, in the content browsing history analysis using the human system, it is possible to analyze not only the individual who gave the recommended information to the content but also the organization and group attributes reasonably and accurately from a statistical viewpoint. It becomes. Even if it is difficult to secure a sufficient amount of history to perform an impact analysis because there is little recommended information given by a specific individual, the impact level can be set for a given set unit to which multiple users belong. By analyzing it, a sufficient history can be secured.

According to a fourth aspect of the present invention, in any one of the first to third aspects, the total number of contents in the first content list is the first total number and the first content list for each cluster. The number of contents in which the concept appears is the first number of appearances, the total number of contents in the second content list is the second total number, and the number of contents in which the concept appears in the second content list is the first number. When the number of appearances is 2, the number of contents in which the concept appears in the second content list under the conditions of the first total number, the first number of appearances, and the second total number is second appearance number or more to become the first probability and to calculate a second probability of the second number of occurrences below, based on either one of the first probability and the second probability Standard normal distribution A feature score is calculated by an inverse function of a distribution function, the user interest score for the concept is updated using the feature score calculated for each cluster, and for the content in which the concept appears, for each concept that appears in the content An evaluation score for the content is further calculated using the user interest score and the influence score.

  According to the fourth aspect, the user's interest and the degree of influence from other users on the user are estimated, these estimation results are merged, information is personalized based on the merged estimation results, and information is sent to the user. Can be presented. The user's interest score and the influence score representing the influence that the user receives from other users are scored probabilities and can be handled in the same dimension. Therefore, in order to recommend new content to the user, a score update formula for an evaluation score for the information based on each concept given to the content to be presented and recommendation information given by other users Calculate using. It is possible to sort the content to be presented to the user using the evaluation score calculated for each content.

  That is, according to the present invention, an influence analysis method that enables an information recommendation service that is close to the actual social life and selection behavior for the user by considering the user's interests and the influence of other users on the user, It is possible to provide an influence analysis apparatus and a program thereof.

1 is an overall configuration diagram of an information presentation system using an influence analysis apparatus according to the present invention. The block diagram which shows the function structure of each apparatus of the information presentation system of FIG. The figure which shows the process outline | summary of an interest analyzer. The schematic diagram for demonstrating operation | movement of the feature score calculation part of interest. The figure which shows the detail of a characteristic score calculation process. The figure which shows the detail of a concept system update process. The figure which shows the recommendation tag provided to content. The figure which shows a list browsing content list and a detailed browsing content list. The figure which shows the influence analysis process which concerns on 1st Example. The figure which shows the influence analysis process which concerns on 2nd Example. The figure which shows the influence analysis process which concerns on 3rd Example. The figure which shows the influence analysis process which concerns on 4th Example. The figure which shows a content scoring process.

Embodiments according to the present invention will be described below with reference to the drawings.
FIG. 1 is an overall configuration diagram of an information presentation system using an influence analysis apparatus according to the present invention. This system includes a client terminal 400, a content server 500, an interest analysis device 100, an influence analysis device 200, and a content scoring device 300. Between the client terminal 400 and the content server 500, between the content server 500 and the interest analysis device 100, between the content server 500 and the influence analysis device 200, and between the content server 500 and the content scoring device 300. Are connected by a communication network. The user acquires desired content from the content server 500 by browsing operation on the client terminal 400, and presents the acquired content on the screen of the client terminal 400 for browsing.

  The client terminal 400 collects a content browsing history by a user operation, and browses a list browsing content list (first content list) in which a plurality of contents are browsed as a list, and a detailed browsing content list (in which a content body is browsed from the content list ( 2nd content list) is transmitted to the content server 500. The content server 500 transfers the list browsing content list and the detailed browsing content list to the interest analysis device 100 and the impact analysis device 200 via the communication network.

The interest analysis device 100 calculates a feature score of interest 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.
The influence analysis device 200 calculates the characteristic score and the influence score of the influence on other users (individuals or groups) who added “recommended tags” to the content based on the list browsing content list and the detailed browsing content list. Then, the degree of influence from other users (individuals or groups) on the user is estimated.

  The content scoring apparatus 300 inquires of the interest analysis apparatus 100 about the user interest score, and inquires the influence analysis apparatus 200 about the influence degree score from another user (individual or group) to the user, and receives it from the content server 500. From the “presentation content list”, a list of sorted contents (sorted presented content list) in consideration of the user's interest and the influence from other users (individuals or groups) is generated and transmitted to the content server 500. To do.

FIG. 2 is a block diagram showing a functional configuration of each device of the information presentation system shown in FIG. Each processing unit in FIG. 2 is realized by, for example, a CPU (Central Processing Unit) of each device and a control program executed on a memory.
[Interest analysis device]
The interest analysis apparatus 100 includes a history information receiving unit 110, an interest feature score calculation unit 120, an interest concept system update processing unit 130, and a concept system / user interest score database 140. The concept system / user interest score database 140 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), and TotalZ (user interest score). The definition and calculation method of TotalZ will be described later.

FIG. 3 shows processing of the interest analysis device 100.
The history information receiving unit 110 receives the list browsing content list and the detailed browsing content list from the client terminal 400 via the content server 500. 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 interest feature score calculation unit 120 calculates an interest feature score (Z value to be described later) of each concept using a statistical model of concept selection using the list browsing content list and the detailed browsing content list.
FIG. 4A 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 “”.

The interest feature score calculation unit 120 calculates the feature score Z for each concept ID using the analysis parameters S, a, N, and n. FIG. 5 shows details of the feature score calculation process of interest. In FIG. 5, i is a concept identifier, and j is a cluster ID.
Here, the cluster is an identifier 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.

  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. 4B, for example, there are five or more contents including the concept of “baseball” among the ten contents browsed 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. 4B, the horizontal axis is the value of the item whose value is 0, so “0.02”.
Then, as shown in FIG. 5, the interest feature score calculation unit 120 calculates the interest feature score Z by the inverse function of the cumulative distribution function of the standard normal distribution using the calculated cumulative probabilities H1 and H2. As shown in FIG. 4C, 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, the interest concept system update processing unit 130 described later obtains a user interest score (TotalZ) for the concept of “baseball”.

  The interest feature score calculation unit 120 generates an update target concept list and outputs it to the interest 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, the user can register the content presented on the client terminal 400 as a favorite content, input an evaluation for the content, and the like. When the client terminal 400 can transmit such an operation history to the interest analysis apparatus 100, the following processing is performed.

  For example, when the content is registered as a favorite, the interest feature score calculation unit 120 increases the weight w such that w = 1.5 for all 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.

  The interest 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.

FIG. 6 shows details of processing of the interest concept system update processing unit 130. The interest concept system update processing unit 130 uses the user interest score update formula for each concept i shown in FIG. 6 for the concept that has appeared in the content (appearance concept) and the concept ID of the superordinate concept of this appearance concept. The values of the scores TotalZ _in and X _{i (n−1)} , Y _{i (n−1)} are obtained, and the column of the user ID (client terminal ID) corresponding to the cluster ID in the conceptual system / user interest score database 140 is obtained. Each value stored in the corresponding user interest score table 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).

In FIG. 6, 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 interesting 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.

  Furthermore, the interest 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 subordinate concepts, for each concept ID of the “update target concept list”, the concept system table of the concept system / user interest score database 140 is referred to, a list of concept IDs is extracted from the child concept ID list, and each child ID is further extracted. The process of extracting the child concept list with reference to the concept system table for the concept ID of the 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 score of the updated concept (the concept that appears in the content and the superordinate concept) is not updated.

[Client terminal]
2, the client terminal 400 includes a history collection unit 410, a history information transmission unit 420, a content presentation unit 430, a content request transmission unit 440, and a recommendation tag addition unit 450.
The content request transmission unit 440 makes a content presentation request to the content server 500 in accordance with a user instruction (input). Specifically, for example, content request data including a client terminal ID (or user ID) and a request time is transmitted to the content server 500. The request time may be added in the content server 500. 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.

  As described above, the history collection unit 410 collects user operation histories and creates a list browsing content list and a detailed browsing content list. The history information transmission unit 420 transmits the list browsing content list and the detailed browsing content list created by the history collection unit 410 to the content server 500.

  The recommended tag adding unit 450 adds a recommended tag to the content by the user's operation input when the user wants to recommend the content to the other person regarding the list display content list and the detailed browsing content displayed on the client terminal. Information indicating that a recommended tag has been added to the content (recommended information) is transmitted to the recommended tag input unit 570.

  As a preparation for estimating the degree of influence from other users, the user can see recommended tags from other users (individuals or groups). That is, when another user wants to recommend the browsed information to someone, a recommendation tag can be assigned, and whether or not a tag has been assigned can be browsed by another user.

  FIG. 7 shows recommended tags assigned to the contents. For example, if Mr. A adds a recommendation tag to “information 2” and “information 5” as a recommendation target for other users, “information 2” and “information” in the list browsing content list and the detailed browsing content list of AA A recommended tag (Recommended by Mr. A) is displayed at “5”.

[Content Server]
2, the content server 500 includes a content transmission processing unit 510, a sorted presentation content list reception unit 520, a presentation content list transmission unit 530, a presentation content list input unit 540, a history information transfer unit 550, and a content request transfer unit 560. The recommended tag input unit 570 and the recommended tag transmission unit 580 are provided.

The history information transfer unit 550 transfers the list browsing content list and the detailed browsing content list received from the client terminal 400 to the interest analysis device 100 via the communication network.
The presentation content list input unit 540 receives a presentation content list that lists the content to be presented to the client terminal 400 used by the user by the service operator. The presented content list transmission unit 530 transmits the input presented content list to the content scoring device 300 via the communication network.

  The presented content list includes a content ID, a concept ID / relevance list, a content body, a content registration time, and recommendation information. The content ID is a unique ID assigned by the content server 500 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 recommended information is stored in association with the content ID.

  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 520 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 sorted content score list transmission unit 310. The content transmission processing unit 510 transmits the sorted presentation content list to the client terminal 400 corresponding to the client terminal ID (or user ID).
The content request transfer unit 560 transfers content request data that is a content presentation request from the content request transmission unit 440 of the client terminal 400 to the content evaluation processing unit 320.

[Effect analysis device]
The influence analysis device 200 includes a history information receiving unit 210, a human relation database 220, an influence characteristic score calculation unit 230, an influence degree conceptual system update processing unit 240 for each person / group, and each person / group. There is provided an influence concept system database 250 having an influence degree score for.

  The history information receiving unit 210 acquires a list browsing content list, a detailed browsing content list, and recommended tag information assigned to each list from the history information transfer unit 550. An example of a list browsing content list and a detailed browsing content list to which recommended tags are attached is shown in FIG.

  In FIG. 8, for example, when AA browses details of 10 (= a) contents from a list of 50 (= S) contents displayed on the client terminal 400-1. think of. Here, out of the 50 contents displayed in the list, 15 contents (= N) containing the concept of “baseball” and 10 contents with “Recommended by Mr. A” (= M), of which 6 (= P) content includes “Recommended by Mr. A” and “Baseball”. Of the 10 contents browsed by the user, 4 contents (= m) with “Mr. A's recommendation”, 5 contents (= n) containing the concept of “baseball”, of which Suppose that there are four (= p) contents with “Recommended by Mr. A” and “Baseball”.

  When the user browses the information, if the browsing rate increases for the content with the recommended tag, the degree of influence from other users to whom the user's recommended tag is added is considered to be positive (+). In addition, when the browsing rate for information to which a recommendation tag is assigned decreases, the degree of influence from other users to whom the user's recommendation tag is assigned is regarded as negative (−). Moreover, when the browsing rate about the information with which the recommendation tag was provided did not change, it can be considered that there is no influence from the other user who provided the recommendation tag to a user.

Hereinafter, the influence analysis process in the example of FIG. 8 will be described according to each embodiment.
(First embodiment)
FIG. 9 is a diagram illustrating an influence analysis process according to the first embodiment. The influence characteristic score calculation unit 230 uses the above S, a, M, and m as analysis parameters to calculate an influence characteristic score Z ′ from Mr. A to Mr. AA. The characteristic score Z ′ of the influence degree can be calculated by replacing N and n with M and m in the calculation formula shown in FIG.

  In order to obtain the influence characteristic score Z ′, first, cumulative probabilities H1 and H2 are obtained. In H1, when the number of occurrences of “Recommended by Mr. A” among the S list contents is M, the detailed browsing in which “Recommended by Mr. A” appears when a detailed browsing content is randomly selected and viewed. This is the cumulative probability that the content is m or more. In addition, when the number of occurrences of “Recommended by Mr. A” out of the S list browsing contents is M, “Recommended by Mr. A” appears when a detailed browsing content is randomly selected and viewed. This is the cumulative probability that the number of detailed browsing contents to be m or less. Then, one of H1 and H2 that is less than 0.5 is adopted, and the feature score Z ′ is obtained by the inverse function of the cumulative distribution function of the standard normal distribution. When H2 is used as the cumulative probability, the sign of the return value of the inverse function of the standard normal distribution cumulative distribution function is set to be a negative characteristic score. If both H1 and H2 are 0.5 or more, the feature score is 0.

In the above case (S = 50, a = 10, M = 10, m = 4), H1 = 0.0966, H2 = 0.981, and the feature score is Z ′ = 1.3.
From the above, the characteristic score of the degree of influence on “Recommended by Mr. A” is calculated. The calculated feature score is passed to an influence concept system database 250 having an influence score for each person / group. In the influence degree concept system database 250 having an influence degree score for each person / group, an influence degree score is given to each concept constituting the same concept system as the interest estimation.

  In the case of the first embodiment, the influence score is stored in the root concept of the concept system indicating the influence degree of Mr. A in order to regard the influence degree as equivalent for each concept. When the list content list is updated and the user's browsing history is also updated, the influence feature score is calculated again, and the influence score is updated. For the update, the influence score update formula of the following formula (1) is used. In the case of the first embodiment, the concept i is a root concept.

なお、式（１）において、重みｗは、初期値ｗ＝１とし、コンテンツに関するユーザの操作履歴（お気に入りコンテンツとしての登録や、コンテンツへの評価入力等）、コンテンツの閲覧時間及び閲覧状況、並びにコンテンツとオススメタグとの関連度の少なくとも１つに応じて変化させることができる。ユーザ影響度スコアの算出に際し、閲覧時のユーザの状況や閲覧操作の特徴（お気に入り登録、長時間閲覧等）などを反映することで、ユーザの行動や状況に基づいてユーザ影響度スコアをさらに的確に求めることが可能となる。

In equation (1), the weight w is an initial value w = 1, the user's operation history regarding the content (registration as a favorite content, evaluation input to the content, etc.), content browsing time and browsing status, and It can be changed according to at least one of the relevance levels between the content and the recommended tag. In calculating the user influence score, the user influence score is more accurately based on the user's behavior and situation by reflecting the user's situation at the time of browsing and the characteristics of browsing operation (favorite registration, long-time browsing, etc.). It becomes possible to ask for.

  Also, the root concept score is given to the subordinate concepts that are descendants with the same value. After the update of the impact score for “Recommended by Mr. A” is completed, the characteristics related to the impact level are also applied in the same way as above for those who have been given a “recommended tag” other than Mr. A included in the same list browsing content. The score is calculated, and the influence score is updated using the above formula (1).

  That is, according to the first embodiment, the number of contents (M) in which each “recommended tag” appears in the list browsing content list that is a user selection candidate and the detailed browsing content list selected by the user from the list browsing content list. The number of contents (m) in which each “recommended tag” appears is calculated, and this M and m are compared and analyzed. Since the "recommended tag" that is given can utilize the characteristics of the number of occurrences of events that are relatively "not selected" as well as events that are relatively "selected", It is possible to estimate the degree of influence from a high accuracy.

  In addition, by determining H1 and H2 by hypergeometric distribution, the user is given not only the feature of “selecting” content with “recommended tag”, but also “recommended tag” by the user. It is possible to obtain the user's influence score with high accuracy by utilizing the feature that the content is relatively “not selected” compared to chance.

(Second embodiment)
In the second embodiment, a method for estimating the degree of influence for each specific concept (or specific genre) from Mr. A to Mr. AA will be described. FIG. 10 is a diagram illustrating an influence analysis process according to the second embodiment. The influence degree feature score calculation unit 230 calculates the influence degree feature score Z ′ for Mr. A using the analysis parameters S, a, P, and p. The characteristic score Z ′ of the influence degree in this case can be calculated by replacing N and n with P and p in the calculation formula shown in FIG.

In the case of the second embodiment, H1 selects a detailed browsing content at random when P is the number of content that has “S recommendation A” and “baseball” at the same time among the S browsing content S. The cumulative probability that the number of detailed browsing contents in which “Recommended by Mr. A” and “Baseball” appear at the same time will be p or more. In addition, when the number of contents where “A-san's recommendation” and “baseball” are added at the same time appears among the list browsing contents is H2, when “a” detailed browsing contents are randomly selected and viewed, This is the cumulative probability that the number of detailed browsing contents in which “Mr. A's recommendation” and “baseball” appear simultaneously will be p or less. Then, one of H1 and H2 that is less than 0.5 is adopted, and the feature score Z ′ is obtained by the inverse function of the cumulative distribution function of the standard normal distribution. When H2 is used as the cumulative probability, the sign of the return value of the inverse function of the standard normal distribution cumulative distribution function is set to be a negative characteristic score. If both H1 and H2 are 0.5 or more, the feature score is 0.
From the above, the characteristic score of the degree of influence on “Mr. A's recommendation regarding baseball” is calculated.

  In addition, for a specific genre such as “sports”, which is a superordinate concept of “baseball”, the detailed browsing content is a using the number (= q) where “Mr. A's recommendation” and “sports” appear simultaneously. When browsing randomly selected items, select the cumulative probability (H1) that the number of detailed browsing contents that “Recommended by Mr. A” and “Sports” appear simultaneously is q or more, and a detailed browsing content. The cumulative probability (H2) that the number of detailed browsing contents in which “A-san's recommendation” and “sports” appear at the same time becomes q or more is obtained. Then, in the same manner as described above, the characteristic score of the degree of influence on “Mr. A's recommendation regarding sports” is obtained.

Similarly, for “soccer”, which is a subordinate concept of “sports”, for example, using the number (= q) of “Mr. A's recommendation” and “Sports” appearing simultaneously, the impact on “Mr. A ’s recommendation about soccer” Also find the degree feature score.
After the update of the impact score for “Recommended by Mr. A” is completed, the characteristics related to the impact level are also applied in the same way as above for those who have been given a “recommended tag” other than Mr. A included in the same list browsing content. The score is calculated, and the influence score is updated using the above formula (1).

  According to the second embodiment, “recommended tag” is added to the content with “recommended tag”, and the “recommended tag” is added to the content using the concept at the same time. The degree of influence of each concept on other users can be estimated. In other words, the influence on the person is not only expressed by one score, but also the difference in the degree of influence depending on the genre (for example, Mr. AA is affected by the genre X but not the genre Y. ) Etc. can also be grasped.

(Third embodiment)
FIG. 11 is a diagram illustrating an influence analysis process according to the third embodiment. In the third example, a method for estimating the degree of influence from group F on Mr. AA will be described. In other words, when other users who have given recommendation information have group attributes and organizational systems and can create a hierarchical system for these relationships (hereinafter, the system consisting of people is referred to as a human system) The upper group attributes of other users who have been given recommended tags appearing in the contents of the browsing content list and the detailed browsing content list are extracted from the human system, and the higher group attributes are regarded as appearing in the content. It is further characterized in that the user's influence score for the attribute is updated. Note that the human system relationship information can have a graph structure when a number of other users or group attributes are related to each other. In the case of having a graph structure, the direct or indirect superior attribute of each other or group attribute is referred to as a “parent group”, and the direct or indirect superior attribute is referred to as a “child group” Considered a group).

  The human system database 220 receives a list of recommended tag granters included in the list browsing content list (in this example, “A-san”, “C-san”, “D-san”, and “E-san”) and received history information. Received from the unit 210 and stored. The history information receiving unit 210 reads the human system (group or attribute, etc.) higher than the recommended tag grantor from the human system database 220 (in this example, “group F”, “group G”, and “group H”). ). The history information receiving unit 210 adds the parent group read from the human system database 220 and passes the recommended tag information to the influence characteristic score calculating unit 230.

  The influence characteristic score calculator 230 receives the recommended tag information to which the group information is added from the history information receiver 210. The influence characteristic score calculation unit 230 is the same method as in the first embodiment, and the influence characteristic score for “Recommended by Mr. A”, “Recommended by Mr. C”, “Recommended by Mr. D”, and “Recommended by Mr. E”. Update.

  Further, an influence score for the parent group is obtained. As a specific example, a method of obtaining the influence score of “Group F” is shown. First, contents including recommended tags of individuals “Mr. A”, “Mr. B”, and “Mr. C” belonging to “Group F” (in this example, “Mr. A” and “Mr. C” are recommended) Is regarded as “Group F recommendation”, and the characteristic score of the influence degree is obtained in the same manner as described above by using the total number of contents including recommended tags of individuals belonging to “Group F”, and the above formula (1) is obtained. Use to update the impact score.

  When the number of “Group F recommendation” that appears in the list browsing content (S) is R, the detailed browsing content in which “Group F recommendation” appears when the detailed browsing content (a) is selected is r. It shall be a piece. At this time, the influence characteristic score calculator 230 calculates the influence characteristic score Z ′ for “group F” using the analysis parameters S, a, R, and r. The characteristic score Z ′ of the influence degree in this case can be calculated by replacing N and n with R and r in the calculation formula shown in FIG.

  In addition, regarding child groups that belong to the lower level of the parent group to be learned (in “Group F”, “Mr. A”, “Mr. B”, “Mr. C”), the child groups that are not included in the list browsing content list (For “Group F” in this example, “Mr. B”), the influence score is updated using the characteristic score of “Group F”.

The updated impact score is stored in the root concept of the impact concept system of each person and group in the impact concept system database 250 having the impact score for each person / group. And the score of the root concept is given to the subordinate concepts corresponding to the descendants with the same value.
(Fourth embodiment)
FIG. 12 is a diagram illustrating an influence analysis process according to the fourth embodiment. The human relation database 220 is the same as that in the third embodiment. The feature score calculation unit 230 of the influence degree uses the same method as in the second embodiment for the recommended tag received from the human relationship database 220, and affects the genre for the person (or group) to which the recommended tag is assigned. Update degree score.

[Content scoring device]
The content scoring device 300 includes a sorted content score list transmission unit 310, a content evaluation processing unit 320, a content database 330, and a presentation content list / recommended tag reception unit 340. FIG. 13 is a diagram showing content scoring processing.

  The content scoring device 300 receives the content to be presented from the presented content list transmission unit 530, scores the content, sorts the content based on the score, and sorts the sorted content into the sorted presented content list receiving unit 520. Pass data.

  Specifically, the presentation content list / recommended tag receiving unit 340 receives from the presentation content list transmission unit 530 a presentation target content, a conceptual system indicating the content of the presentation target content, and “ The “recommended tag” is received and stored in the content database 330. Next, the content evaluation processing unit 320 tags each presentation target content (for example, evaluation content (evaluation content 1 has “concept H”, “concept E”, “recommended A”), and “recommended B”). )), The following formula using the “concept H” score from the influence concept system (“Mr. A” and “Mr. B” influence concept system) from the person who gave the recommended tag of the content The evaluation impact score is obtained from (2), and the same applies to “concept E”.

そして、前述した評価用影響度スコアと評価用興味スコアを用いて、下記式（４）により評価用影響度スコアと評価用興味スコアを重ね合わせ、評価コンテンツ１の評価スコアを求める。

同様にして、すべての提示対象コンテンツについて、評価スコアを求め、評価スコアを元にコンテンツをソートする。ソート済のコンテンツを、ソート済コンテンツスコアリスト送信部３１０に渡す。ソート済コンテンツスコアリスト送信部３１０は、ソート済み提示コンテンツリスト受信部５２０にソート済コンテンツスコアリストを送信する。 Thereafter, an evaluation interest score is obtained by the following equation (3) using the scores of “concept H” and “concept E” in the concept system having the interest score.

Then, using the evaluation impact score and the evaluation interest score described above, the evaluation impact score and the evaluation interest score are superimposed by the following formula (4) to obtain the evaluation score of the evaluation content 1.

Similarly, evaluation scores are obtained for all the presentation target contents, and the contents are sorted based on the evaluation scores. The sorted content is passed to the sorted content score list transmission unit 310. The sorted content score list transmission unit 310 transmits the sorted content score list to the sorted presentation content list reception unit 520.

The sorted content list receiving unit 520 sends the sorted content to the content transmission processing unit 510 based on the sorted content list, and transmits it to the content presentation unit 430 of the client terminal 400-1.
As described above, in the above embodiment, the degree of influence from the other user when browsing the content is estimated with high accuracy by considering the user's own interest and the influence of the user on the user. can do. This makes it possible to realize an information recommendation service that is close to the actual social life and selection behavior for the user.

  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 ... Influence degree analysis apparatus, 300 ... Content scoring apparatus, 400 ... Client terminal, 500 ... Content server, 110 ... History information receiving part, 120 ... Interest characteristic score calculation part, 130 ... Interest concept System update processing unit, 140 ... Concept system / user interest score database, 150 ... Presentation content list reception unit, 160 ... Content database, 170 ... Content evaluation processing unit, 180 ... Sorted content score list transmission unit, 210 ... History information reception , 220 ... Human relation database, 230 ... Influence score characteristic score calculation part, 240 ... Concept system update processing part of influence level for each person / group, 250 ... Influence concept system having an influence score for each person / group Database, 310 ... sorted content Core list transmission unit 320 ... Content evaluation processing unit 330 ... Content database 340 ... Present content list / recommended tag reception unit 410 ... Content presentation unit 420 ... History collection unit 430 ... History information transfer unit 440 ... Content Request transmission unit, 450 ... recommended tag addition unit, 510 ... content transmission unit, 520 ... sorted content list reception unit, 530 ... history information transfer unit, 540 ... presentation content list input unit, 550 ... presentation content list transmission unit, 560 ... content request transfer unit, 570 ... recommended tag input unit, 580 ... recommended tag transmission unit.

Claims (6)

A method of analyzing the degree of influence on a user by using a concept system that systematizes an influence score that a user receives from another user for a plurality of concepts,
Clustering a first content list in which the user has browsed a plurality of contents as a list and a second content list in which a content body is browsed from the first content list;
For each cluster, the total number of contents in the first content list is the first total number, and the number of appearances of content to which recommendation information is given by the other user in the first content list is the first number of appearances. When the total number of contents in the second content list is the second total number and the number of appearances of the content to which the recommendation information is added in the second content list is the second appearance number, Under the conditions of the total number of 1, the first number of appearances, and the second total number, the number of appearances of the content to which the recommendation information is added to the second content list is equal to or greater than the second number of appearances. comprising first probability and to calculate a second probability of the second number of occurrences below, the original standard normal distribution either one of the first probability and the second probability A calculation step for calculating a feature score by inverse function of the product distribution function,
And an update step of updating the influence score for the concept using a feature score calculated for each cluster.   In the calculating step, the recommendation information is given in the first content list and the number of contents in which a predetermined concept appears is the first appearance number, and the recommendation information is given in the second content list. The influence analysis method according to claim 1, wherein the number of contents in which the predetermined concept appears is set as the second appearance number.   In the calculating step, the number of appearances of the content to which the recommendation information is given by a predetermined set to which the other user belongs in the first content list is defined as the first appearance number, and the number of appearances in the second content list The impact analysis method according to claim 1, wherein the number of appearances of content to which the recommendation information is given by a predetermined set is set as the second appearance number. 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. and step second calculating a probability, for calculating a feature score by inverse function of the cumulative distribution function of the original standard normal distribution either one of said first probability and said second probability that,
Updating the user interest score for the concept using a feature score calculated for each cluster;
The content in which the concept appears further includes an evaluation step of calculating an evaluation score for the content using the user interest score and the influence score of each concept that appears in the content. 4. The influence analysis method according to any one of 1 to 3. An apparatus for analyzing the degree of influence on a user using a concept system that systematizes an influence score that a user receives from other users for a plurality of concepts,
Means for clustering a first content list in which the user has browsed a plurality of contents as a list and a second content list in which a content body is browsed from the first content list;
For each cluster, the total number of contents in the first content list is the first total number, and the number of contents in which the concept to which recommendation information is given by the other user appears in the first content list is the first. The total number of contents in the second content list is the second total number, and the number of contents in which the concept to which the recommendation information is added in the second content list appears is the second number of appearances When the first total number, the first number of appearances, and the second total number are satisfied, the number of appearances of the content to which the recommendation information is added to the second content list is the first number. calculating a first probability and a second probability of the second number of occurrences below the second occurrence number or more, either one of the first probability and the second probability A calculating means for calculating a feature score by inverse function of the cumulative distribution function of the standard normal distribution bets,
An influence analyzing apparatus, comprising: an updating unit that updates the influence score for the concept using a feature score calculated for each cluster.   6. An influence analysis program for causing a computer to function as each means constituting the influence analysis apparatus according to claim 5.

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