WO2004114155A1 - Dispositif, procede et programme de recommandation de contenu - Google Patents

Dispositif, procede et programme de recommandation de contenu Download PDF

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Publication number
WO2004114155A1
WO2004114155A1 PCT/JP2003/007860 JP0307860W WO2004114155A1 WO 2004114155 A1 WO2004114155 A1 WO 2004114155A1 JP 0307860 W JP0307860 W JP 0307860W WO 2004114155 A1 WO2004114155 A1 WO 2004114155A1
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WO
WIPO (PCT)
Prior art keywords
content
user
content recommendation
score
recommendation
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Application number
PCT/JP2003/007860
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English (en)
Japanese (ja)
Inventor
Seishi Okamoto
Hiroya Inakoshi
Akira Sato
Takahisa Ando
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Fujitsu Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Fujitsu Limited filed Critical Fujitsu Limited
Priority to PCT/JP2003/007860 priority Critical patent/WO2004114155A1/fr
Publication of WO2004114155A1 publication Critical patent/WO2004114155A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9536—Search customisation based on social or collaborative filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation

Definitions

  • the present invention relates to a content recommendation device, method, and program for recommending useful content to a user, and more particularly to a content recommendation device, method, and program using profile data and user case data, which characterize the content.
  • a shopping site on the Internet provides a service that recommends useful content to customers who have a history of purchasing content based on their purchase history. This service saves customers from having to search for the content that suits them from a vast amount of content, and can lead to sales expansion for shopping site operators.
  • a profile data is created by characterizing the user and the content, and a content recommendation is requested based on the created profile data.
  • a method of searching for a user similar to the target user and recommending content that has been frequently browsed or purchased by the similar user and content highly evaluated by the similar user has been adopted (Patent Document 1).
  • this content recommendation method users who browse or purchase the same content as the target user or users who perform similar evaluations are set as similar users, and similar users often browse. It is possible to recommend useful content for the target user by recommending content that has been highly evaluated by similar users who are often purchased.
  • the content having a high content evaluation with a high number of browsing and purchase times may be recommended to the target user, and the target user
  • the problem may be that useful content is not always recommended to the public.
  • This near point Tsu is to be recommended to the target user, which is determined by whether it is useful for similar users occurs because the usefulness to the target user is not reflected directly (e.g., similar user to the target user If 10 people are selected and 9 of them have high reputation and one of them has high reputation, nine similar users give high reputation to the target user.
  • the latter content in which one similar user gives a high rating, may be useful.
  • the present invention provides a content recommendation device, a method, and a method for recommending content that is truly useful to a target user based on the similarity with the target user's case, using content that has been selected by a similar user as a content recommendation candidate.
  • the purpose is to provide the program. Disclosure of the invention
  • the present invention is based on a user database in which characteristics of a plurality of users are recorded and a profile in which characteristics of a target user are recorded from an example database in which examples of contents selected by the users are recorded.
  • a computer that recommends the best content for the target user
  • a similar user search unit that searches for a similar user similar to the profile from the user database, a content recommendation candidate determination unit that sets content selected by the similar user in the past as a content recommendation candidate from the case database,
  • a search unit for searching for a content previously selected by a specific user recorded in a case database; a score calculation unit for calculating each similarity between a content recommendation candidate and a previously selected content; and a target user based on each similarity.
  • a recommended content determining unit that determines content to be recommended.
  • a content recommendation candidate determined based on a selection history example such as past browsing or purchase of a similar user similar to the target user, and a history of past selections of the target user. Since the recommended content is determined by calculating the similarity from the case, if the content is highly similar to the selection history of the target user, even if there is only one similar user who selected the content, The content can be recommended.
  • the content recommendation candidate determination unit is characterized in that the content selected by the target user in the past is not a recommendation candidate. Also, the content recommendation candidate determination unit deletes the content that the target user has already selected from the determined content recommendation candidates. This prevents the content already selected by the target user from being unnecessarily recommended.
  • the recommended content determination unit recommends a predetermined number of profile contents in the descending order of the score from the content recommendation candidates.
  • the content that the target user has frequently viewed or purchased in the past will be recommended as useful content for the user.
  • the recommended content determination unit may recommend the content of a predetermined number of profiles in descending order of the score and the content of a constant number of profiles in descending order of the score from the content recommendation candidates.
  • the case database stores a case in which a specific user previously added the content selected by the user to the evaluation value given by the target user, and the score calculation unit sets the content recommendation candidate similar to the content having a high evaluation value to:
  • the score for each content recommendation candidate is calculated using the evaluation value such that the score of the content recommendation candidate similar to the case content having a high similarity and a low evaluation value is low.
  • the score calculation unit calculates, for each content recommendation candidate, the total sum of similarities with a plurality of case contents as a score.
  • the score calculation unit calculates, for each content recommendation candidate, an average of the similarities with a plurality of case contents as a score.
  • the score calculation unit calculates the similarity to the case content for each content recommendation candidate, and obtains the maximum value or the minimum value among the calculated similarities as the score.
  • the present invention provides a content recommendation method. That is, the present invention provides a user database that records the characteristics of each of a plurality of users, and a case database that records cases of contents selected by the users, based on a profile that records the characteristics of the target user.
  • a content recommendation method for recommending an optimal content for the target user
  • the content selected by a similar user in the past A content recommendation candidate determining step in which the content recommendation candidate is used as a content recommendation candidate; a search step for searching for the content selected by the specific user in the past recorded in the case database;
  • the present invention provides a program for content recommendation.
  • This program is based on a user data base that records the characteristics of each of a plurality of users, and a profile that records the characteristics of the target user from a case database that records cases of the content selected by the user.
  • the content recommendation device computer that recommends the optimal content for the target user,
  • Figure 1 is an explanatory diagram of the network environment to which the present invention is applied;
  • FIG. 2 is a block diagram of a functional configuration of the content recommendation device according to the present invention
  • FIG. 3 is an explanatory diagram of user profile data
  • Figure 4 is an illustration of profile data characterized by content
  • Figure 5 is an illustration of purchase and listing example data
  • Fig. 6 is an explanatory diagram of the processing content by the content candidate determining unit of Fig. 2
  • Fig. 7 is an explanatory diagram of the processing content of the search unit, score calculation unit, and recommended content determining unit of Fig. 2;
  • FIG. 8 is a flowchart of the content recommendation processing of the present invention using the functional configuration of FIG. 2;
  • Figure 9 is an explanatory diagram of the evaluation matrix given by the user to the content
  • Figure 10 is an explanatory diagram of the evaluation matrix given by the similar user to the target user
  • Figure 11 is an explanatory diagram of the evaluation matrix given by the target user
  • Figure 12 is an explanatory diagram of the profile list corresponding to the content to which the target user has given an evaluation
  • Figure 13 is an illustration of the profile list corresponding to the top two content recommendation candidates for similar users of Figure 10;
  • Fig. 14 is an explanatory diagram of importance, which is a profile attribute value calculated based on the content to which the target user has given an evaluation in the past; BEST MODE FOR CARRYING OUT THE INVENTION
  • FIG. 1 is an explanatory diagram of a network environment to which content recommendation processing according to the present invention is applied.
  • the content recommendation processing of the present invention is executed by the server 10.
  • the user devices 14-1 and 14-2 are connected via the evening network 12.
  • the user devices 14-1 and 14-2 are equipped with WWW browsers, and perform content recommendation requests by specifying user information by accessing the server 10 via the Internet 12.
  • the user database 16, the case database 18, and the profile database 20 are connected to the server 10. These databases may be data files such as XML files.
  • the server 10 includes a CPU 22, a memory 24 and a cache 26 as a hardware configuration.
  • the memory 24 is loaded with a program for executing the content recommendation process of the present invention, and the CPU 22 executes the program to execute the content recommendation process.
  • FIG. 2 is a block diagram of a functional configuration of a content recommendation process according to the present invention executed by the server 10 of FIG. 2 and FIG. 1.
  • a server 10 functioning as a content recommendation processing device includes a user information input unit 28, a content recommendation candidate determination unit 30, a search unit 32, a score calculation unit 34, a recommended content determination unit 36, An output section 38 is provided.
  • a user database 16, a case database 18, and a profile database 20 are provided for the server 10.
  • the user database 16 stores, for example, user data that characterizes users who use the content recommendation processing of the present invention as registered users.
  • the case database 18 stores the history of the user browsing or purchasing the content as case data.
  • the profile database 20 stores a profile database that characterizes the content of a web page, a product, and the like as a set of pairs of attributes and attribute values or a set of keywords.
  • the user information input unit 28 inputs information of a target user who is a content recommendation target in response to a user recommendation request input regarding content transmitted from the user devices 141-1 and 14-12 in FIG. .
  • Recommended content The supplementary decision unit 30 searches the case database 18 for contents previously browsed or purchased by a similar user similar to the target user input in the user information input unit 28, and based on the search result, the target user is searched. Determine content recommendation candidates. In this case, a similar user similar to the target user is searched using the user database 16.
  • the search unit 32 searches the case database 18 for the case contents browsed or purchased by the target user in the past, and searches the profile corresponding to the searched case contents from the profile file base 20.
  • the score calculation unit 34 calculates the similarity of the target content searched by the search unit 32 to the profile of the example content for each content recommendation candidate profile determined by the content recommendation candidate determination unit 30. Is calculated.
  • the recommended content determination unit 36 determines the content to be recommended to the target user based on the score for each content recommendation candidate calculated by the score calculation unit 34. In determining the recommended content in this case, basically, the specified number of contents are determined and recommended in descending order of the score, but in addition to this, the specified number of contents are determined in descending order of the score and recommended to the target user.
  • the output unit 38 has input the recommendation request for the content determined by the recommended content determination unit 36. ⁇ The output unit 38 transfers the content to the user device.
  • FIG. 3 is an explanatory diagram of the user profile data 40 stored in the user database 16 of FIG.
  • the user profile data 40 is composed of a set of attribute-attribute-value pairs for the user following the user ID.
  • the attributes are age, gender, There are occupations, marriages, number of children, and addresses, and user-specific attribute values are stored for each.
  • FIG. 4 is an explanatory diagram of profile data stored in the profile database 20 of FIG.
  • the profile data used in the present invention includes the profile data 42 shown in FIG. 4 (A) and the profile data 44 shown in FIG. 4 (B).
  • the profile data 42 in Fig. 4 (A) characterizes the content of a web page or product as a set of attribute-attribute-value pairs.
  • the profile data 40 has a book name, an author name, and a publisher as attributes following the content ID, and stores attribute values corresponding to the attributes. Therefore, this profile data 42 is profile data that characterizes a book as content.
  • the profile data 44 in Fig. 4 (B) characterizes contents such as web pages and products as a set of keywords.
  • the case where the profile data 42 that characterizes the content by the set of the attribute-attribute-value pairs in Fig. 4 (A) is stored in the profile database 20 is an example. To explain.
  • FIG. 5 is an explanatory diagram of case data stored in the case database 18 of FIG. FIG. 5 (A) shows basic case data 46, in which the relation type, date / time, user ID, and content ID are set as attributes, and the corresponding attribute values are stored.
  • the relationship type stores the user's action on the content, for example, “buy” or “browse”. In this example, “buy” is registered.
  • the date and time of the case data 46 can be used when performing a search by deciding the designated period in case search.
  • the user ID can be used as a key for case search by similar users and case search by target users.
  • the content ID can be used when searching for the corresponding profile data 42 shown in FIG. 4 (A).
  • FIG. 5 (B) shows other case data 48 stored in the case database 18.
  • the case data of the target user is evaluated based on the evaluation value. Can be narrowed down to specific case data. Also, this evaluation value can be used for calculating the similarity in the score calculation.
  • FIG. 6 is an explanatory diagram of the processing performed by the content recommendation candidate determination unit 30 of FIG.
  • the content recommendation candidate determination unit 30 searches the user database 16 by a similar user search 52 using the recommendation target user 50 obtained by the recommendation request input, Search for similar users 5 4— 1 to 5 4—n.
  • the similar user case search 5 6 is used to search the case database 18 using the user ID of the similar user 5 4—1 to 5 4—n as a search key.
  • Similar user case data 5 8—1 to 5 8 Search for 11.
  • content candidate determination 60 content recommendation candidates 60-0 to 60-0-n are determined from similar user case data 58-1-1 to 58-n retrieved from the case database 18. I do.
  • the following methods are used for the content candidate determination 60
  • the higher specified number is designated as content recommendation candidates 60-1 to 60-n.
  • the higher specified number is designated as content recommendation candidates 60-1 to 60-n.
  • a recommendation candidate it is not necessary to recommend the case content of the target user who made the recommendation request because the target user has already purchased or browsed the content, so the case content of the target user is deleted. I do.
  • a profile search 62 2 of the content recommendation candidates is performed, and the profile data 20 4 — 1 to 6 4 — corresponding to the content recommendation candidates is obtained from the profile database 20. Search for n.
  • FIG. 7 is an explanatory diagram of the processing content of the search unit 32 in FIG. 2 and also briefly shows the processing of the score calculation unit 34 and the recommended content determination unit 36.
  • the search unit 32 uses the user ID obtained from the recommendation target user database 50 who made the recommendation request input, and performs a case search 66 of the target user from the case database 18 through the case search 18 of the target user.
  • the score calculation unit 34 for each of the profile data 6 4 — 1 to 6 4 — n corresponding to the content recommendation candidates obtained by the content recommendation candidate determination unit 30 of FIG.
  • d (x i, y i) is calculated using the distance of X and y to the profile feature a i.
  • All profile data 7 2 1 1 to 7 2 _ n of the example content obtained by the search unit 3 2 in Fig. 7 for each of the profile data 6 4 1 to 6 4 n Degrees are calculated, and the sum is used as the score of each content recommendation candidate. That is, the score of the content recommendation candidate is calculated by the following equation.
  • the score of the content recommendation candidate is calculated not by the sum of the similarities according to the formula (2) but by the average of the similarities or the highest or lowest value among the similarities. It may be a score.
  • the profile data 72-1 to 72-n of the case contents searched by the search unit 32 in Fig. 7 are given evaluation information from the target user as shown in the case data 48 in Fig. 5 (B).
  • a score calculation is performed such that the similarity increases for content with high evaluation and the score decreases for content with low evaluation. For example, using the evaluation value, Calculate the score of the recommendation candidate.
  • a score calculation based on the weight of the profile feature value may be performed.
  • the weights W 1, W 2, to Wn of each profile feature value are obtained from the frequency of profile feature values and statistical information in the set of case contents searched by the search unit 32.
  • the sum of the feature value W of the profile of the content recommendation candidate obtained by the content recommendation candidate determination unit 30 is calculated as a score.
  • the score calculation 74 in the score calculation section 34 includes a case where the evaluation data of the target user is stored in the profile data 72-1-1 to 72-n of the case content searched by the search section 32. In the descending order of the evaluation value, the score for each content recommendation candidate is calculated using only the profile data of the specified number of case contents or the open file data of the case content whose evaluation value is equal to or higher than the specified threshold. You may make it calculate.
  • the recommended content determination unit 36 determines the content recommended to the target user based on the content recommendation candidate score calculated by the score calculation unit 34 by the content recommendation 76.
  • the determination method in the content recommendation 76 can be, for example, any of the following.
  • FIG. 8 is a flowchart of the content recommendation processing of the present invention having the functional configuration of FIG. 2, and this processing procedure simultaneously shows the contents of a program for the content recommendation processing executed by the server 10.
  • the procedure of the content recommendation process of the present invention is as follows. Step S1: Input information of the recommendation target user who has made the content recommendation request.
  • Step S2 Search the user database 16 for a user similar to the target user.
  • Step S3 Search case data of similar users from the case database 18 and determine a content recommendation candidate based on this. At the same time, a profile database corresponding to the content recommendation candidate is searched from the profile database 20 .
  • Step S4 Delete the content corresponding to the target user's case in the content recommendation candidates.
  • Step S5 Search case data of the target user from the case database 18 to determine case contents.
  • Step S6 Search the profile of the case content from the profile database 20.
  • Step S7 Calculate the total sum of similarities between each example content profile and the content recommendation candidate profile as a score.
  • Step S8 Based on the score, determine the content to be recommended from the content recommendation candidates, and respond to the target user who made the recommendation request.
  • FIG. 9 is an explanatory diagram of the evaluation matrix 78 given by the user to the content.
  • This evaluation matrix 78 is created from a case database in which three users A, B, and C store evaluations previously given to each of the contents CI, C2, C3, and C4.
  • the content evaluation in the evaluation matrix 78 is given on a five-point scale from 1 to 5, where 1 is the lowest rating and 5 is the highest rating.
  • user A gives content C 1 the lowest rating, content C 2 and 3 the highest rating, and content C 4 the bad rating.
  • the similarity between user A and user B is determined by the Pearson correlation function (Communications of ACM, Vol. 40, No. 3, 197 7) The following equation is calculated using the correlation function of.
  • V (A) and V (B) are the standard deviations of the evaluations of users A and B, respectively
  • G ov (A, B) is the covariance of the evaluations of users A and B
  • the similarity between user A and user C is calculated by the following equation.
  • Pearson's correlation function takes a value of 1 or more and 1 or less, and user A and user B close to 1 are very similar, and user A close to 1 1 And User C are not quite similar. Therefore, the content recommendation processing according to the present invention based on such a Pearson correlation function will be specifically described as follows.
  • Similar users are obtained for the target users who receive the content recommendation request, and the content having a high evaluation from the similar users is obtained.
  • This method of selecting a content highly evaluated by similar users as a content recommendation candidate can be referred to as preprocessing of the content recommendation process of the present invention, which is the same content as the conventional content recommendation method.
  • FIG. 10 is an evaluation matrix 80 showing the evaluations given by the users to the contents of users D, E, and F selected as similar users to the target user Z who receives the content recommendation.
  • the similar users D, E, F, and the contents C11, C12, C13, and C14 are given a five-level evaluation from 1 to 5.
  • the contents C 11 and the contents C 12 in the evaluation matrix 80 are highly evaluated by all similar users, so that the recommendation to the target user Z is made. Will be done.
  • the content recommendation candidate determination unit 30 in FIG. 6 has determined that the content C 11 and the content C 12 have been determined as content recommendation candidates. I do.
  • FIG. 11 shows the evaluation matrix 82 of the target user Z who receives the recommendation of the content, and shows the past evaluation of the content C 21, C 22 C 23, and C 24 of the target user Z. I have.
  • FIG. 12 shows a profile list 84 corresponding to the content for which the target user Z has been evaluated, as in the evaluation matrix 82 of FIG.
  • the profile list 84 stores contents C 21, C 22, C 23, and C 24 as attributes, and collectively stores the attribute values. That is, in the profile list 84, each (content, genre) pair constitutes profile data for each content. From the evaluation matrix 82 of FIG. 11 and the profile list 84 of the content of FIG. 12, it can be seen that the target user Z has a high evaluation of the content of the genre “mystery”.
  • the profiles of the content recommendation candidates C 11 and C 12 determined by the evaluation matrix 80 of FIG. 10 are the profile list 86 of FIG. 13, the similar user of FIG. Assuming that two highly-reputed contents C ll and C 12 are recommended to the target user Z, in this content recommendation, the social science content C 1, which is a genre with a low reputation in the target user Z, is used.
  • the past evaluation of the target user Z for the content recommendation candidates C 11 and C 12 evaluated by the similar users obtained from FIG. By determining the importance of the attribute values of the profile of the case content profile, and re-evaluating using this importance, it is possible to make the target user Z truly among the content recommendation candidates that have been highly evaluated by similar users. Tetsuyu Of recommended content is realized.
  • Figure 14 shows the importance list 88 that indicates the importance of each profile attribute value calculated based on the content that the target user Z has evaluated in the past.
  • the importance takes a value of 1 or less.
  • the scores of the content recommendation candidates C11 and C12 based on the evaluation of the similar users in FIG. 10 are recalculated.
  • the score of content C 11 is 0.1 because the genre is social science, and the score is 1.0 because content C 12 is a mystery.
  • the content C 12 with a high score is determined as the content to be recommended to the target user Z by recalculating the score.
  • the target user Z can recommend the attribute value of interest, in this example, the content whose genre is mystery to the target user, and the similar user evaluates it but the target user Z is interested.
  • the present invention has been described with reference to the corresponding drawings, the present invention is not limited to these embodiments, and various other modifications may be made without departing from the scope or spirit of the present invention. And changes are possible.
  • the content recommendation at the product sales site has been described.
  • the content recommendation for lending a product may be considered.
  • it may be used for content recommendation for distribution of moving images.
  • it may be a content recommendation for product introduction.
  • the present invention is not limited by the numerical values shown in the above embodiments. Industrial applicability
  • content recommendation is received.
  • the content recommendation candidate recommended based on the past browsing and purchases of other users similar to the target user to be viewed or the evaluation given to the user's content, and the past browsing and purchase of the target user, and Uses a history example such as an evaluation given by the user to calculate the cost for each content recommendation candidate, and re-evaluates the content to be recommended, so that the content is more useful for the target user. Can be recommended.

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Abstract

Selon l'invention, une section d'entrée reçoit des informations relatives à un utilisateur objet auquel un contenu est recommandé. Une section de détermination de recommandations de contenus potentielles recherche des contenus sélectionnés préalablement par des utilisateurs similaires à l'utilisateur objet, et détermine les recommandations de contenus potentielles faites à l'utilisateur objet. Une section de recherche réalise une recherche dans une base de données de cas afin de trouver les contenus sélectionnés préalablement par l'utilisateur objet. Une section de calcul de score calcule le score représentant la similarité de chaque contenu des recommandations de contenus potentielles avec chaque contenu sélectionné par l'utilisateur objet. Une section de détermination de contenu à recommandation détermine le contenu à recommander à l'utilisateur objet en fonction des scores des recommandations de contenus potentielles.
PCT/JP2003/007860 2003-06-20 2003-06-20 Dispositif, procede et programme de recommandation de contenu WO2004114155A1 (fr)

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