JP2005157843A - Content search program, its recording medium and content search method and device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide content information to match a variety of search requests from a user by using a content search device that is used in searching for contents useful for the user in response to the user's request from a client device and in sending information about the contents found to the client device. <P>SOLUTION: A profile storage part 110 stores the profiles of a user and contents and the relationship between the user and the contents. Based on the user profile and the relationship stored in the profile storage part 110, a content recommending part 130 recommends contents highly evaluated by users similar to the user who requested the search for the content. A similar content search part 140 uses content profiles to create a list of contents similar to the contents recommended by the content recommending part 130. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention searches a content useful for a user in response to a user request from a client device, and transmits a content search program to the client device. In particular, the present invention relates to a content search program and a recording medium thereof, a content search method, and a content search device that can respond to various content search requests of a user.

  Conventionally, various content search systems have been provided for the purpose of effectively using content accumulated in each department in a company. The content search system is usually provided with a personalization function for the purpose of providing different content services for each user.

  Here, the personalization function is to narrow down information provided for each user by filtering from all information managed by the content search system. As a filtering method, for example, a method based on information content (Content-based Filtering), a method based on user evaluation (Collaborative Filtering), or the like is known (see, for example, Patent Document 1).

JP 2002-215665 A

  However, the conventional simple collaborative filtering method has a problem that only content highly evaluated by similar users is recommended. That is, there is a problem that the method of recommending content is limited.

  For example, there is a problem that content that is similar to content that is highly evaluated by similar users but is not subject to evaluation is not recommended, and there are cases where it is not possible to respond to various search requests of users.

  The present invention has been made to solve the above-described problems caused by the prior art, and a content search program, a recording medium thereof, a content search method, and a content search device capable of responding to various content search requests of a user. The purpose is to provide.

  In order to solve the above-described problems and achieve the object, the present invention searches content useful for a user in response to a user request from the client device, and transmits information related to the searched content to the client device. A content search program for use in a content recommendation procedure for recommending content based on an evaluation of a user similar to the user, and a similar content search for searching for similar content similar to the content recommended by the content recommendation procedure It is characterized by causing a computer to execute a procedure and a content information transmission procedure for transmitting information related to similar content searched by the similar content search procedure to a client device.

  The present invention also provides a computer-readable recording of a content search program used when searching for content useful for a user in response to a user request from the client device and transmitting information related to the searched content to the client device. A content recommendation procedure for recommending content based on an evaluation of a user similar to the user, and a similar content search procedure for searching for similar content similar to the content recommended by the content recommendation procedure A content search program for causing a computer to execute a content information transmission procedure for transmitting information related to similar content searched by the similar content search procedure to a client device is recorded.

  The present invention is also a content search method used when searching for content useful for a user in response to a user request from a client device and transmitting information related to the searched content to the client device, A content recommendation step of recommending content based on an evaluation of a user similar to the user, a similar content search step of searching for similar content similar to the content recommended by the content recommendation step, and a search of the similar content search step And a content information transmission step of transmitting information relating to similar content to the client device.

  Further, the present invention is a content search device used when searching for content useful for a user in response to a user request from the client device, and transmitting information related to the searched content to the client device, Content recommendation means for recommending content based on the evaluation of a user similar to the user, similar content search means for searching for similar content similar to the content recommended by the content recommendation means, and search by the similar content search means And content information transmitting means for transmitting information related to similar content to the client device.

  According to this invention, the content is recommended based on the evaluation of the user similar to the requesting user, the similar content similar to the recommended content is searched, and the information related to the searched similar content is transmitted to the client device. Therefore, it is possible to search for content similar to content highly evaluated by similar users.

  In the present invention, the content relationship indicating the relationship between the contents is stored in the profile storage device as a content relationship profile, and the similar content search procedure further includes the relationship between the contents from the user. A content relationship similar to the received content relationship is selected from the profile storage device, and content is searched based on the selected content relationship.

  According to this invention, the inter-content relationship indicating the relationship between the contents is stored in the profile storage device as the inter-content relationship profile, the inter-content relationship is received from the user, and the inter-content relationship similar to the received inter-content relationship is obtained. Since the content is selected from the profile storage device and searched for based on the selected inter-content relationship, the content can be searched based on the similarity of the inter-content relationship.

  According to the present invention, since content similar to content that is highly evaluated by similar users is searched, it is possible to respond to various content search requests from users.

  In addition, according to the present invention, content is searched based on the similarity of the relationship between the contents, so that it is possible to respond to various content search requests of the user.

  Exemplary embodiments of a content search program, a recording medium thereof, a content search method, and a content search device according to the present invention will be explained below in detail with reference to the accompanying drawings.

First, the configuration of the content search system according to the first embodiment will be described. FIG. 1 is a functional block diagram illustrating the configuration of the content search system according to the first embodiment. As shown in the figure, in this content search system, the content search device 100 and n client devices 10 ₁ to 10 _n are connected by a LAN 30, and the client devices 10 ₁ to 10 _n are connected via the LAN 30 and the Internet 40. constructed is connected to the number m the content server 20 ₁ to 20 _m of Te.

The client devices 10 ₁ to 10 _n are devices used by the user for searching for content, and the content servers 20 _{1 to} 20 _m are servers that provide content searched for by the user.

The content search device 100 is a device that searches for content in response to a user request from the client devices 10 ₁ to 10 _n , and includes a profile storage unit 110, a request reception unit 120, a content recommendation unit 130, and similar content The search unit 140 includes a list transmission unit 150, a content storage unit 160, a content transmission unit 170, a communication unit 180, and a profile management unit 190.

  The profile storage unit 110 is a storage unit that stores information about users and contents as a profile, and is realized by an object database. The profile storage unit 110 also stores a relationship (relationship) between the user and the content.

  FIG. 2 is a diagram illustrating profiles and relations stored in the profile storage unit 110. As shown in the figure, the profile storage unit 110 stores a user profile 201, a relation 202, and a content profile 203.

  The user profile 201 represents information about a user using a plurality of attribute information, and each attribute information is composed of a pair of an attribute name and an attribute value. For example, in FIG. 2, the attribute value for the attribute name “name” is “Ichiro Yamada”, and the attribute value for the attribute name “affiliation” is “sales”.

  FIG. 3 is a diagram illustrating an example of the user profile 201 stored in the profile storage unit 110. As shown in the figure, this user profile 201 has attribute names “user number”, “name”, “gender”, “affiliation”, “position”, “age”, “year”, “born”, “ It consists of attribute information which is “special committee”, “hobby”, and “specialty”.

  For example, a user whose attribute value is “1” for the attribute name “user number” is “Ichiro Yamada” for the attribute name “name”, and “m” for the attribute name “gender”. , The attribute value of the attribute name “affiliation” is “sales”, the attribute value of the attribute name “post” is “section manager”, the attribute value of the attribute name “age” is “38”, and the attribute name “years” The attribute value of “3”, the attribute value of “name of origin” is “Tokyo”, the attribute value of the attribute name “technical group” is “MBA”, and the attribute name is “hobby”. The value is “fishing” and the attribute value of the attribute name “special skill” is “judo”. In the following, a user whose attribute name “name” is “Ichiro Yamada” is simply referred to as “Ichiro Yamada”.

  The content profile 203 represents information about content using a plurality of attribute information, and each attribute information is composed of a pair of an attribute name and an attribute value. For example, in FIG. 2, the attribute value for the attribute name “storage location” is “file: //aaa.bb”, and the attribute value for the attribute name “creation date” is “2003/10/09”.

  FIG. 4 is a diagram illustrating an example of the content profile 203 stored in the profile storage unit 110. As shown in the figure, the content profile 203 has attribute names “content name”, “storage location”, “content type”, “creation date”, “creator”, “version”, and “genre”. Consists of attribute information.

  For example, the content having the attribute name “content name” with the attribute value “content A” has the attribute name “storage location” with the attribute value “file: //aaa.bb” and the attribute name “content type”. The attribute value is “HTML”, the attribute value “creation date” is “2003/10/09”, the attribute name “version” is “1.1”, and the attribute name “genre” The attribute value of “Product A”. In the following, a content whose attribute value “content name” has an attribute value “content A” is simply referred to as “content A”.

  The relation 202 indicates the connection between the user and the content, and this relation 202 includes attribute information. For example, in FIG. 2, the attribute value for the attribute name “importance” is “0.2”, and this attribute information represents the frequency of access to the content of the user. That is, when the user has many accesses to the content, the attribute value of the relation attribute name “importance” becomes large. The relation 202 is stored in the profile storage unit 110 as a relation profile.

The request receiving unit 120 is a processing unit that receives a user content search request from the client devices 10 ₁ to 10 _{n, and} receives user identification information, the number of contents to be searched, and the like.

  The content recommendation unit 130 is a processing unit that recommends content similar to the user who made the content search request and highly evaluated by the user. Specifically, the content recommendation unit 130 first calculates the user similarity of each user with respect to the user who made the content search request.

  Here, the user similarity is calculated based on the number of matching attribute values among predetermined attribute information included in the user profile. For example, in the case of using attribute information whose attribute names are “Affiliation”, “Position”, and “Professional Committee” among the attribute information of the user profile shown in FIG. 3, a content search request is made. If the user is “Taro Suzuki”, “Yamada Ichiro” has “0” as the number of matching attribute information, “Ryoko Ogawa” has “1” as the number of matching attribute information, and “Hanako Ishikawa” ”Is the number of matching attribute information“ 3 ”,“ Toru Yamashita ”is the number of matching attribute information“ 2 ”, and“ Nobuya Imai ”is the number of matching attribute information“ 1 ”. .

  The user similarity of “Ichiro Yamada” is “0/3” with respect to “Taro Suzuki” who has made a content search request by dividing and standardizing by the number of attribute information “3” using these numbers. = 0 ”, the user similarity of“ Ogawa Ryoko ”is“ 1/3 = 0.3 ”, the user similarity of“ Ishikawa Hanako ”is“ 3/3 = 1 ”, and the user similarity of“ Toru Yamashita ” The degree is “2/3 = 0.7”, and the user similarity of “Imai Shinya” is “1/3 = 0.3”.

  And the content recommendation part 130 calculates the evaluation score of each content using a user similarity and the relationship between a user and a content. Here, as the relation between the user and the content, specifically, the attribute value of the attribute name “importance” of the relation between the user and the content is used.

  FIG. 5 is a diagram illustrating an example of a relationship between a user and content. In the figure, the numerical value given to the relation between the user and the content (here, the line connecting the user and the content) indicates the attribute value of the attribute name “importance”.

  For example, the attribute value of the attribute name “importance” of the relation between “Ichiro Yamada” and “content A” is “0.5”, and the relation value between “Ryoko Ogawa” and “content A” The attribute value of the attribute name “importance” is “0.9”, and the attribute value of the attribute name “importance” of the relation between “Hanako Ishikawa” and “content A” is “0.8”. .

  The content recommendation unit 130 uses the average of the product of the attribute value and the user similarity as an evaluation score. For example, the evaluation score for “content A” by “Taro Suzuki” is (0 × 0.5 + 0.3 × 0.9 + 1.0 × 0.8) /3=0.36. Similarly, the content recommendation unit 130 calculates an evaluation score for other content, and selects the content with the highest evaluation score as the recommended content.

  As described above, the content recommendation unit 130 recommends content based on the user similarity to the user who has made the content search request and the relation between the user and the content, thereby searching for the content evaluated by the similar user. be able to.

  The similar content search unit 140 is a processing unit that searches for content similar to the content recommended by the content recommendation unit 130. Here, the similar content search unit 140 calculates the content similarity between the contents based on the number of matching attribute values among predetermined attribute information included in the content profile.

  For example, the content recommended by the content recommendation unit 130 is “content A”, and the attribute information whose attribute names are “content type”, “creation date”, “version”, and “genre” in the content profile shown in FIG. Is used for calculation of the content similarity, “content B” matches “version” and “genre” with respect to “content A”, so the content similarity is “2”.

  Similarly, “content C” matches “content type”, “creation date”, and “version” with respect to “content A”, so the content similarity is “3”, and “content D” is “content type”. ”Match, the content similarity is“ 1 ”.

  In this way, the similar content search unit 140 selects content having a high degree of content similarity with the content recommended by the content recommendation unit 130, and outputs a list thereof.

List transmission unit 150, the processing unit similar content search unit 140 creates the data for displaying the content list to be output to the client device 10 ₁ to 10 _n, via the communication unit 180 to the client device 10 ₁ to 10 _n It is.

The content storage unit 160 is a storage unit that stores a part of content managed by the content search device 100. Content content search device 100 is managed by using a content profile 203, this in addition to the content storage unit 160, are stored in the external content server 20 ₁ to 20 _m.

The content transmission unit 170 is a processing unit that reads content specified by the user from the content storage unit 160 and transmits the content to the client devices 10 ₁ to 10 _n . That is, when the user requests a specific content display from the content list transmitted to the client devices 10 ₁ to 10 _n by the list transmission unit 150 and the content requested by the user is stored in the content storage unit 160. The content transmission unit 170 reads the content from the content storage unit 160 and transmits the content to the client devices 10 ₁ to 10 _n .

If the content requested by the user from the content list transmitted to the client devices 10 ₁ to 10 _n by the list transmission unit 150 is stored in the external content servers 20 _{1 to} 20 _m , the user request It is sent directly to the content server 20 ₁ to 20 _m.

The communication unit 180 is a processing unit that communicates with the client devices 10 ₁ to 10 _n via the LAN 30. Specifically, the communication unit 180 receives a search request and transmits a content list and content. Further, the communication unit 180 receives information such as the access status of the content from the content server 20 ₁ to 20 _m over the LAN30 and the Internet 40.

The profile management unit 190 is a processing unit that manages information about profiles and relations stored in the profile storage unit 110. For example, the profile manager 190 receives information such as the access status of the content from the content transmitting unit 170 or the content server 20 ₁ to 20 _m, and updates the information about the content profiles and relationships.

Next, a processing procedure of the content search apparatus 100 according to the first embodiment will be described. FIG. 6 is a flowchart illustrating the processing procedure of the content search apparatus 100 according to the first embodiment. As shown in the figure, in the content search device 100, the request receiving unit 120 receives a content search request from the client devices 10 ₁ to 10 _n (step S601), and analyzes the received request (step S602). Here, contents analyzed by the request receiving unit 120 include identification of a user who has made a search request and the number of contents to be displayed in a list.

Then, the content recommendation unit 130 selects content to be recommended to the user specified by the request reception unit 120 (step S603), and the similar content search unit 140 selects content similar to the content selected by the content recommendation unit 130. The contents list of the designated number is searched and created (step S604). Then, the list transmission unit 150 transmits the content list to the client devices 10 ₁ to 10 _n via the communication unit 180 (step S605).

  As described above, the content recommendation unit 130 selects content to be recommended to the user, and the similar content search unit 140 searches the content similar to the content selected by the content recommendation unit 130 and creates a content list. It is possible to provide the user with a list of content similar to content that is highly evaluated by similar users.

  Next, the processing procedure of the content recommendation unit 130 shown in FIG. 1 will be described. FIG. 7 is a flowchart showing a processing procedure of the content recommendation unit 130 shown in FIG. The process of the content recommendation unit 130 corresponds to the recommendation process execution (step S603) shown in FIG.

  As shown in the figure, the content recommendation unit 130 calculates the user similarity of another user with respect to the user who requested the content search using the attribute information of the user profile 201 (step S701), and calculates the calculated user similarity. The degree is divided by the number of attribute information using the standardization (step S702).

  Then, the evaluation score of each content is calculated from the user similarity of each user to the user who requested the content search and the importance of the content for each user (step S703), and the content with the highest evaluation score is recommended (step S703). S704).

  Thus, the content recommendation unit 130 calculates the evaluation score of each content from the user similarity of each user to the user who requested the content search and the importance of the content for each user, and the content with the highest evaluation score is calculated. By recommending, the content search apparatus 100 can search for content that is highly evaluated by similar users.

  Next, a processing procedure of the similar content search unit 140 illustrated in FIG. 1 will be described. FIG. 8 is a flowchart showing a processing procedure of the similar content search unit 140 shown in FIG. Note that the processing of the similar content search unit 140 corresponds to the similar search execution (step S604) shown in FIG.

  As shown in the figure, the similar content search unit 140 calculates the content similarity of each content with respect to the content recommended by the content recommendation unit 130 using the attribute information of the content profile 203 (step S801).

  Then, the content having a high content similarity is selected from the top as many as the designated number (step S802), and a content list is created based on the selected content (step S803).

  As described above, the similar content search unit 140 creates a list of content similar to the content recommended by the content recommendation unit 130, so that the content search apparatus 100 can list the content similar to the content highly evaluated by similar users. Can be provided to the user.

  As described above, in the first embodiment, the profile storage unit 110 stores the profile of the user and the content and the relation between the user and the content, and the content recommendation unit 130 is similar to the user who requested the content search. Content with high user evaluation is recommended based on the user profile and relation stored in the profile storage unit 110, and the similar content search unit 140 uses the content profile to list content similar to the content recommended by the content recommendation unit 130. Since the creation is made, it is possible to provide the user with a list of content similar to content highly evaluated by similar users.

  In the first embodiment, the case where the content recommendation unit 130 recommends one content having the highest evaluation by similar users has been described. However, the present invention is not limited to this, and a plurality of content recommendation units 130 are provided. The same applies to the case where the content list is created using the plurality of contents recommended by the content recommendation unit 130 and the content similar to each of the plurality of contents. be able to.

  In the first embodiment, the content search apparatus 100 that searches for content similar to content that is highly evaluated by similar users has been described. However, in order for a user to efficiently access useful content from a vast amount of content, it is desired that the content search device has more search functions. Therefore, in the second embodiment, a content search apparatus that searches for content requested by a user by specifying a relation between contents will be described.

  First, the configuration of the content search apparatus according to the second embodiment will be described. FIG. 9 is a functional block diagram of the configuration of the content search apparatus according to the second embodiment. Here, for convenience of explanation, functional units that play the same functions as the respective units shown in FIG.

  As shown in the figure, the content search apparatus 900 includes a profile storage unit 910, a request reception unit 920, a content recommendation unit 130, a similar content search unit 930, a list transmission unit 150, and a content storage unit 160. , A content transmission unit 170, a communication unit 180, and a profile management unit 190.

  In addition to the information stored in the profile storage unit 110, the profile storage unit 910 also stores information about relations between contents. FIG. 10 is a diagram showing the relation between contents stored in the profile storage unit 910.

  The figure shows two relations of “reference” and “quote” as relations between contents. For example, in the figure, there is a relation that “specification 1” “references” “reference 1” between content “specification 1” and content “reference 1”, and content “manual 1”. And “contents 1” have a relation that “manual 1” “cites” “reference 1”. The profile storage unit 910 stores such a relation between contents as a relation profile.

  FIG. 11 is a diagram illustrating an example of a relationship profile. In the figure, the relation profile of the relation "reference" is an attribute whose attribute name is "relation number", "relation name", "connection destination content name", "relation creator", and "relation creation date" The relation profile of the relation "quote" indicates that it is composed of information, "relation number", "relation name", "connection destination content name", "citation page", "relation creator" and "relation creation" Indicates that it is composed of attribute information having “day” as an attribute name.

  For example, a relation “reference” whose “relation number” is “1” has a “connection destination content name” of “reference 1”, a “relation creator” of “suzuki”, and a “relation creation date” Is “2003/10/09”, “Relation number” is “2”, the relation “quoting” is “connection destination content name” is “reference 1”, and “citation page” is “100”. Page ”,“ Relation Creator ”is“ ishikawa ”, and“ Relation Creation Date ”is“ 2002/11/01 ”.

  The request accepting unit 920 accepts designation of relation attribute values from the user in addition to the content search request accepted by the request accepting unit 120. For example, the request reception unit 920 receives specification of attribute values such as “connection destination content = document 1” and “citation page = 100”.

  The similar content search unit 930 performs a similar search between relations in addition to the similar search between contents performed by the similar content search unit 140. Specifically, the similar content search unit 930 searches for content using a relation having similar attribute values of the relation received by the request reception unit 920 from the user.

  For example, when the similar content search unit 930 receives a content search request in which “connection destination content = document 1” and “citation page = 100” are specified, the similar content search unit 930 is specified using the relation profile shown in FIG. Calculate the relation similarity of each relation to the attribute value.

  First, the relation whose “relation number” is “1” matches “connection destination content = document 1”, so the relation similarity is “1”, and the relation whose “relation number” is “2” The relation similarity is “2” because “first content = document 1” and “citation page = 100” match.

  Similarly, the similar content search unit 930 calculates the relation similarity for the relations whose “relation number” is “3”, “4”, and “5”, and obtains “0”, “1”, and “0”. .

  As a result, the relation whose “relation number” is “2” becomes the relation having the highest relation similarity with the relation similarity “2”, and the content with the relation, that is, the “manual” shown in FIG. 1 ”is the content with the highest relation similarity. In this way, the similar content search unit 930 can search for content using a relation having attribute information similar to the specified attribute value.

  Next, a processing procedure of the similar content search unit 930 according to the second embodiment will be described. FIG. 12 is a flowchart illustrating the processing procedure of the similar content search unit 930 according to the second embodiment.

  As shown in the figure, the similar content search unit 930 calculates the relation similarity of each relation based on the specified attribute value (step S1201), and is specified by the user in descending order of the calculated relation similarity. The number of relations selected is selected (step S1202). Then, the content with the selected relation is searched (step S1203), and a content list is created (step S1204).

  In this way, the similar content search unit 930 calculates the relation similarity using the attribute information of the relation between the contents, and searches for the content with the relation with the high relation similarity calculated, thereby searching for the content. The apparatus 900 can perform a content search based on the relation relationship.

  As described above, in the second embodiment, the profile storage unit 910 stores a relation between contents as a relation profile, and the similar content search unit 930 searches for a relation similar to the relation specified by the user using the relation profile. In addition, since the content with the similar relation is searched, the content search based on the similar relation of the relation can be performed.

  In the first and second embodiments, the content search devices have been described. However, a content search program having the same function can be obtained by realizing the configuration of these content search devices with software. Therefore, a computer system that executes these content search programs will be described.

  FIG. 13 is a diagram illustrating a computer system that executes the content search program according to the first and second embodiments. As shown in the figure, the computer system 300 includes a main body 301, a display 302 that displays information on a display screen 302 a according to instructions from the main body 301, and for inputting various information to the computer system 300. A keyboard 303, a mouse 304 for designating an arbitrary position on the display screen 302a of the display 302, a LAN interface connected to the LAN 306 or a wide area network (WAN), and a modem connected to the public line 307 are included. Here, the LAN 306 connects the computer system 300 to another computer system (PC) 311, a server 312, a printer 313, and the like.

  FIG. 14 is a functional block diagram showing the configuration of the main body 301 shown in FIG. As shown in the figure, the main unit 301 includes a CPU 321, a RAM 322, a ROM 323, a hard disk drive (HDD) 324, a CD-ROM drive 325, an FD drive 326, an I / O interface 327, and a LAN. An interface 328 and a modem 329 are included.

  The content search program executed in the computer system 300 is stored in a portable storage medium such as a floppy disk (FD) 308, a CD-ROM 309, a DVD disk, a magneto-optical disk, an IC card, and the like. It is read and installed in the computer system 300.

  Alternatively, the content search program is stored in a database of the server 312 connected via the LAN interface 328, a database of another computer system (PC) 311, and the like, read from these databases, and installed in the computer system 300. Is done.

  The installed content search program is stored in the HDD 324 and executed by the CPU 321 using the RAM 322, the ROM 323, and the like.

  As described above, the content search program, the recording medium thereof, the content search method, and the content search device according to the present invention are useful for a content search system that searches a user for useful content from a vast amount of content. It is suitable for a content search system that needs to respond to various search requests.

1 is a functional block diagram illustrating a configuration of a content search system according to a first embodiment. It is a figure which shows the profile and relation which a profile memory | storage part memorize | stores. It is a figure which shows an example of the user profile which a profile memory | storage part memorize | stores. It is a figure which shows an example of the content profile which a profile memory | storage part memorize | stores. It is a figure which shows an example of the relationship between a user and content. 3 is a flowchart illustrating a processing procedure of the content search apparatus according to the first embodiment. It is a flowchart which shows the process sequence of the content recommendation part shown in FIG. It is a flowchart which shows the process sequence of the similar content search part shown in FIG. It is a functional block diagram which shows the structure of the content search apparatus concerning the present Example 2. It is a figure which shows the relation between the contents which a profile memory | storage part memorize | stores. It is a figure which shows an example of a relationship profile. It is a flowchart which shows the process sequence of the similar content search part which concerns on the present Example 2. 1 is a diagram illustrating a computer system that executes a content search program according to first and second embodiments. FIG. It is a functional block diagram which shows the structure of the main-body part shown in FIG.

Explanation of symbols

10 ₁ , 10 ₂ , 10 _n client devices 20 ₁ , 20 ₂ , 20 _m content server 30 LAN
40 Internet 100, 900 Content search device 110, 910 Profile storage unit 120, 920 Request reception unit 130 Content recommendation unit 140, 930 Similar content search unit 150 List transmission unit 160 Content storage unit 170 Content transmission unit 180 Communication unit 190 Profile management unit 201 User Profile 202 Relation 203 Content Profile 300, 311 Computer System 301 Main Body 302 Display 302a Display Screen 303 Keyboard 304 Mouse 306 LAN
307 Public line 308 floppy disk 309 CD-ROM
312 Server 313 Printer 321 CPU
322 RAM
323 ROM
324 Hard disk drive 325 CD-ROM drive 326 Floppy disk drive 327 I / O interface 328 LAN interface 329 Modem

Claims (10)

A content search program used when searching for content useful for a user in response to a user request from a client device and transmitting information about the searched content to the client device,
A content recommendation procedure for recommending content based on an evaluation of a user similar to the user;
A similar content search procedure for searching similar content similar to the content recommended by the content recommendation procedure;
A content search program for causing a computer to execute a content information transmission procedure for transmitting information related to similar content searched by the similar content search procedure to a client device. A profile storage device stores a user profile that characterizes a user using attribute information that is a pair of an attribute name and an attribute value, and a relationship profile that characterizes a relationship between the user and content using the attribute information. ,
The content recommendation procedure calculates a user similarity between users using a user profile stored in the profile storage device, and calculates a user evaluation value for the content using a relationship profile stored in the profile storage device The content search program according to claim 1, wherein the content is recommended based on the calculated user similarity and the evaluation value.   In the content recommendation procedure, the user similarity is calculated using the number of user profile attribute information having the same attribute value among users, and attribute information indicating the access frequency to the user's content is included in the attribute information of the relation profile. The content search program according to claim 2, wherein the evaluation value is calculated by using the content search program.   The content search program according to claim 3, wherein the content recommendation procedure recommends content using an average value of products of the user similarity and an evaluation value. A content profile that characterizes content using attribute information that is a pair of an attribute name and an attribute value is stored in the profile storage device,
The similar content search procedure calculates content similarity between contents using a content profile stored in the profile storage device, and searches for the similar content using the calculated content similarity. The content search program as described in any one of Claims 1-4. A content relationship indicating a relationship between contents is stored in the profile storage device as a content relationship profile,
The similar content search procedure further receives a content relationship from the user, selects a content relationship similar to the received content relationship from the profile storage device, and selects a content based on the selected content relationship. 5. The content search program according to claim 1, wherein a search is performed. In the profile storage device, an inter-content relationship is stored as an inter-content relationship profile using attribute information that is a pair of an attribute name and an attribute value.
The similar content search procedure receives attribute information on the relationship between contents from the user, and uses the number of pieces of attribute information whose attribute values match the similarity between contents in the received relationship between contents and the relationship between other contents. The content search program according to claim 6, wherein similar content relationships are selected using the calculated content relationship similarity. A computer-readable recording medium storing a content search program used when searching for content useful for a user in response to a user request from a client device and transmitting information about the searched content to the client device. And
A content recommendation procedure for recommending content based on an evaluation of a user similar to the user;
A similar content search procedure for searching similar content similar to the content recommended by the content recommendation procedure;
A computer-readable recording medium recording a content search program for causing a computer to execute a content information transmission procedure for transmitting information related to similar content searched by the similar content search procedure to a client device. A content search method used when searching for content useful for a user in response to a user request from a client device and transmitting information about the searched content to the client device,
A content recommendation step for recommending content based on an evaluation of a user similar to the user;
A similar content search step of searching for similar content similar to the content recommended by the content recommendation step;
A content information transmitting step of transmitting information related to the similar content searched in the similar content searching step to a client device. A content search device used when searching for content useful to a user in response to a user request from a client device and transmitting information about the searched content to the client device,
Content recommendation means for recommending content based on the evaluation of a user similar to the user;
Similar content search means for searching for similar content similar to the content recommended by the content recommendation means;
A content information transmission device comprising: content information transmission means for transmitting information related to similar content searched by the similar content search means to a client device.

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