Patent Publication Number: US-8117212-B2

Title: Information processing apparatus and method, program and recording medium

Description:
CROSS REFERENCES TO RELATED APPLICATIONS 
     The present invention contains subject matter related to Japanese Patent Application JP 2006-255729 filed in the Japan Patent Office on Sep. 21, 2006, the entire contents of which being incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     This invention relates to an information processing apparatus and method, a program and a recording medium, and more particularly to an information processing apparatus and method, a program and a recording medium wherein items are recommended to a user. 
     2. Description of the Related Art 
     In recent years, the communication techniques have developed and various communication services provided through a network represented by the Internet have been popularized. One of such communication services is a recommendation system which introduces recommendable contents or commodities to users in order to allow a user to appropriately select and, for example, purchase, from among a large number of contents or commodities, those contents or commodities which conform to a liking of the user. 
     One of such recommendation systems for contents or commodities by a computer system as described above introduces a content or a commodity selected at random. However, also a recommendation system is available which introduces contents or commodities, for example, suitable for the liking of users and estimated to be selected by the users in order to achieve more useful recommendation. In this instance, a server which performs such recommendation usually estimates the liking of users and motivates users to feed back some information thereto in order to select contents or commodities to be introduced. 
     The feedback information from users includes express information like, for example, five-stage evaluation from “favorable” to “unfavorable” and non-express information such as information that, in the case of music, a reproduced musical piece is favorable whereas a skipped musical piece is unfavorable. The server of the system estimates the liking of a user based on such feedback information and determines items to be presented to the user. 
     For the determination just described, for example, content based filtering (CBF) is available and disclosed, for example, in Japanese Patent Laid-Open No. 2001-160955 (hereinafter referred to as Patent Document 1). According to the content based filtering, where meta data are applied to each content, the liking of a user is determined as a sum total or an average of the meta data of those contents which have been enjoyed by the user. Then, the inner product or the cosine similarity degree between the liking of the user and an unknown content is used to determine whether or not the content should be recommended to the user. 
     Also collaborative filtering (CF) is available for the determination described above and is disclosed, for example, in P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl, “GroupLens: Open Architecture for Collaborative Filtering of Netnews”, Conference on Computer Supported Cooperative Work, pp. 175-186, 1994 (hereinafter referred to as Non-Patent Document 1). According to the collaborative filtering, for example, predictive evaluation values based on similarity in content evaluation between users are used to recommend an unknown content to a user without utilizing meta data of the content. 
     In both methods, in order for the server to perform appropriate recommendation to a user, feedback information from more than a fixed number of users may be required. However, in an ordinary case, feedback of a great amount of information from users in a short period of time cannot be anticipated. Particularly, there is the possibility that a cold start problem that inappropriate recommendation is performed in an initial stage after operation of the system is started may occur. This is described, for example, in Maltz, D. and Ehrlich, K., “Pointing the way: Active collaborative filtering”, Proceedings of the Annual ACM SIGCHI Conference on Human Factors in Computing Systems (CHI95), pp. 202-209, 1995 (hereinafter referred to as Non-Patent Document 2). 
     In the method disclosed in Non-Patent Document 2, it is attempted to solve the problem by causing an existing user to transmit a pointer of information to another user. Also another method has been proposed wherein non-express feedback information of a user is obtained from residing time on a Web page or a movement of a mouse to cover express evaluation or feedback. The method is disclosed, for example, in Claypool, M., Le, P., Waseda, M. and Brown, D., “Implicit Interest Indicators”, Proceedings of the 6th International Conference on Intelligent User Interfaces, pp. 33-40, 2001 (hereinafter referred to as Non-Patent Document 3). 
     SUMMARY OF THE INVENTION 
     However, the method disclosed in Non-Patent Document 2 has the possibility that the liking of an individual may not necessarily be reflected. Meanwhile, where the server estimates the liking of a user based on non-express feedback as in the method disclosed in Non-Patent Document 3, the correctness of the estimation relies much upon the type of the content. Therefore, it is difficult to apply the method of Non-Patent Document 3 generally. 
     Thus, it is difficult for the server to carry out useful recommendation as described above. 
     Therefore, it is demanded to provide an information processing apparatus and method, a program and a recording medium wherein an appropriate item to a user can be recommended in a recommendation system which introduces contents or commodities. 
     According to the present invention, such recommendation of an appropriate item is achieved by feeding back not only an express evaluation behavior of users but also a relationship between recommendation object information. 
     More particularly, according to an embodiment of the present invention, there is provided an information processing apparatus for providing a service of introducing, from a predetermined item which is to make a reference, associated items associated with the reference item to one or more different information processing apparatus, including an association degree collection section configured to collect association degrees applied between items by users of the different information processing apparatus and indicative of weights of a relationship between the items, and an introduction section configured to determine the associated items from the reference item based on a result of the collection by the association degree collection section and introduce the determined associated items to any of the different information processing apparatus. 
     The information processing apparatus may further include a user liking calculation section configured to calculate a liking of any of the users of the different information processing apparatus based on the result of the collection by the association degree collection section, and a comparison section configured to compare the liking of the user calculated by the user liking calculation section and the individual items to determine similarity degrees therebetween, the introduction section introducing those of the items which are determined to have a high similarity degree to the liking of the user by the comparison by the comparison section as the associated items. 
     The information processing apparatus may further include a similar user search section configured to compare the applied association degrees for each of the users and search for similar users who have a high similarity degree of the liking to one of the users based on a result of the comparison, and an item evaluation prediction section configured to predict an evaluation value for each of the items by the user based on evaluation items for the items by the similar users searched out by the similar user search section, the introduction section introducing those of the items to which it is predicted for the user of the information processing apparatus to provide high evaluation by the item evaluation prediction section as the associated items. 
     The information processing apparatus may further include a reliability degree calculation section configured to calculate a reliability degree of each of the association degrees as information. In this instance, the introduction section may introduce, together with the associated items, the association degrees applied from the reference item to the associated items and the reliability degrees of the association degrees. 
     The information processing apparatus may further include a user reliability degree calculation section configured to calculate a reliability degree of users by whom the association degrees are applied, a similar user search section configured to compare the applied association degrees for each of the users and search for similar users who have a high similarity degree of the liking to one of the users based on a result of the comparison, and an item evaluation prediction section configured to perform weighting for the similar users searched out by the similar user search section based on the reliability degrees of the users calculated by the user reliability calculation section and predict evaluation values for the items by the user based on the weights of the similar users and evaluation values for the items by the similar users, the introduction section introducing those of the items to which it is predicted for the user of the information processing apparatus to provide high evaluation by the item evaluation prediction section as the associated items. 
     The information processing apparatus may further include an additional meta data application section configured to apply new meta data to the items based on the association degrees. In this instance, the information processing apparatus may further include a reliability degree calculation section configured to calculate a reliability degree of the association degrees as information, the additional meta data application section applying new meta data to the items based on both of the association degrees and the reliability degrees of the association degrees. Or, the information processing apparatus may further include an item similarity degree calculation section configured to calculate a similarity degree between the items using new meta data applied by the additional meta data application section, the introduction section introducing those of the items which have a high similarity degree to the reference item calculated by the item similarity degree calculation section as the associated items. Or else, the information processing apparatus may further include a user liking calculation section configured to calculate a liking of users of the different information processing apparatus based on a result of the collection by the association degree collection section, and a comparison section configured to compare the new meta data for the items applied by the additional meta data application section with the likings of the users calculated by the user liking calculation section to determine a similarity degree therebetween, the introduction section introducing those of the items which are decided to have a high similarly degree to the likings of the users by the comparison by the comparison section as the associated items. 
     The information processing may further include an importance degree calculation section configured to calculate an importance degree of the items among all items based on the association degrees applied between the items. In this instance, the information processing apparatus may further include a correction section configured to correct a display form of information of the associated items to be introduced to the other information processing apparatus based on the importance degrees calculated by the importance degree calculation section, the introduction section causing any of the other different information processing apparatus which is a destination of the instruction to display the information of the associated items in the display form corrected by the correction section. Or, the information processing apparatus may further include an explanation application section configured to apply an explanation of the associated items which is to be introduced to the other information processing apparatus to the information of the associated items based on the importance degrees calculated by the importance degree calculation section. Or else, the information processing apparatus may further include a user liking calculation section configured to calculate a liking of users of the different information processing apparatus based on a result of the collection by the association value correction section, and an explanation application section configured to apply an explanation of the associated items which is to be introduced to the other information processing apparatus to the information of the associated items based on the importance degrees calculated by the importance degree calculation section and the likings of the users calculated by the user liking calculation section. 
     According to another embodiment of the present invention, there is provided an information processing method for an information processing apparatus for providing a service of introducing, from a predetermined item which is to make a reference, associated items associated with the reference item to one or more different information processing apparatus, including the steps of collecting association degrees applied between items by users of the different information processing apparatus and indicative of weights of a relationship between the items, and determining the associated items from the reference item based on a result of the collection by the process at the association degree collection step and introduce the determined associated items to any of the different information processing apparatus. 
     According to another embodiment of the present invention, there is provided a program which can be executed by a computer which controls a process of providing a service of introducing, from a predetermined item which is to make a reference, associated items associated with the reference item to one or more different information processing apparatus, including the steps of collecting association degrees applied between items by users of the different information processing apparatus and indicative of weights of a relationship between the items, and determining the associated items from the reference item based on a result of the collection by the process at the association degree collection step and introduce the determined associated items to any of the different information processing apparatus. 
     According to a further embodiment of the present invention, there is provided a recording medium on or in which the program is recorded. 
     In the information processing apparatus and method and the program, association degrees applied between items by users of the different information processing apparatus and indicative of weights of a relationship between the items are collected. Then, associated items are determined from the reference item based on a result of the collection and introduced to any of the different information processing apparatus. 
     With the information processing apparatus and method and the program, items can be recommended to a user. Particularly, upon such recommendation of items, those items which are very appropriate to the user can be recommended. 
     The above and other features and advantages of the present invention will become apparent from the following description and the appended claims, taken in conjunction with the accompanying drawings in which like parts or elements denoted by like reference symbols. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram showing an example of a configuration of an item introduction system to which the present invention is applied; 
         FIG. 2  is a diagrammatic view illustrating association between items; 
         FIG. 3  is a block diagram illustrating functions of a server shown in  FIG. 1 ; 
         FIG. 4  is a block diagram illustrating functions of a CPU of the server; 
         FIG. 5  is a schematic view showing an example of a configuration of a storage section shown in  FIG. 3 ; 
         FIG. 6  is a flow chart illustrating an example of a flow of an association degree application process; 
         FIG. 7  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the association degree application process; 
         FIG. 8  is a flow chart illustrating an example of a flow of a presentation process; 
         FIG. 9  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the presentation process; 
         FIG. 10  is a table illustrating an example of a liking vector; 
         FIG. 11  is a table illustrating an example of a new item; 
         FIG. 12  is a table illustrating an example of the distance from the liking vector; 
         FIG. 13  is a table illustrating an example of normalized meta data; 
         FIG. 14  is a table illustrating an example of a modified liking vector; 
         FIG. 15  is a table illustrating an example of the distance from the modified liking vector; 
         FIG. 16  is a flow chart illustrating another example of the flow of the presentation process; 
         FIG. 17  is a block diagram illustrating an example of the manner of operation of functional blocks which participate in the presentation process of  FIG. 16 ; 
         FIGS. 18 to 20  are tables illustrating an example of association degrees applied by different users; 
         FIG. 21  is a correspondence table illustrating comparison of the association degrees applied by the users; 
         FIG. 22  is a table illustrating correlation coefficients of one to the others of the users; 
         FIG. 23  is a flow chart illustrating an example of a flow of an association degree reliability degree setting process; 
         FIG. 24  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the association degree reliability degree setting process; 
         FIG. 25  is a table illustrating an example of association degrees applied by all users; 
         FIG. 26  is a table illustrating an example of the reliability degree of association degrees; 
         FIG. 27  is a flow chart illustrating an example of a user reliability degree setting process; 
         FIG. 28  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the user reliability degree setting process; 
         FIG. 29  is a table illustrating an example of average values of the association degree among all users; 
         FIG. 30  is a table illustrating an example of standard deviations of association degrees; 
         FIG. 31  is a table illustrating an example of absolute values of deviations of association degrees by a user; 
         FIGS. 32 and 33  are flow charts illustrating different examples of the flow of the presentation process; 
         FIG. 34  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the presentation process of  FIG. 33 ; 
         FIG. 35  is a schematic view illustrating an example of presentation information; 
         FIG. 36  is a flow charts illustrating a still further example of the flow of the presentation process; 
         FIG. 37  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the presentation process of  FIG. 36 ; 
         FIG. 38  is a block diagram illustrating an example of a flow of an additional meta data production process; 
         FIG. 39  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the additional meta data production process; 
         FIG. 40  is a table illustrating an example of association degrees; 
         FIG. 41  is a table illustrating an example of additional meta data according to a SAM parameter; 
         FIG. 42  is a flow chart illustrating an example of a flow of a similar item process; 
         FIG. 43  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the similar item process; 
         FIG. 44  is a table illustrating an example of meta data of items; 
         FIG. 45  is a table illustrating an example of distances from one of the items illustrated in  FIG. 44 ; 
         FIG. 46  is a table illustrating an example of distances where additional meta data are involved; 
         FIG. 47  is a flow chart illustrating an example of a flow of a user liking matching process; 
         FIG. 48  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the user liking matching process; 
         FIG. 49  is a table illustrating an example of normalized meta data; 
         FIG. 50  is a table illustrating an example of a user liking vector; 
         FIG. 51  is a table illustrating an example of the Euclid distances where no additional meta data are involved; 
         FIG. 52  is a table illustrating an example of the Euclid distances where additional meta data are involved; 
         FIG. 53  is a flow chart illustrating an example of a flow of an item importance degree calculation process; 
         FIG. 54  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the item importance degree calculation process; 
         FIG. 55  is a view illustrating an example of a transition stochastic matrix; 
         FIGS. 56 and 57  are views illustrating an example of matrices obtained by characteristic value decomposition of the transition stochastic matrix; 
         FIG. 58  is a table illustrating an example of importance degrees; 
         FIG. 59  is a flow chart illustrating an example of a flow of an item importance degree presentation correction process; 
         FIG. 60  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the item importance degree presentation correction process; 
         FIG. 61  is a schematic view illustrating another example of the presentation information; 
         FIG. 62  is a flow chart illustrating a yet further example of the flow of the presentation process; 
         FIG. 63  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the presentation process of  FIG. 62 ; 
         FIG. 64  is a flow chart illustrating a yet further example of the flow of the presentation process; 
         FIG. 65  is a block diagram illustrating an example of a manner of operation of functional blocks which participate in the presentation process of  FIG. 64 ; and 
         FIG. 66  is a table illustrating an example of explanations of items. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Before a preferred embodiment of the present invention is described in detail, a corresponding relationship between several features recited in the accompanying claims and particular elements of the preferred embodiment described below is described. The description, however, is merely for the confirmation that the particular elements which support the invention as recited in the claims are disclosed in the description of the embodiment of the present invention. Accordingly, even if some particular element which is recited in description of the embodiment is not recited as one of the features in the following description, this does not signify that the particular element does not correspond to the feature. On the contrary, even if some particular element is recited as an element corresponding to one of the features, this does not signify that the element does not correspond to any other feature than the element. 
     According to the present invention, there is provided an information processing apparatus (for example, a server  11  shown in  FIG. 1 ) for providing a service of introducing, from a predetermined item which is to make a reference, associated items associated with the reference item to one or more different information processing apparatus (for example, a client  12  and another client  13  shown in  FIG. 1 ), including an association degree collection section (for example, an association degree collection section  201  shown in  FIG. 4 ) configured to collect association degrees applied between items by users of the different information processing apparatus and indicative of weights of a relationship between the items, and an introduction section (for example, an item information presentation processing section  214 , an item information presentation processing section  224 , an item information presentation processing section  244  or an item information presentation processing section  255  shown in  FIG. 4 ) configured to determine the associated items from the reference item based on a result of the collection by the association degree collection section and introduce the determined associated items to any of the different information processing apparatus. 
     The information processing apparatus may further include a user liking calculation section (for example, a user liking calculation section  212  shown in  FIG. 4 ) configured to calculate a liking of any of the users of the different information processing apparatus based on the result of the collection by the association degree collection section, and a comparison section (for example, an item-user liking matching section  213  shown in  FIG. 4 ) configured to compare the liking of the user calculated by the user liking calculation section and the individual items to determine similarity degrees therebetween, the introduction section introducing those of the items which are determined to have a high similarity degree to the liking of the user by the comparison by the comparison section as the associated items. 
     The information processing apparatus may further include a similar user search section (for example, a user similarity degree calculation section  222  shown in  FIG. 4 ) configured to compare the applied association degrees for each of the users and search for similar users who have a high similarity degree of the liking to one of the users based on a result of the comparison, and an item evaluation prediction section (for example, an item evaluation prediction section  223  shown in  FIG. 4 ) configured to predict an evaluation value for each of the items by the user based on evaluation items for the items by the similar users searched out by the similar user search section, the introduction section introducing those of the items to which it is predicted for the user of the information processing apparatus to provide high evaluation by the item evaluation prediction section as the associated items. 
     The information processing apparatus may further include a reliability degree calculation section (for example, an association degree reliability degree calculation section  231  shown in  FIG. 4 ) configured to calculate a reliability degree of each of the association degrees as information. 
     The introduction section may introduce, together with the associated items, the association degrees applied from the reference item to the associated items and the reliability degrees of the association degrees (for example, at step S 127  of  FIG. 33  or step S 146  of  FIG. 34 ). 
     The information processing apparatus may further include a user reliability degree calculation section (for example, a user reliability degree calculation section  232  shown in  FIG. 4 ) configured to calculate a reliability degree of users by whom the association degrees are applied, a similar user search section configured to compare the applied association degrees for each of the users and search for similar users who have a high similarity degree of the liking to one of the users based on a result of the comparison, and an item evaluation prediction section (for example, the item evaluation prediction section  223  shown in  FIG. 4 ) configured to perform weighting for the similar users searched out by the similar user search section based on the reliability degrees of the users calculated by the user reliability calculation section and predict evaluation values for the items by the user based on the weights of the similar users and evaluation values for the items by the similar users, the introduction section introducing those of the items to which it is predicted for the user of the information processing apparatus to provide high evaluation by the item evaluation prediction section as the associated items. 
     The information processing apparatus may further include an additional meta data application section (for example, an additional metal data calculation section  241  shown in  FIG. 4 ) configured to apply new meta data to the items based on the association degrees. 
     The information processing apparatus may further include a reliability degree calculation section (for example, the association degree reliability degree calculation section  231  shown in  FIG. 4 ) configured to calculate a reliability degree of the association degrees as information, the additional meta data application section applying new meta data to the items based on both of the association degrees and the reliability degrees of the association degrees. 
     The information processing apparatus may further include an item similarity degree calculation section (for example, an item similarity degree calculation section  243  shown in  FIG. 4 ) configured to calculate a similarity degree between the items using new meta data applied by the additional meta data application section, the introduction section introducing those of the items which have a high similarity degree to the reference item calculated by the item similarity degree calculation section as the associated items. 
     The information processing apparatus may further include a user liking calculation section (for example, the user liking calculation section  212  shown in  FIG. 4 ) configured to calculate a liking of users of the different information processing apparatus based on a result of the collection by the association degree collection section, and a comparison section (for example, an item-user liking matching section  245 ) configured to compare the new meta data for the items applied by the additional meta data application section with the likings of the users calculated by the user liking calculation section to determine a similarity degree therebetween, the introduction section introducing those of the items which are decided to have a high similarly degree to the likings of the users by the comparison by the comparison section as the associated items. 
     The information processing may further include an importance degree calculation section (for example, an item importance degree calculation section  251  shown in  FIG. 4 ) configured to calculate an importance degree of the items among all items based on the association degrees applied between the items. 
     The information processing apparatus may further include a correction section (for example, a presentation information correction section  254  shown in  FIG. 4 ) configured to correct a display form of information of the associated items to be introduced to the other information processing apparatus based on the importance degrees calculated by the importance degree calculation section, the introduction section causing any of the other different information processing apparatus which is a destination of the instruction to display the information of the associated items in the display form corrected by the correction section. 
     The information processing apparatus may further include an explanation application section (for example, a step S 269  of  FIG. 62 ) configured to apply an explanation of the associated items which is to be introduced to the other information processing apparatus to the information of the associated items based on the importance degrees calculated by the importance degree calculation section. 
     The information processing apparatus may further include a user liking calculation section configured to calculate a liking of users of the different information processing apparatus based on a result of the collection by the association value correction section, and an explanation application section (for example, a step S 288  of  FIG. 64 ) configured to apply an explanation of the associated items which is to be introduced to the other information processing apparatus to the information of the associated items based on the importance degrees calculated by the importance degree calculation section and the likings of the users calculated by the user liking calculation section. 
       FIG. 1  shows an example of a configuration of an item introduction system to which the present invention is applied. 
     Referring to  FIG. 1 , the item introduction system  1  shown includes a server  11  and a plurality of clients  12  and  13  connected to each other by a network  10 . In the item introduction system  1 , the server  11  introduces or recommends an item to the clients  12  and  13 . The item introduction system  1  is utilized, for example, a sales service of a content and so forth. 
     For example, a user of a client who wants to purchase some content would issue a request for information of contents to the server  11 , which is the sales side of contents, in order to select some content to be purchased. In accordance with the request, the server  11  introduces information of contents as items to the client of the requesting source. The user of the client would refer to the information of the introduced contents to decide whether or not the contents should be purchased. 
     In this manner, the item introduction system  1  is not only a recommendation system for introducing items but also a system which implements recommendation of a comparatively desirable item based not only on an express evaluation behavior of a user but also on an input of a relationship between items. Further, the item introduction system  1  can be applied widely because it uses an approach which is not restricted by a domain of a content (movie, music, web page or the like) which represents a relationship between items. 
     It is to be noted that a content here includes a medium content such as, for example, music, a movie, a TV program or a systematic writing as well as a commodity to be sold. 
     Meanwhile, an item is a unit of information presentation or recommendation in the item introduction system  1 , and the substance of an item depends upon a situation. For example, where the content is music, the item may be an artist, an album, a musical piece or the like, but where the content is a movie, the item may be a staff member such as a movie director, an actor or actress, a distributing agency or the like. In other words, an item includes not only an individual work or commodity but also a group which has a common attribute (meta data). 
     It is to be noted that meta data include all data which are added to or included in a content which does not rely upon a user who experiences the content such as, for example, where the content is music, a genre, a hometown or a year of debut of an artist, a word appearing in review writings, or a characteristic amount extracted from the content. 
     In  FIG. 1 , the item introduction system  1  is shown including a single server (server  11 ), one network (network  10 ), and two clients (clients  12  and  13 ). However, the number of networks, servers and clients is optional, and the item introduction system  1  may include a plurality of networks, a plurality of servers or three or more clients. Further, for example, part of processing of the server side may be assigned as processing between clients using the P2P technique or the like. 
     The network  10  may include one or a plurality of networks represented, for example, by the Internet or a LAN (Local Area Network). The network  10  may be a wire network or a wireless network or else a network which involves both of wire and wireless communication. 
     The server  11  provides a service of introducing an item to a client. Thereupon, the server  11  presents items associated with an item presented already or associated items which are associated with a reference item which may be an item designated by a client or the like. The server  11  manages not only information of items of an object of introduction but also information which represents associations of the items. Thus, the server  11  selects associated items from a reference item based on the information for associating the items with each other and presents the associated items to the client. It is to be noted that the association between items is performed based on a request (feedback) from the client. 
     Further, the server  11  calculates and utilizes the degree of reliability of the information for associating the items and the users or produces and utilizes a new parameter through application of the relationship between the items. Further, the server  11  calculates and utilizes the degree of importance of the individual items based on the association of the items. 
     The individual clients operate independently of each other and designate items to be associated with each other or a weight of the association (hereinafter referred to as association degree) to the server  11 . Further, each client issues, independently of such processing as just described, a request for presentation of items so that items relating to the items presented already or items relating to a designated item may be presented to the client. 
     In the following description, it is assumed that the client  12  performs association of items and the client  13  receives presentation of items. Actually, since a process of performing association of items and a process of receiving presentation of items are independent of each other, one client may perform both of the processes or may perform only one of the processes. Further, each process may be executed at an arbitrary timing. 
     In the following, roles of each apparatus in the item introduction system  1  are described in more detail. 
     The client  12  which performs association of items issues, designating a relationship between items and a degree of association, a request to the server  11  to apply a desired association degree to the relationship between the desired items as indicated by a broken line arrow mark  21 . The server  11  includes a database for managing information relating to association between items and updates the database based on the request from the client  12  and applies an association degree to the relationship of the designated items to establish association between the items. 
       FIG. 2  schematically illustrates relationships relating to association between items. Referring to  FIG. 2 , a plurality of items  31  to  35  are each used as an object of introduction prepared in advance, and each of arrow marks  41  to  48  indicates a relationship between items whose association degree is registered by the client  12 . 
     As indicated by the arrow marks  41  to  48 , each two ones of the items  31  to  35  may bidirectionally have a relationship which is directed from one to the other of the two. For example, the item  31  and the item  35  have a relationship indicated by the arrow mark  42  which is directed from the item  31  to the item  35  and another relationship indicated by the arrow mark  48  which is directed from the item  35  to the item  31 . 
     An association degree is applied to a relationship between items in which a direction indicated by an arrow mark in this manner is taken into consideration. Each of the arrow marks  41  to  48  shown in  FIG. 2  indicates a relationship between items to which an association degree is applied in this manner. 
     For example, if a relationship which is directed from the item  31  to the item  32  and an association degree “3” are designated by the client  12 , then the server  11  applies the association degree “3” to the relationship indicated by the arrow mark  41  which is directed from the item  31  to the item  32 . At this time, the server  11  registers the association degree “3” as an association degree applied by the client  12 . In other words, an association degree is applied to one relationship (arrow mark) for each client. It is to be noted that, as occasion demands, the server  11  calculates the sum total, an average value or the like of applied association degrees for each relationship (arrow mark) between items. 
     Referring back to  FIG. 1 , the client  13  to which an item is to be presented issues a presentation request for the item or for relating items to the server  11  as indicated by the arrow mark  22 . The server  11  searches such information relating to the association between items as illustrated in  FIG. 2  for the designated item or items relating to items presented already in accordance the request received from the client  13 . Then, the server  11  presents the information relating to the items to the client  13  as indicated by the arrow mark  23 . 
     For example, a method of searching, when a user of a client tries to purchase some content in a content sales service, a large number of contents prepared in advance for the content to be purchased at random not only requires complicated operations for the user but also does not always assure success in finding of a satisfactory content which the user may want to purchase. Therefore, the method described may possibly reduce the motivation for purchase of the user. 
     Or, even if a dealer of contents introduces recommendable contents to a user, the contents selected by the dealer may not necessarily be favorable to the user. Therefore, the item introduction system  1  urges a user to apply an association degree between items which are information relating to contents. The server  11  grasps liking of the user (or all users) through this act and, for example, when a certain item is presented to a certain user, searches for associated items based on association between items. Then, the server  11  presents information of the item designated by the user and simultaneously presents information of associated items associated with the item designated by other users or the user itself. 
     Consequently, each user can refer to presented associated items without conducting a search by itself and can easily expand the width of contents whose purchase may be investigated. As a result, the user can easily purchase a greater amount of contents satisfactory to the user itself. 
     Since application of an association degree is independent of actual purchase, each user can apply an association degree freely without actually purchasing a content or freely within a predetermined restricted range after effective management of the service is started. Accordingly, application of a large amount of application of association degrees can be expected from an initial stage after the service is started. For example, where a reward is paid, upon purchase of a content arising from such item introduction, to users who have applied an association degree to a relationship to items which contributes to the purchase, if the amount of the reward to be paid to each user is determined so as to increase in inverse proportion to the number of those users who applied the association degree, then it can be expended that the application of an association degree upon starting of the service is activated as much. 
     In this manner, the server  11  of the item introduction system  1  introduces items to users of clients. 
     The item introduction system  1  may be utilized for any service such as, for example, a sales service of a content, a download service of a content, a rental service or an access service. Further, the item introduction system  1  itself may establish an introduction service. 
     The following description proceeds under the assumption that the item introduction system  1  is utilized for a sales service of contents and introduces information relating to the contents as items for the simplified description. 
       FIG. 3  shows an example of an internal configuration of the server  11 . 
     Referring to  FIG. 3 , the server  11  shown includes a central processing unit (CPU)  101  which executes various processes based on a program stored in a read only memory (ROM)  102  or a program loaded from a storage section  113  into a random access memory (RAM)  103 . Also data and so forth necessary for the CPU  101  to execute various processes are stored suitably into the RAM  103 . 
     The CPU  101 , ROM  102  and RAM  103  are connected to each other by a bus  104 . Also an input/output interface  110  is connected to the bus  104 . 
     An inputting section  111  which may include a keyboard and a mouse, an outputting section  112  including a display unit which may be a cathode ray tube (CRT) or a liquid crystal display (LCD) unit, a speaker and so forth, a storage section  113  which includes a hard disk and so forth, and a communication section  114  formed from a modem or the like are connected to the input/output interface  110 . The communication section  114  performs a communication process through a network including the Internet. 
     A drive  115  is connected to the input/output interface  110  as occasion demands, and a removable medium  121  such as a magnetic disk, an optical disk, a magnetic optical disk, a semiconductor memory or the like is loaded suitably into the input/output interface  110 . Thus, a computer program read out from the removable medium  121  loaded in the drive  115  is installed into the storage section  113  as occasion demands. 
       FIG. 4  illustrates functions of the CPU  101  which executes a program. 
     Referring to  FIG. 4 , the CPU  101  includes an association degree collection section  201 , a content based filtering processing section  202 , a collaborative filtering processing section  203 , a reliability degree processing section  204 , an additional meta data processing section  205  and an item importance degree processing section  206 . The association degree collection section  201  accepts an association degree supplied from the client  12  and allocates the association degree to a relationship between items. The content based filtering processing section  202  specifies similar items which match with the liking of a user based on meta data of a content and presents the similar items as associated items. The collaborative filtering processing section  203  predicts evaluation of an item from a behavior of similar users without using meta data and presents associated items based on a result of the prediction. The reliability degree processing section  204  performs processes relating to an association degree or a reliability degree of a user. The additional meta data processing section  205  produces new meta data based on an applied association degree and performs presentation of associated items based on the meta data. The item importance degree processing section  206  calculates an importance degree of items and performs presentation of the items based on the importance degrees. 
     The content based filtering processing section  202  includes an information presentation request processing section  211  for accepting a request from the client  13 , a user liking calculation section  212  for calculating a liking of a user, and an item-user liking matching section  213  for performing matching of a liking of a user and items. The content based filtering processing section  202  further includes an item information presentation processing section  214  for performing introduction of associated items to the user of the client  13 , and an item explanation application section  215  for applying, to the information of an item to be presented, an explanation relating to the item. 
     The collaborative filtering processing section  203  includes an information presentation request processing section  221  for accepting a request from the client  13 , a user similarity degree calculation section  222  for calculating a similarity degree between users, and an item evaluation prediction section  223  for preventing evaluation of a new item of a user. The collaborative filtering processing section  203  further includes an item information presentation processing section  224  for performing introduction of associated items to the user of the client  13 , and an item explanation application section  225  for applying, to the information of items to be presented, explanations relating to the items. 
     The reliability degree processing section  204  includes an association degree reliability degree calculation section  231  for calculating a reliability degree of an association degree, and a user reliability degree calculation section  232  for calculating a reliability degree of a user. 
     The additional meta data processing section  205  includes an additional meta data calculation section  241  for performing an analysis based on association degrees between items to produce new meta data, and an information presentation request processing section  242  for accepting a request from the client  13 . The additional meta data processing section  205  further includes an item similarity degree calculation section  243  for calculating a similarity degree of items based on newly added meta data. The additional meta data processing section  205  further includes an item information presentation processing section  244  for introducing associated items to the user of the client  13  and an item-user liking matching section  245  for performing matching of a liking of a user and items. 
     The item importance degree processing section  206  includes an item importance degree calculation section  251  for calculating an importance degree of an item, and an information presentation request processing section  252  for accepting a request from the client  13 . The item importance degree processing section  206  further includes a presentation information production section  253  for producing presentation information to be presented to the user of the client  13 . The item importance degree processing section  206  further includes a presentation information correction section  254  for correcting presentation information, and an item information presentation processing section  255  for performing introduction of items to the user of the client  13 . 
       FIG. 5  illustrates an example of an internal configuration of the storage section  113  shown in  FIG. 3 . 
     Referring to  FIG. 5 , the storage section  113  includes an inter-item association degree database  301  for associating items registered in the client  12  with each other and storing and managing such association degrees of the items. The storage section  113  further includes a user information database  302  for storing and managing information relating to the user of the client  12  which registers an association degree, and an item information database  303  for storing and managing information items to be introduced. 
     Particular processes of the item introduction system  1  are described below. 
     Basic processes to be executed by the item introduction system  1  according to the present invention can be roughly divided into association degree application and item recommendation. First, an example of a flow of the association degree application process which is executed for the client  12  by the server  11  is described with reference to FIG.  6 ., and  FIG. 7  as occasion demands. 
     After the association degree application process is started, the association degree collection section  201  of the server  11  accepts association degree evaluation at step S 1 . As seen in  FIG. 7 , the client  12  designates a combination of items to be associated with each other through the network  10  and issues a request to apply an association degree between the items (arrow mark  401 ). The association degree collection section  201  of the server  11  acquires the request from the user. It is to be noted that, in the following description, the association degree applied between an item  0   i  and another item  0   j  by a user u is represented by w u   ij . 
     At this time, w u   ij  is an index representing that a higher value thereof indicates a higher association degree. For the value of w u   ij , predetermined domains common to the system such as, for example, integral values of “1” to “5” may be provided or an arbitrary real number may be provided without particularly providing a limitation thereto. 
     When the association degree evaluation is accepted, the association degree collection section  201  performs point limitation decision at step S 2 . In particular, the association degree collection section  201  acquires a currently possessed point of the user of the requesting source for the association degree request from the user information database  302  as seen in  FIG. 7  (arrow mark  402 ) and decides whether or not the association degree application by the current request has a value exceeding the point. 
     If application of an association degree is permitted unconditionally, then there is the possibility that, for example, each user may apply the highest association degree between all items. In such an instance, the associations between items may become useless to item introduction and the server  11  may come to fail to recommend a comparatively appropriate item to the user of the client  13 . 
     Therefore, in order to prevent application of such a random appreciation degree by a user as just described, the server  11  manages the total value of association degrees applied by each user as a user point and limits the total value of association degrees which can be applied by the user. In particular, the server  11  allocates a predetermined user point number to each user in advance and decrements the user point every time the user applies an association degree while inhibiting such application of an association degree which makes the user point negative. 
     After such limitation decision regarding the user point is performed, the association degree collection section  201  decides at step S 3  whether or not the limitation is exceeded. If the association degree collection section  201  decides that the limitation is not exceeded, then the processing advances to step S 4 , at which the association degree collection section  201  updates the association degree in accordance with the request from the client  12 . In particular, the association degree collection section  201  updates the inter-item association degree database  301  so as to apply the association degree between the items according to the request as seen in  FIG. 7  (arrow mark  403 ). Thereupon, the association degree is retained in such a matrix form that association degrees from the item  0   i  to the item  0   j  make components of i rows and j columns although the association degrees may actually be retained in any data structure in the database. 
     The inter-item association degree database  301  stores association degrees between items as values for individual users by whom the association degrees are applied and further stores total values and/or average values or the like as common values to the users of all clients of the item introduction system  1 . For example, where a sum total T of association degrees applied by all users between certain items is calculated and stored, the inter-item association degree database  301  calculates the sum total T using the following expression (1): 
     
       
         
           
             
               
                 
                   T 
                   = 
                   
                     
                       ∑ 
                       u 
                     
                     ⁢ 
                     
                       ( 
                       
                         w 
                         ij 
                         u 
                       
                       ) 
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     Meanwhile, for example, where an average value M of association degrees applied by all users between certain items is calculated and stored, the inter-item association degree database  301  uses the following expression (2) to calculate the average value M: 
     
       
         
           
             
               
                 
                   M 
                   = 
                   
                     
                       ∑ 
                       u 
                     
                     ⁢ 
                     
                       
                         ( 
                         
                           w 
                           ij 
                           u 
                         
                         ) 
                       
                       / 
                       
                          
                         Uij 
                          
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     where |Uij| is the number of users by whom an association degree between the item  0   i  and the item  0   j  is applied. 
     It is to be noted that the relationship between the item  0   i  and the item  0   j  may be regarded as a symmetrical relationship such that a common association degree may be applied to the ij component (a relationship wherein the item  0   i  is the association source and the item  0   j  is the association destination) and the ji component (a relationship wherein the item  0   j  is the association source and the item  0   i  is the association destination). Or, the relationship between the item  0   i  and the item  0   j  may be regarded as an unsymmetrical relationship such that different association degrees are applied to the ij component and the ji component independently of each other, that is, the degree by which the item  0   j  is associated with the item  0   i  and the degree by which the item  0   i  is associated with the item  0   j  are different from each other. 
     After the inter-item association degree database  301  is updated, the association degree collection section  201  decrements the user point at step S 5 . In particular, as seen in  FIG. 7 , the association degree collection section  201  updates the user information database  302  so as to decrement the user point of the user by whom the association degree is applied by the applied value of the association degree (arrow mark  404 ). After the user information database  302  is updated, the association degree collection section  201  ends the association degree application process. 
     On the other hand, if the association degree collection section  201  decides at step S 3  that the limitation is exceeded, for example, the user point becomes negative, in the user point limitation decision at step S 2 , then it ends the association degree application process immediately without applying the requested association degree. 
     The user point described above may be any point only if it limits application of an association degree by a user, and what entity is provided particularly to the user point or in what manner the user point is managed may be determined arbitrarily. This depends, for example, upon the configuration of the system by which the present invention is implemented or the substance of a service. Further, if it can be anticipated that random application of an association degree by any user is not performed, then such point limitation as described above can be omitted. 
     As described above, the item introduction system  1  performs recommendation of a content based on a history of application of an association degree by a user assuming that the act of applying an association degree by the user indicates a liking of the user. For item recommendation to a user, a method which is based on a short period context (situation of information accessing or the like) in the situation and another method which is based on a long period liking of a user are available. It is to be noted that a liking of a user can be represented by an average vector of meta data of contents enjoyed (and decided to be favorable) by the user till then as disclosed in Patent Document 1 mentioned hereinabove or a nature of a user (for example, whether the user likes a best-selling content or a minor content or the like). The nature of a user is acquired, for example, by causing the user to input it in advance or by deciding the nature based on a tendency and/or transition of the history of the user. 
     Meanwhile, methods for item recommendation to a user can be technically divided into content based filtering based on meta data and collaborative filtering which is based on estimation of similar behaviors of a user without using meta data. 
     Or, it is possible to apply weighting to an item based on application of association degrees by a user. In this instance, for example, if an association degree from the item  0   i  to the item  0   j  is applied, then a higher evaluation may be provided to the item  0   i  based on an assumption that a greater amount of information may be applied to a comparatively favorable item, or conversely a higher evaluation may be applied to the item  0   j  based on another assumption that an association degree may be applied so that information may flow to a favorable item. Or otherwise, a high evaluation degree may be applied to both of the item  0   i  and the item  0   j  based on a further assumption that both of the item  0   i  and the item  0   j  are favorable to the user. 
     Now, a recommendation method of an item to a user is described. First, description is given of recommendation of an item carried out by content based filtering of representing, where meta data are applied to individual contents, a liking of a user by a sum total or an average of meta data of contents enjoyed by the user and determining whether or not a content should be recommended to the user based on the inner product or the cosine similarity degree of a liking of the user and an unknown content. 
     An example of a flow of a presentation process to be executed by the server  11  for introducing an item to the client  13  by content based filtering is described with reference to  FIG. 8 . Also  FIGS. 9 to 15  are referred to as occasion demands. 
     After the presentation process is started, the information presentation request processing section  211  of the content based filtering processing section  202  accepts an information presentation request at step S 21 . As seen in  FIG. 9 , the client  13  uses a Web browser to issue a request for presentation of information relating to a list of items conforming to liking of the user to the network  10  as indicated by an arrow mark  411 . After the request is accepted, the information presentation request processing section  211  issues a notification of the request to the user liking calculation section  212  as indicated by an arrow mark  412 . 
     When the notification is accepted, the user liking calculation section  212  acquires information representative of the user and items necessary to calculate a liking of the user of the client  13  which is the source of the request at step S 22  of  FIG. 8 . For example, the user liking calculation section  212  accesses the inter-item association degree database  301  to acquire information of the association degrees applied by the user of the client  13  (arrow mark  413  in  FIG. 9 ). Then, the user liking calculation section  212  accesses the content based filtering processing section  202  to acquire information regarding the user of the client  13  as indicated by an arrow mark  414  of  FIG. 9 . Further, the user liking calculation section  212  accesses the item information database  303  to acquire information of the items whose association degree is applied by the user of the client  13 , that is, the items on the opposite sides to the item whose association degree is applied as indicated by an arrow mark  415  of  FIG. 9 . 
     After the necessary information is acquired, the user liking calculation section  212  calculates a liking of the user based on the information at step S 23 . For example, meta data of a content corresponding to the item is represented by a vector, and a liking of the user is represented as the sum of such vectors similarly, for example, as in the case of the method disclosed in Patent Document 1. Thereupon, the user liking calculation section  212  takes such measures as to multiply an item to which only an association degree is applied by a weight lower than that applied to an item to which an evaluation of “favorable” is provided expressly to determine a liking vector UP u  of the user u in accordance with the following expression (3): 
     
       
         
           
             
               
                 
                   
                     UP 
                     u 
                   
                   = 
                   
                     
                       
                         ∑ 
                         
                           i 
                           ∈ 
                           
                             E 
                             u 
                             * 
                           
                         
                       
                       ⁢ 
                       
                         v 
                         l 
                       
                     
                     + 
                     
                       
                         λ 
                         f 
                       
                       ⁢ 
                       
                         
                           ∑ 
                           
                             i 
                             ❘ 
                             
                               
                                 w 
                                 ij 
                               
                               ∈ 
                               
                                 W 
                                 u 
                               
                             
                           
                         
                         ⁢ 
                         
                           v 
                           i 
                         
                       
                     
                     + 
                     
                       
                         λ 
                         t 
                       
                       ⁢ 
                       
                         
                           ∑ 
                           
                             j 
                             ❘ 
                             
                               
                                 w 
                                 ij 
                               
                               ∈ 
                               
                                 W 
                                 u 
                               
                             
                           
                         
                         ⁢ 
                         
                           v 
                           j 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
     where EU +  indicates a set of contents to which an evaluation of “favorable” is provided by the user u expressly, and λ f  indicates the weight to the association source item while λ t  indicates the weight to the association destination item. It is to be noted that the liking vector UP u  of the user u may be a weighted average of values determined by the right side of the expression (3). 
     After a liking of the user of the client  13  is calculated as a vector in this manner, the user liking calculation section  212  supplies a result of the calculation to the item-user liking matching section  213  as indicated by an arrow mark  416  of  FIG. 9 . The item-user liking matching section  213  performs matching of the item and the user liking at step S 24 . The item-user liking matching section  213  acquires information regarding all or some of the items stored in the item information database  303  as indicated by an arrow mark  417  of  FIG. 9  and converts the items into vectors based on the information. Then, the item-user liking matching section  213  decides the compatibility of the liking of the user calculated as the liking vector UP u  by the user liking calculation section  212 , that is, whether or not the vectors match with each other. 
     For example, where the vector representation of the liking of the user u is UP u  and the vector representation of the item  0   i  is vi, the item-user liking matching section  213  calculates the cosine similarity degree Sc in accordance with the following expression (4):
 
 Sc=UP   u   ·v   i   /|UP   u   ∥v   i |  (4)
 
where “·” indicates the inner product. The item-user liking matching section  213  lines up the items depending upon the magnitude of the value of the cosine similarity degree Sc calculated in such a manner as described above and decides N (N is an arbitrary integral number) items which have comparatively high values as items which match with the liking of the user of the client  13 , that is, as items which are nearer to the liking of the user than the other items and whose possibility that they may be favorable to the user is high. It is to be noted that the calculation of the similarity degree between an item and a user liking may be performed not only by the method of the cosine similarity degree described hereinabove but also by any technique such as, for example, by a method using a Euclid distance, an intercity distance or the like.
 
     The item-user liking matching section  213  supplies a result of the matching decided as described above, that is, a list of items decided as matching with a liking of the user and arrayed in accordance with the similarity degree as indicated by an arrow mark  418  in  FIG. 9 . The item information presentation processing section  214  produces presentation information at step S 25  and presents the presentation information to the user of the client  13  at step S 26 . The item information presentation processing section  214  shapes the list of items supplied thereto from the item-user liking matching section  213  in order to present the list to the user to the storage section  113  to form presentation information and supplies the presentation information to the client  13  through the network  10  so as to be presented to the user as indicated by an arrow mark  419  of  FIG. 9 . 
     After the presentation information is presented, the item information presentation processing section  214  ends the presentation process. 
     For example, it is assumed that a liking vector UP u  of the user u produced by evaluation performed by the user u for items of a content having five meta data is calculated as in a table shown in  FIG. 10  and vectors of new items (items  10  to  13 ) which may make an object of recommendation are given as seen in a table shown in  FIG. 11 . In the tables of  FIGS. 10 and 11 , “meta  1 ” to “meta  5 ” represent meta data of the content different from each other. In this instance, the Euclid distance from the liking vector UP u  from each of the items illustrated in the table of  FIG. 11  is such as illustrated in a table shown in  FIG. 12 . 
     Further, it is assumed that items  1  to  4  have five data of “meta  1 ” to “meta  5 ” as seen in a table shown in  FIG. 13 . If a user having the liking vector UP u  applies an association degree to a relationship from the item  4  to the item  2  from among the items mentioned and the relationship of the items is fetched with weights of λ f =0.5 and λ t =0.3 into the liking vector UP u , then the liking vector UP u  is modified to a liking vector UP u ′ using the following expression (5) and such a table as seen in  FIG. 14  is obtained:
 
 UP   u ′=(5* UP   u +0.5 *v 4+0.3 *v 2)/5.8  (5)
 
     Consequently, the Euclid distance between a new item (items  10  to  13 ) which makes an object of recommendation and the liking vector (modified liking vector UP u ′) has such values as seen in a table shown in  FIG. 15 . In the examples of  FIGS. 12 and 15 , the priority degree of recommendation of the item  10  and the item  11  varies by modification to the user liking. 
     By this, even if the user of the client  13  does not perform a search, since associated items more conforming to the liking of the user itself are presented, contents with which the user is satisfied readily can be purchased by a greater amount. In short, the server  11  can present useful information to the user of the client  13 . 
     Now, collaborative filtering of recommending an unknown content to a user using an predictive evaluation value based on a similarity in content evaluation between users without utilizing meta data of contents is described as another example of the recommendation method of an item to a user. In this instance, the server  11  searches for similar users based on the tendency in application of an association degree and recommends those items to which the similar users provide comparatively high evaluation (express evaluation or high association degree) to the user of the client  13 . 
     An example of a flow of a presentation process executed by the server  11  for introducing items to the client  13  by collaborative filtering is described with reference to  FIG. 16 . Also  FIGS. 17 to 22  are referred to as occasion demands. 
     After the presentation process is started, the information presentation request processing section  221  of the collaborative filtering processing section  203  accepts an information presentation request at step S 41 . Referring to  FIG. 17 , the client  13  uses the Web browser to issue a request for presentation of information regarding a list of items which conform to the liking of the user to the network  10  as indicated by an arrow mark  421 . After the request is accepted, the information presentation request processing section  221  issues a notification of the request to the user similarity degree calculation section  222  as indicated by an arrow mark  422 . 
     When the notification is received, the user similarity degree calculation section  222  determines similar users at step S 42 . In particular, the user similarity degree calculation section  222  accesses the inter-item association degree database  301  to acquire information of the association degree applied by the user of the client  13  and other users as indicated by an arrow mark  423  of  FIG. 17 . Then, the user similarity degree calculation section  222  calculates a correlation coefficient r uu ′ between the association degree applied by the user of the client  13  and the association degree applied by each of the other users u′ based on the information of the association degree in accordance with the following expressions (6) and (7): 
     
       
         
           
             
               
                 
                   
                     r 
                     
                       uu 
                       * 
                     
                   
                   = 
                   
                     
                       
                         ∑ 
                         
                           i 
                           , 
                           
                             j 
                             ❘ 
                             
                               
                                 w 
                                 ij 
                               
                               ∈ 
                               
                                 
                                   W 
                                   u 
                                 
                                 ⋂ 
                                 
                                   W 
                                   
                                     u 
                                     ′ 
                                   
                                 
                               
                             
                           
                         
                       
                       ⁢ 
                       
                         
                           ( 
                           
                             
                               w 
                               ij 
                               u 
                             
                             - 
                             
                               
                                 w 
                                 _ 
                               
                               u 
                             
                           
                           ) 
                         
                         ⁢ 
                         
                           ( 
                           
                             
                               w 
                               ij 
                               
                                 u 
                                 ′ 
                               
                             
                             - 
                             
                               
                                 w 
                                 _ 
                               
                               
                                 u 
                                 ′ 
                               
                             
                           
                           ) 
                         
                       
                     
                     
                       
                          
                         
                           
                             W 
                             u 
                           
                           ⋂ 
                           
                             W 
                             
                               u 
                               ′ 
                             
                           
                         
                          
                       
                       ⁢ 
                       
                         σ 
                         u 
                       
                       ⁢ 
                       
                         σ 
                         
                           u 
                           ′ 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
             
               
                 
                   
                     σ 
                     u 
                   
                   = 
                   
                     
                       
                         1 
                         
                            
                           
                             W 
                             u 
                           
                            
                         
                       
                       ⁢ 
                       
                         
                           ∑ 
                           
                             i 
                             , 
                             
                               j 
                               ❘ 
                               
                                 
                                   w 
                                   ij 
                                 
                                 ∈ 
                                 
                                   W 
                                   u 
                                 
                               
                             
                           
                         
                         ⁢ 
                         
                           
                             ( 
                             
                               
                                 w 
                                 ij 
                                 u 
                               
                               - 
                               
                                 
                                   w 
                                   _ 
                                 
                                 u 
                               
                             
                             ) 
                           
                           2 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
     The user similarity degree calculation section  222  decides those N (N is an arbitrary natural number) users u′ whose correlation coefficient r uu ′ calculated in this manner is comparatively high as users who are high in similarity to the user u, that is, as similar users. It is to be noted that alternatively a user u′ whose value of the correlation coefficient r uu ′ is higher than a predetermined value determined in advance may be determined as a user having a high degree of similarity to the user u. Or, a function which monotonously decreases in response to the Euclid distance or the cosine distance between association degrees applied commonly may be used as a similarity degree between the users in place of the correlation coefficient r uu ′ calculated using the expressions (6) and (7). 
     After users u′ having a high similarity degree to the user u are determined, the user similarity degree calculation section  222  further specifies items to which the user u′ provides high evaluation (express evaluation or high degree of association) based on the association degrees. For example, the user similarity degree calculation section  222  determines, for each item, an average value of association degrees (or expression evaluation values) applied by the user u′ and specifies N (N is an arbitrary natural number) items which have a comparatively high average value as items to which a comparatively high evaluation is provided by the user u′, that is, as recommendation items to be recommended to the user u. Alternatively, those items whose average value is higher than a predetermined threshold value determined in advance may be specified as recommendation items. 
     After users u′ having a high similarity degree to the user u, recommendation items to which the user u′ provides high evaluation and average values of evaluation values (or similarity degrees) provided by the users u′ to the recommendation items are determined in this manner, the user similarity degree calculation section  222  supplies the determined information to the item evaluation prediction section  223  as indicated by an arrow mark  424  of  FIG. 17 . The item evaluation prediction section  223  performs weighted averaging of the similarity degrees between the users to estimate evaluation of the recommendation items of the user u at step S 43 . In particular, the item evaluation prediction section  223  accesses the user information database  302  to acquire an average value of the evaluation values for each item provided by the user u as indicated by an arrow mark  425  in  FIG. 17 . The item evaluation prediction section  223  uses an average value of the evaluation values provided by the user u, an average value of the evaluation values provided by each user u′ for each recommendation item and the correlation coefficient r uu ′ to perform arithmetic operation in accordance with the following description ( 8 ) to predict a prediction evaluation value for the recommendation items of the user u: 
                       e   ^     ui     =       e   u     +         ∑     u   ′       ⁢       r     uu   ′       ⁡     (       e       u   ′     ⁢   i       -       e   _       u   ′         )             ∑     u   ′       ⁢     r     uu   ′                     (   8   )               
where ê ui  is the predictive evaluation value for the recommendation item i of the user u, ē u  the average value of the evaluation values of the user u, and e u′i  the average value of the evaluation values of the users u′ for the recommendation item i.
 
     It is to be noted that, for example, a correlation coefficient of the association degree and a weighted average of correlation coefficients of evaluation values or the like may alternatively be used to predict a predictive evaluation value for a recommendation item i of the user u. 
     After the item evaluation prediction section  223  predicts a predictive evaluation value for the recommendation item i of the user u as described above, it supplies the predictive evaluation value to the item information presentation processing section  224  as indicated by an arrow mark  426  of  FIG. 17 . When the predictive evaluation value is acquired, the item information presentation processing section  224  produces presentation information at step S 44  and presents the presentation information at step S 45 . In particular, the item information presentation processing section  224  accesses the item information database  303  to acquire information of each recommendation item as indicated by an arrow mark  427  of  FIG. 17  and produces a list of the recommendation items. Then, the item information presentation processing section  224  shapes the list of the recommendation items in response to the predictive evaluation values supplied from the item evaluation prediction section  223  in order to present the list of the presentation items thereby to produce presentation information. Then, the item information presentation processing section  224  supplies the presentation information to the client  13  through the network  10  so that it is presented to the user as indicated by an arrow mark  428  of  FIG. 17 . 
     After the presentation information is presented, the item information presentation processing section  224  ends the presentation process. 
     An example of the presentation process where the number of users is three (users u 1  to u 3 ) is described more particularly. It is assumed that the user u 1  applies an association degree between items (w) as seen in a table shown in  FIG. 18 ; the user u 2  applies an association degree between items (w) as seen in a table shown in  FIG. 19 ; and the user u 3  applies an association degree between items (w) as seen in a table shown in  FIG. 20 . At this time, such association degrees as illustrated in a table of  FIG. 21  are applied by the users between those items between which an association degree is applied by the user u 1 . In the table shown in  FIG. 21 , for example, w 31  indicates a direction from the item  3  toward the item  1  between the item  3  and the item  1 . 
     Accordingly, the correlation coefficients of the user u 2  and the user u 3  to the user u 1  are such as illustrated in a table shown in  FIG. 22 . Here, if the evaluation of a new item by the user u 2  is “5” and that by the user u 3  is “2” while the average value of evaluation values provided by the user u 1  is “2.9” and those by the user u 2  and the user u 3  are “3.1” and “3.3”, respectively, then the predictive evaluation value EV u1  of the user u 1  is calculated in accordance with the following expression (9):
 
 EV   u1 =2.9+(0.891*(5−3.1)−0.45*(2−3.3))/(0.891+0.45)=4.599  (9)
 
     The server  11  predicts an evaluation value of the user u for recommendation contents to which similar users who are similar to the user of the client  13  provide high evaluation, shapes a list of the recommendation contents based on the predictive values and presents the list to the user of the client  13 . By this, even if the user of the client  13  by itself does not perform a search, since associated items suitable for the liking of the user itself are presented, the user can readily purchase a greater number of contents with which the user is satisfied. In other words, the server  11  can present useful information to the user of the client  13 . 
     Further, the server  11  may set, to an association degree between items or to a user, a reliability degree by which the association degree or the user is satisfactory. 
     First, a method of calculating the degree of reliability of an association degree applied between items is described. This is a value common to all users. 
     An example of a flow of an association degree reliability degree setting process executed by the association degree reliability degree calculation section  231  of the reliability degree processing section  204  in order to calculate the association degree of the reliability degree which is a degree of reliability with regard to the reliability degree applied between items is described with reference to  FIG. 23 . Further, reference is had also to  FIGS. 24 to 26  as occasion demands. 
     The association degree reliability degree calculation section  231  of the reliability degree processing section  204  acquires the association degree between the item  0   i  and the item  0   j  from the inter-item association degree database  301  as indicated by an arrow mark  431  in  FIG. 24  at step S 61 . At step S 62 , the association degree reliability degree calculation section  231  calculates the reliability degree based on a dispersion of the association degrees. Then at step S 63 , the association degree reliability degree calculation section  231  stores the calculated reliability degree into the inter-item association degree database  301  as indicated by an arrow mark  432  and then ends the association degree reliability setting process. 
     For example, if a plurality of users apply an association degree between the same items and there is a limitation to the value of the association degree, then the association degree reliability degree calculation section  231  calculates the dispersion value σ ij   2  in accordance with the following expression (10) in order to determine the dispersion of the association degree applied between the same items: 
     
       
         
           
             
               
                 
                   
                     σ 
                     ij 
                     2 
                   
                   = 
                   
                     
                       1 
                       
                          
                         
                           U 
                           ij 
                         
                          
                       
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           u 
                           ∈ 
                           
                             U 
                             ij 
                           
                         
                       
                       ⁢ 
                       
                         
                           ( 
                           
                             
                               w 
                               ij 
                               u 
                             
                             - 
                             
                               
                                 w 
                                 _ 
                               
                               ij 
                             
                           
                           ) 
                         
                         2 
                       
                     
                   
                 
               
               
                 
                   ( 
                   10 
                   ) 
                 
               
             
           
         
       
     
     Then, the association degree reliability degree calculation section  231  calculates the reliability degree of the dispersion of the reliability degree of the combination of the items among the users such as a reciprocal number of the dispersion value σ ij   2  or an exponential function exp (−σ ij   2 ) utilizing a monotonously decreasing function. For example, if it is assumed that an association degree is applied as seen in a table shown in  FIG. 25  by all users, then such reliability degrees of the association degrees determined utilizing an exponential function as seen in a table shown in  FIG. 26  are obtained. It is to be noted that, where a reliability degree is calculated in this manner, it is necessary to store the values of the reliability degree by the individual users into the inter-item association degree database  301 . 
     Now, calculation of a reliability of a user is described. The reliability of a user can be represented, for example, by a displacement of the tendency of the user from the tendency of the association degree of all users. 
     Now, an example of a flow of a user reliability degree setting process for calculating the reliability degree of a user is described with reference to a flow chart of  FIG. 27 . In the following description, also  FIGS. 18 ,  25  and  28  to  31  are referred to as occasion demands. 
     After the user reliability degree setting process is started in order to calculate the reliability degree of a certain user, the user reliability degree calculation section  232  accesses the inter-item association degree database  301  as indicated by an arrow mark  441  in  FIG. 28  at step S 81  to acquire all reliability degrees applied between each two items. Then at step S 82 , the user reliability degree calculation section  232  calculates the reliability degree of the association degree applied by the user between each two items based on a displacement of the association degree from the tendency of the association degree of all users. 
     For example, where the association degree by the user u of the reliability degree between the item  0   i  and the item  0   j  is represented by w u   ij , the user reliability degree calculation section  232  first calculates an average value of absolute values of deviations from the association degree using the following expression (ii): 
                       d   _     u     =       1          W   u            ⁢       ∑     i   ,     j   ❘       w   ij     ∈     W   u             ⁢              w   ij   u     -       w   _     ij                  ∑   u     ⁢         (       w   ij   u     -       w   _     ij       )     2     /          U   ij                              (   11   )               
where  d   u  is the average of absolute values of deviations from the association degree,  w   ij  is the average of association degrees of the item  0   i  and the item  0   j , W u  is a set of association degrees applied by the user u, and |W u | is the number of the association degrees. Then, the user reliability degree calculation section  232  calculates the reliability degree C u  of the user u using the following expression (12):
 
     
       
         
           
             
               
                 
                   
                     c 
                     u 
                   
                   = 
                   
                     1 
                     
                       
                         d 
                         _ 
                       
                       u 
                     
                   
                 
               
               
                 
                   ( 
                   12 
                   ) 
                 
               
             
           
         
       
     
     For example, if the user u applies association degrees as indicated by the table shown in  FIG. 18  and all users including the user u add the association degrees as seen in a table shown in  FIG. 25 , then such averages of the association degrees added by all users between the items as seen in  FIG. 29  are obtained. Further, such standard deviations of the association degrees applied by all users between the items as seen in  FIG. 30  are obtained. Furthermore, such deviations of the association degrees added by the user u as seen in a table shown in  FIG. 31  are obtained. As a result, the value of the reliability degree CU of the user u is calculated in accordance with the following expression (13):
 
 C   u =(0.408+1.414+0.816+1.414+0+1)/6=0.842  (13)
 
     It is to be noted that the calculation method described above is a mere example, and any calculation method other than the method described above may be used only if it provides a lower reliability degree as the displacement from the tendency of the entirety increases. 
     After the reliability degree of the user is calculated as described above, the user reliability degree calculation section  232  stores the reliability degree calculated for the user into the user information database  302  as indicated by an arrow mark  442  in  FIG. 28 . Thereafter, the user reliability degree setting process is ended. 
     It is to be noted that, although the reliability degree of the association degree and the reliability degree of the user described above may be calculated independently of each other in accordance with such methods as described above, alternatively one of them may be determined, whereafter the determined value is used to determine the other of them. 
     Where a reliability degree of an association degree is calculated based on the reliability of each user, the reliability degree c ij  of the association degree can be calculated, for example, using an average of reliability degrees of all users who contribute to the association degree w ij  as indicated by the following expression (14): 
     
       
         
           
             
               
                 
                   
                     c 
                     ij 
                   
                   = 
                   
                     
                       1 
                       
                          
                         
                           U 
                           ij 
                         
                          
                       
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           u 
                           ∈ 
                           
                             U 
                             ij 
                           
                         
                       
                       ⁢ 
                       
                         
                           d 
                           _ 
                         
                         u 
                       
                     
                   
                 
               
               
                 
                   ( 
                   14 
                   ) 
                 
               
             
           
         
       
     
     On the contrary, where the reliability degree of each user is to be calculated from reliability degrees applied for each association degree, the reliability degree C u  of the user u can be calculated, for example, using an average of reliability degrees of association degrees applied by the user u as indicated by the following expression (15): 
     
       
         
           
             
               
                 
                   
                     c 
                     u 
                   
                   = 
                   
                     
                       1 
                       
                          
                         
                           W 
                           u 
                         
                          
                       
                     
                     ⁢ 
                     
                       
                         ∑ 
                         
                           i 
                           , 
                           
                             j 
                             ❘ 
                             
                               
                                 w 
                                 ij 
                               
                               ∈ 
                               
                                 W 
                                 u 
                               
                             
                           
                         
                       
                       ⁢ 
                       
                         c 
                         ij 
                       
                     
                   
                 
               
               
                 
                   ( 
                   15 
                   ) 
                 
               
             
           
         
       
     
     As described above, reliability degrees applied to individual association degrees and reliability degrees applied to individual users can be converted mutually therebetween. 
     The reliability degrees calculated in this manner can be utilized, for example, for such recommendation of associated items to be presented to the user as described above. For example, in the collaborative filtering described hereinabove, the reliability degree of the user may be utilized for calculation of a predictive evaluation value. In other words, where two users have similar similarities with regard to a certain user, the accuracy in prediction is enhanced by causing the estimation value of the user having a high priority degree to be reflected with a high degree on the predictive evaluation value. 
     A flow of a presentation process in this instance is described with reference to  FIG. 32 . It is to be noted that the present presentation process corresponds to the presentation process described hereinabove with reference to the flow chart of  FIG. 16 . 
     In particular, the information presentation request processing section  221  accepts an information presentation request from the client  13  similarly as at step S 41  and issues a notification of the information presentation request to the user similarity degree calculation section  222  at step S 101 . At step S 102 , the user similarity degree calculation section  222  determines similar users using the expressions (6) and (7) similarly as at step S 42  and then determines an average value of association degrees or expression evaluation values applied by the similar users u′. Further, the user similarity degree calculation section  222  specifies recommendation items to which the similar users u′ provide high evaluation (express evaluation or high association degree), and supplies information of the recommendation items to the item evaluation prediction section  223 . 
     At step S 103 , the item evaluation prediction section  223  predicts the predictive evaluation value for the recommendation items of the user u using the reliability degree C u  of the user u by arithmetic operation of the following expression (16) instead of arithmetic operation of the expression (8) conducted for prediction: 
     
       
         
           
             
               
                 
                   
                     
                       e 
                       ^ 
                     
                     ui 
                   
                   = 
                   
                     
                       e 
                       u 
                     
                     + 
                     
                       
                         
                           ∑ 
                           
                             u 
                             ′ 
                           
                         
                         ⁢ 
                         
                           
                             c 
                             
                               u 
                               ′ 
                             
                           
                           ⁢ 
                           
                             
                               r 
                               
                                 uu 
                                 ′ 
                               
                             
                             ⁡ 
                             
                               ( 
                               
                                 
                                   e 
                                   
                                     
                                       u 
                                       ′ 
                                     
                                     ⁢ 
                                     i 
                                   
                                 
                                 - 
                                 
                                   
                                     e 
                                     _ 
                                   
                                   
                                     u 
                                     ′ 
                                   
                                 
                               
                               ) 
                             
                           
                         
                       
                       
                         
                           ∑ 
                           
                             u 
                             ′ 
                           
                         
                         ⁢ 
                         
                           
                             c 
                             
                               u 
                               ′ 
                             
                           
                           ⁢ 
                           
                             r 
                             
                               uu 
                               ′ 
                             
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   16 
                   ) 
                 
               
             
           
         
       
     
     If it is assumed here that, where such association degrees as seen in the tables shown in  FIGS. 18 to 20  are applied by the three users u 1  to u 3 , respectively, the evaluation of a new item by the user u 2  is “5” and that by the user u 3  is “2” while the average values of evaluation values provided by the users u 1  to u 3  are “2.9”, “3.1” and “3.3”, respectively, as described hereinabove and besides the reliability of the user u 2  is “1.3” and that of the user u 3  is “0.4”, then the predictive evaluation value for the item by the user u 1  is calculated using the expression (16) and is determined in accordance with the following expression (17):
 
2.9+(0.891*(5−3.1)*1.3−0.45*(2−3.3)*0.4)/(0.891*1.3+0.45*0.4)=4.719  (17)
 
     In short, in this instance, the evaluation of the user u 2  is reflected intensely on the predictive evaluation value. 
     After the item evaluation prediction section  223  predicts predictive evaluation values for the recommendation items i of the user u as described above, it supplies the predictive evaluation values to the item information presentation processing section  224 . When the predictive evaluation values are acquired, the item information presentation processing section  224  produces presentation information at step S 104  similarly as at step S 44 , and the display information is displayed at step S 105  similarly as at step S 45 . 
     After the presentation information is presented, the item information presentation processing section  224  ends the presentation process. 
     Since a reliability degree is calculated and utilized for selection of items to be recommended to the user in this manner, the server  11  can present items which are comparatively conforming to the liking of the user and are estimated to be satisfied likely by the user. In other words, the server  11  can present information very useful to the user. 
     It is to be noted that the server  11  may otherwise present such reliability degrees as described above expressly to the user so as to assist the decision of the user. For example, if user reliability degrees are calculated and it is indicated clearly to what degree the users are reliable, a user to whom items are to be presented can decide the reliability of the association degree applied between the items depending upon the users by whom the reliability degrees are applied. For example, if a table of association degrees applied by a user is displayed in a profile page or the like of the user together with the reliability degree of the user, then the user of the client  13  to whom items are to be introduced can readily grasp by what degree the association degrees may be taken into consideration. 
     Further, the server  11  may present the reliability degree to a user not in the form of a numeral or character but in the form of adjustment of the display of the presentation information which is based on the reliability degree. 
     An example of a flow of a presentation process of recommending items using the content based filtering in this instance is described with reference to  FIG. 33 . The flow chart of  FIG. 33  corresponds to that of  FIG. 8 . Also  FIGS. 34 and 35  are referred to in the following description as occasion demands. 
     After the presentation process is started, the information presentation request processing section  211  of the content based filtering processing section  202  accepts an information presentation request as indicated by an arrow mark  451  in  FIG. 34  at step S 121  similarly as at step S 21 . Then, the information presentation request processing section  211  issues a notification of the information presentation request to the user liking calculation section  212  as indicated by an arrow mark  452  in  FIG. 34 . 
     When the notification is received, the user liking calculation section  212  acquires information representative of the relationship between the users and the items from the inter-item association degree database  301 , user information database  302  and item information database  303  as indicated by an arrow marks  453  to  455  in  FIG. 34  at step S 122  similarly as at step S 22 . 
     After the information is acquired, the user liking calculation section  212  calculates a liking of the user based on the information and supplies a result of the calculation to the item-user liking matching section  213  as indicated by an arrow mark  456  in  FIG. 34  at step S 123  similarly as at step S 23 . The item-user liking matching section  213  acquires all information stored in the item information database  303  or information relating to some items from within the information from the item information database  303  as indicated by an arrow mark  457  in  FIG. 34  at step S 124  similarly as at step S 24 . Then, the item-user liking matching section  213  performs matching between the items and the user liking and supplies a result of the matching, that is, a list of those items determined as matching with the liking of the user and arrayed in accordance with the similarity degree, to the item information presentation processing section  214  as indicated by an arrow mark  458  in  FIG. 34 . 
     The item information presentation processing section  214  produces presentation information at step S 125  similarly as at step S 25 . 
     After the presentation information is produced, the item information presentation processing section  214  acquires the reliability degrees from the inter-item association degree database  301  at step S 126  as indicated by an arrow mark  459  in  FIG. 34  and then adjusts the display based on the reliability degree at step S 127 . 
     For example, a list of items arrayed in accordance with the similarity degree may be displayed such that the size of characters of each item therein is set in response to the reliability degree of the item as in the case of presentation information  461  illustrated in  FIG. 35 . Or, the color or the font may be set in response to the reliability degree of each item. On the contrary, the list of items may be sorted in order of the reliability degree while the size or color of the display is varied in response to the association degree. Furthermore, the display size of a display image of a mark or an image (album jacket) representative of an item, the resolution of an image, the saturation, lightness or the like of a color or the like may set in response to the reliability degree. 
     After the display of the list of items is adjusted in response to the reliability degree in this manner, the item information presentation processing section  214  presents the presentation information to the user of the client  13  at step S 128  as indicated by an arrow mark  460  in  FIG. 34 . 
     After the presentation information is presented, the item information presentation processing section  214  ends the presentation process. 
     Now, an example of a flow of a presentation process of recommending items using the collaborative filtering is described with reference to  FIG. 36 . It is to be noted that the flow chart of  FIG. 36  corresponds to that of  FIG. 16 . Also  FIG. 37  is referred to in the following description as occasion demands. 
     The components of the collaborative filtering processing section  203  perform the processes at steps S 141  to S 144  similarly as at steps S 41  to S 44 . 
     In particular, the information presentation request processing section  221  accepts an information presentation request as indicated by an arrow mark  471  in  FIG. 37  and issues a notification of the information processing request to the user similarity degree calculation section  222  as indicated by an arrow mark  472  in  FIG. 37  at step S 141 . Upon reception of the notification, the user similarity degree calculation section  222  acquires information of the association degrees from the inter-item association degree database  301  as indicated by an arrow mark  473  in  FIG. 37  at step S 142 . Further, the user similarity degree calculation section  222  determines users u′ similar to the user u of the requesting source, recommendation items to which a high evaluation is provided by the users u′ and average values of evaluation values or association degrees provided to the recommendation items by the users u′. Further, the user similarity degree calculation section  222  supplies the thus determined information to the item evaluation prediction section  223  as indicated by an arrow mark  474  in  FIG. 37 . The item evaluation prediction section  223  acquires an average value of evaluation values provided to the items by the user u as indicated by an arrow mark  475  in  FIG. 37  at step S 143 . Then, the item evaluation prediction section  223  predicts a predictive evaluation value for the recommendation items i for the user u and supplies the predictive evaluation values to the item information presentation processing section  224  as indicated by an arrow mark  476  in  FIG. 37 . When the predictive evaluation values are acquired, the item information presentation processing section  224  acquires information of the recommendation items from the item information database  303  as indicated by an arrow mark  477  in  FIG. 37  and produces presentation information based on the acquired information at step S 144 . 
     After the presentation information is produced, the item information presentation processing section  224  acquires the reliability degrees from the inter-item association degree database  301  as indicated by an arrow mark  478  in  FIG. 37  at step S 145 . Then at step S 146 , the item information presentation processing section  224  adjusts the display of the presentation information base on the reliability degrees. This adjustment is similar to that in the content based filtering described hereinabove with reference to  FIG. 35 , and therefore, overlapping description of such adjustment is omitted hereinto avoid redundancy. 
     After the presentation information is shaped, the item information presentation processing section  224  supplies the presentation information to the client  13  through the network  10  as indicated by an arrow mark  479  in  FIG. 37  so as to be presented to the user at step S 147 . Then, the presentation process is ended. 
     As a technique for converting a relative relationship in a set such as a set of association degrees between items described hereinabove into an absolute coordinate in a vector space or a probability space, a main coordinate analysis, a SAM (Semantic Aggregation Model) and so forth are available. The main coordinate analysis is disclosed, for example, in Gower J. C., “Multivariate Analysis and Multidimensional Geometry”, Statistician, Vol. 17, No. 1, pp. 13 to 28, 1967 (hereinafter referred to as Non-Patent Document 4). The SAM is disclosed, for example, Daichi MOCHIHASHI, “Stoichiometric Representation of Meaning”, Natural Language Process of Report of Study of the Information Processing Society of Japan, Vol. 2002, No. 4, 2002 (hereinafter referred to as Non-Patent Document No. 5). 
     By such techniques, those items which have a high association degree or those items which have a similar tendency in association degree with an item of a third party are disposed at positions near to each other in a vector space or a probability space. Meta data applied newly in this manner reflect, different from those which are determined comparatively objectively, for example, like the genre of music, a subjective sense of the human being or the nature of a community in which a service is provided. Therefore, the meta data can be utilized appropriately in recommendation. 
     For example, in the SAM, based on co-occurrence frequencies N(w 1 , w 2 ) (which form a square matrix) of two words w 1  and w 2 , the simultaneous probability P(w 1 , w 2 ) in which both words w 1  and w 2  co-occur are represented using a potential random variable z as P(w 1 , w 2 )=ΣzP(w 1 |z)P(w 2 |z)P(z), and a distribution parameter is determined by an EM algorithm. Then, P(z|w)∝P(w|z)P(z) (zε{z 1 , z 2 , . . . , zK}) is regarded as a random representation for the word w (K is the number of occurrence values of the potential random variable z). This is applied to an association between items, and a word is considered as an item and the co-occurrence frequency of the word is made correspond to the association degree between items to obtain a random representation P(z| 0   i ) of the certain item  0   i.    
     An example of a flow of an additional meta data production process of producing new meta data (additional meta data) from an association degree between items is described with reference to  FIG. 38 . As occasion demands, also  FIGS. 39 to 41  are referred to in the following description. 
     After the additional meta data production process is started, the additional meta data calculation section  241  of the additional meta data processing section  205  acquires association degrees of all items at step S 161 . The additional meta data calculation section  241  accesses the inter-item association degree database  301  to extract all association degrees applied to all items as indicated by an arrow mark  481  of  FIG. 39 . 
     After all information of the association degree is acquired, the additional meta data calculation section  241  estimates parameters of the random distribution by such a technique as disclosed, for example, in Non-Patent Document 5 at step S 162 . Here, P(z), P(0|z) (0ε{01, 02, . . . }: all item set, zε{z 1 , z 2 , . . . , zK)) are used as parameters. 
     The additional meta data calculation section  241  determines, for each item  0 , a vector whose component is P(z|0) to zε(z 1 , z 2 , . . . , zK} as additional data at step S 163 . After the additional data are produced, the additional meta data calculation section  241  stores the additional data into the user information database  302  at step S 164 , and then the additional meta data production process is ended. 
     It is assumed that association degrees are applied symmetrically between items, for example, as seen in a table shown in  FIG. 40 . Although, in the table  40  shown in  FIG. 40 , association degrees applied between four items are shown, usually a number of items on the order of 10^2 or more are used in the calculation. 
     If such association degrees are used to estimate P(z|0) by the SAM with K set to K=4, then the additional meta data calculation section  241  obtains such a result, for example, as illustrated in a table shown in  FIG. 41  (since the EM algorithm provides a result which depends upon an initial value, there is no reproducibility). If parameters estimated in this manner are set as meta data of the items after they suitably undergo such processes as normalization, then the item  20  and the item  21  or the item  22  and the item  23  which have a high association degree with each other are disposed at positions proximate to each other in the space. 
     It is to be noted that, when additional data are produced as described above, the additional meta data calculation section  241  may utilize the reliability degrees with regard to the association degrees, that is, association degree reliability degrees. Since the association degree reliability degree indicates whether or not an association degree between items is sufficiently reliable, the additional meta data calculation section  241  can produce additional meta data having a high degree of accuracy by attaching the importance to those association degrees which exhibit a high reliability degree. For example, where a reliability degree c ij  is provided to an association degree w ij , the additional meta data calculation section  241  determines the product w i c ij  of them as the association degree applied between the item  0   i  and the item  0   j  and produces additional meta data based on the association degree by the method described hereinabove. By this, the additional meta data calculation section  241  can produce additional meta data on which the association degree reliability degree is reflected. 
     Now, recommendation of similar items which utilizes the additional meta data produced in this manner is described. As a method of recommendation which utilizes a short-term context, for example, a method of presenting link information to an item similar to the item  0   i  simultaneously, for example, when a request to present information of the item  0   i  is received is available. Here, the similar item is determined depending upon vector representations of various meta data retained in the item information database  303  and Euclid distances or cosine distances combined with association degrees applied between items by users and/or additional data. 
     An example of a flow of a similar item process of presenting such similar items as described above is described with reference to  FIG. 42 . As occasion demands, also  FIGS. 43 to 46  are referred to. 
     The information presentation request processing section  242  of the additional meta data processing section  205  accepts an information presentation request regarding the item  0   i  from the Web browser or the like of the client  13  as indicated by an arrow mark  491  in  FIG. 43  at step S 181 . Further, the information presentation request processing section  242  issues a notification of the information presentation request to the item similarity degree calculation section  243  as indicated by an arrow mark  492  in  FIG. 43 . 
     The item similarity degree calculation section  243  determines similar items at step S 182 . If the vector representation by meta data of the item  0   i  is represented by v i  and the vector representation of additional meta data by s i  while the association degree from the item  0   i  to the item  0   j  is represented by w ij , then after the item similarity degree calculation section  243  acquires information of the association degree from the item information database  303  as indicated by an arrow mark  493  in  FIG. 43 , it determines the Euclid distance d E  in the vector space including additional meta data using the following expression (18). Then, the item similarity degree calculation section  243  sets N (N is an arbitrary natural number) items whose Euclid distance d E  is comparatively short as similar items. 
     
       
         
           
             
               
                 
                   
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     It is to be noted that the following expression (19) may be used in place of the Euclid distance of the expression (18) to set N (N is an arbitrary natural number) items having comparatively low values as similar items. 
     
       
         
           
             
               
                 
                   
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     where vih is the hth component of the vector vi. The item similarity degree calculation section  243  supplies a list of the N similar items determined in such a manner as described above to the item information presentation processing section  244  as indicated by an arrow mark  494  in  FIG. 43 . 
     The item information presentation processing section  244  shapes the list of similar items selected and arrayed as described above to produce presentation information at step S 183 . Then at step S 184 , the item information presentation processing section  244  supplies the presentation information to the client  13  through the network  10  as indicated by an arrow mark  495  in  FIG. 43  so as to be presented to the user of the client  13 . 
     For example, if it is assumed that meta data represented by such numerical values as seen in a table of  FIG. 44  are applied to the items  1  to  4 , then the Euclid distance from the item  20  to the items  21  to  23  is such as illustrated in a table shown in  FIG. 45 . Accordingly, if the items  21  to  23  are arrayed in the descending order of the similarity to the item  20 , then they are arrayed in the order of the item  23 , item  21  and item  22 . 
     On the other hand, if the additional data are applied in such a manner as seen in the table shown in  FIG. 41 , then the Euclid distances determined using the expression (18) given hereinabove are such as illustrated in a table shown in  FIG. 46 . Accordingly, if the items  21  to  23  are arrayed in the descending order of the similarity to the item  20 , then they are arrayed in the order of the item  21 , item  23  and item  22 . 
     It is to be noted that additional meta data can be utilized also in user liking matching. 
     An example of a flow of a user liking matching process which is a presentation process in this instance is described with reference to  FIG. 47 . As occasion demands, also  FIGS. 48 to 52  are referred to. 
     At step S 201 , the information presentation request processing section  242  accepts an information presentation request for the item  0   i  from the Web browser of the client  13  or the like as indicated by an arrow mark  501  in  FIG. 48 . Then, the information presentation request processing section  242  issues a notification of the information presentation request to the item-user liking matching section  245  as indicated by an arrow mark  502  in  FIG. 48 . 
     The item-user liking matching section  245  acquires information of all or some of the items from the item information database  303  as indicated by an arrow mark  502  in  FIG. 48  and performs item-user liking matching of deciding whether or not each item and the liking of the user match with each other at step S 202 . 
     For example, if the liking of the user u in a vector representation is represented by UP u  and the vector representation of the item  0   i  is represented by vi, then the item-user liking matching section  245  calculates the cosine similarity degree Sc using the expression (4) given hereinabove. Then, the item-user liking matching section  245  determines N (N is an arbitrary integer) items which have comparatively high values of the cosine similarity degree Sc as items which match with the liking of the user. It is to be noted that vi here is a vector formed by joining existing meta data and additional meta data. Further, not the cosine distance but the Euclid distance, the intercity distance or the like may be used to perform matching. 
     The item-user liking matching section  245  supplies a list of the items matching with the liking of the user as a result of the matching to the item information presentation processing section  244  as indicated by an arrow mark  504  in  FIG. 48 . 
     The item information presentation processing section  244  produces presentation information at step S 203  and supplies the presentation information to the client  13  through the network as indicated by an arrow mark  505  in  FIG. 48  so as to be presented to the user of the client  13  at step S 204 . 
     It is assumed that, for example, if existing meta data and additional meta data are individually normalized (dispersion  1 , average  0 ), then such a result as seen in a table shown in  FIG. 49  is obtained and such user liking vectors as seen in a table shown in  FIG. 50  are obtained. At this time, by conventional matching between contents and the user liking wherein additional meta data are not taken into consideration, such Euclid distances as seen in a table shown in  FIG. 51  are obtained, and information of similar items is displayed in an array in order of the item  23 , item  20 , item  21  and item  22 . However, where also additional meta data are involved, such Euclid distances as seen in a table shown in  FIG. 52  are obtained, and the information of the similar items is presented in an array in order of the item  20 , item  23 , item  21  and item  22 . 
     As an example of such additional meta data, an importance degree of an item may be calculated from inter-item association degrees. If an absolute importance degree of an item is determined, then since generally a content which is considered desirable is determined, this is convenient in recommendation in such a case that sufficient liking information may not be obtained or the like. Here, the importance degree is determined based on an idea that “an item evaluated as being associated from many items or an item evaluated as being associated from an important item is important”. In order to implement such an idea as just described, for example, the sum total of association degrees in the column/row directions may be taken or a technique of the PageRank (refer to, for example, U.S. Pat. No. 6,285,999) or the HITS (refer to, for example, Kleinberg J., “Authoritative Sources in a Hyperlinked Environment”, Journal of the ACM, 46, 1999) may be used. Here, a simplified form of the PageRank is described. It is to be noted that, although the importance degree should be updated every time an association degree varies, it may otherwise be updated in an asynchronous relationship with a change of an association degree from a burden of processing. 
     An example of a flow of an item importance degree calculation process executed to calculate such an item importance degree as described above is described with reference to  FIG. 53 . As occasion demands, also  FIGS. 54 to 58  are referred to. 
     After the item importance degree calculation process is started, the item importance degree calculation section  251  of the item importance degree processing section  206  extracts the association degree information regarding all items from the association degree collection section  201  as indicated by an arrow mark  511  in  FIG. 54  at step S 221 . The association degree extracted here is represented by a matrix W of i rows and j columns where the association degree from the item  0   i  to the item  0   j  is represented by w ij . 
     At step S 222 , the item importance degree calculation section  251  transposes the matrix W of association degrees to produce a transition stochastic matrix W′ which is normalized for each column. Further, at step S 223 , the item importance degree calculation section  251  performs characteristic value decomposition of the transition stochastic matrix W′ using a matrix V wherein columns are characteristic vectors and a matrix L which has characteristic values at diagonal components in accordance with the following expression (20):
 
 W′=V*L*V− 1  (20)
 
     At step S 224 , the item importance degree calculation section  251  specifies a characteristic vector having a characteristic value whose absolute value is in the maximum and sets the components of the characteristic vector as importance values of the items. Then at step S 225 , the item importance degree calculation section  251  stores the item importance degrees into the item information database  303  as indicated by an arrow mark  512  in  FIG. 54 , and then the item importance calculation process is ended. 
     For example, it is assumed that such sum totals of association degrees applied by all users as seen in a table shown in  FIG. 25  are obtained. Here, for the simplified description, it is assumed that four items including the item  1  to the item  4  are involved. If this is the matrix W, then the transition stochastic matrix W′ becomes such as shown in  FIG. 55 . If characteristic value decomposition of this is performed, then the matrix V wherein columns are characteristic vectors becomes such as shown in  FIG. 56 , and the matrix L which has characteristic values at diagonal components becomes such as shown in  FIG. 57 . Since the maximum characteristic value is “1”, the first column of the matrix V becomes a necessary characteristic vector, and the importance value of the items  1  to  4  becomes “0.723”, “0.225”, “0.546” and “0.358”, respectively, as seen in a table shown in  FIG. 58 . 
     It is to be noted that, while, in the foregoing description, the importance degree of an item is determined based on the association degrees by all users, the determination is not limited to this, but, for example, the importance degree may be determined, for example, based on association degrees applied by one user. In this instance, since item importance degrees personalized by each user (item importance degrees for each user) are calculated, if the server  11  performs recommendation of items utilizing the calculated item importance degrees, then recommendation or presentation of comparatively appropriate items can be achieved. 
     Further, if such a reliability degree of the association degree as described above is utilized, then it is expected that the importance degree with a higher degree of accuracy can be determined. For example, where reliability degrees c ij  are associated with association degrees w ij , if the item importance degree calculation section  251  performs calculation similar to that described hereinabove using a matrix c wherein the components of the ith rows and the jth columns are provided by the products w ij c ij  of the reliability degrees c ij  and the association degrees w ij  newly as a matrix which represents association degrees, then the association degree reliability degree can be reflected on the calculation of an item importance degree. 
     The presentation method of an item to the user may be changed in response to the importance degree of the item calculated in such a manner as described above. By this, recommendation with a high degree of convenience can be provided. An example of a flow of an item importance degree presentation correction process executed in order to perform such presentation as just described is described below with reference to  FIG. 59 . As occasion demands, also  FIGS. 60 and 61  are referred to. 
     After the item importance degree presentation correction process is started, the information presentation request processing section  252  of the item importance degree processing section  206  accepts an information presentation request supplied from the client  13  through the network  10  as indicated by an arrow mark  521  in  FIG. 60  at step S 241 . Then, the information presentation request processing section  252  issues a notification of the information presentation request to the presentation information production section  253  as indicated by an arrow mark  522  in  FIG. 60 . 
     When the notification is received, the presentation information production section  253  acquires information of items which satisfy conditions of the search request from the item information database  303  as indicated by an arrow mark  523  in  FIG. 60  at step S 242 . Here, a search based on arbitrary data such as, for example, the genre or the hometown of an artist is included. At step S 243 , the presentation information production section  253  produces presentation information for presenting the acquired information to the user and supplies the presentation information to the presentation information correction section  254  as indicated by an arrow mark  524  in  FIG. 60 . 
     The presentation information correction section  254  acquires the item importance degrees from the item information database  303  as indicated by an arrow mark  525  in  FIG. 60  at step S 244 . Then at step S 245 , the presentation information correction section  254  corrects the presentation information based on the item importance degrees, for example, by performing re-arrangement of the list of items or changing the size, color or the like of an image or characters to be displayed. 
     It is assumed that, for example, the importance degree of the items is set in such a manner as seen in a table shown in  FIG. 58 . Here, if the item  2 , item  3  and item  4  are recommendable as similar items to a certain item, then it is possible to present the items in an array in the descending order of the magnitude of the importance degree as in the case of presentation information  531  illustrated in  FIG. 61 . It is to be noted that the presentation order may be any order, and for example, the items may be presented in an array in the descending order of the value of the product of the association degree and the importance degree. 
     After the correction comes to an end, the presentation information correction section  254  supplies the presentation information corrected in this manner to the item information presentation processing section  255  as indicated by an arrow mark  526  in  FIG. 60 . 
     The item information presentation processing section  255  supplies the presentation information to the client  13  through the network  10  as indicated by an arrow mark  527  in  FIG. 60  so as to be presented to the user of the client  13  at step S 246 . Thereafter, the item importance degree presentation correction process is ended. 
     As a utilization method of the importance degree of an item described above, an explanation of a selected item may be presented based on the importance degree of items or a relationship between such importance degrees and user liking information may be presented to a user. 
     First, an example of a flow of a presentation process where the importance degree of an item is applied to the content based filtering is described with reference to  FIG. 62 . As occasion demands, also  FIG. 63  is referred to. It is to be noted that the flow chart of  FIG. 62  corresponds to the flow chart of  FIG. 8 . 
     Accordingly, the components of the content based filtering processing section  202  execute processes at steps S 261  to S 265  of  FIG. 62  similarly to the processes at steps S 21  to S 25  of  FIG. 8 . In particular, after the presentation process is started, the information presentation request processing section  211  accepts an information presentation request supplied from the client  13  through the network  10  as indicated by an arrow mark  541  in  FIG. 63  at step S 261 . Further, the information presentation request processing section  211  issues a notification of the information presentation request to the user liking calculation section  212  as indicated by an arrow mark  542  in  FIG. 63 . 
     When the notification is received, the user liking calculation section  212  acquires the information indicative of relationships between the users and items from the inter-item association degree database  301 , user information database  302  and item information database  303  as indicated by an arrow marks  543  to  545  in  FIG. 63  at step S 262 . 
     After the information is acquired, the user liking calculation section  212  calculates a liking of the user based on the acquired information and supplies a result of the calculation to the item-user liking matching section  213  as indicated by an arrow mark  546  in  FIG. 63  at step S 263 . The item-user liking matching section  213  acquires all or some of the items stored in the item information database  303  from the item information database  303  as indicated by an arrow mark  547  in  FIG. 63  and performs matching of the items and the user liking at step S 264 . Further, the item-user liking matching section  213  supplies a result of the matching, that is, a list wherein those items decided as matching with the liking of the user are arranged in accordance with the similarity degree, to the item information presentation processing section  214  as indicated by an arrow mark  548  in  FIG. 63 . 
     The item information presentation processing section  214  produces presentation information at step S 265 . Further, the item explanation application section  215  acquires the importance degree of the items included in the matching result from the item information database  303  as indicated by an arrow mark  549  in  FIG. 63  at step S 266 . Then at step S 267 , the item explanation application section  215  selects a template for an explanation for each of the items from among templates prepared in advance based on the importance degrees of the items and supplies the selected templates to the item information presentation processing section  214  as indicated by an arrow mark  550  in  FIG. 63 . 
     The item explanation application section  215  produces a word or a sentence which explains what nature each of the items decided as matching with the liking of the user has based on the importance degrees of the items and applies the produced word or sentence to the presentation information. 
     For example, as an explanation for an item corresponding to a music content, the item explanation application section  215  selects, for an item having a high importance degree, a template of “Please enjoy this by all means!”, but selects, for another item having a lower importance degree, another template of “Maniac commodity!”. Although the method here allows only simple selection of a sentence determined in advance, a combination of a plurality of words may be selected alternatively. 
     Consequently, a user to whom a list of items is presented can grasp the items with a high degree of accuracy. For example, if an item unknown to a user is presented, then it is difficult for the user to grasp it based only on the item name what item the item is. In such an instance, if an item explanation is applied to the presentation information by the item explanation application section  215 , then the user can correctly recognize the presented item easily. 
     The item information presentation processing section  214  applies the explanation to the list of items of the presentation information at step S 268 . Then at step S 269 , the item information presentation processing section  214  supplies the presentation information to the client  13  through the network  10  so that the list with the explanation of items matching with the liking of the user is presented to the user of the client  13  as indicated by an arrow mark  551  in  FIG. 63 . After the presentation information is presented, the item information presentation processing section  214  ends the presentation process. 
     Now, an example of a flow of a presentation process where the importance degree of an item is applied to collaborative filtering is described with reference to  FIG. 64 . As occasion demands, also  FIG. 65  is referred to. It is to be noted that the flow chart of  FIG. 64  corresponds to the flow chart of  FIG. 16 . 
     The components of the collaborative filtering processing section  203  execute processes at steps S 281  to S 284  in a similar manner as at steps S 41  to S 44 . 
     In particular, the information presentation request processing section  221  accepts an information presentation request as indicated by an arrow mark  561  in  FIG. 65  and issues a notification of the information presentation request to the user similarity degree calculation section  222  as indicated by an arrow mark  562  in  FIG. 65  at step S 281 . When the notification is received, the user similarity degree calculation section  222  acquires the information of the association degrees from the inter-item association degree database  301  as indicated by an arrow mark  563  in  FIG. 65  at step S 282 . Further, the user similarity degree calculation section  222  determines, based on the information of the association degrees, similar users u′ similar to the user u of the requesting source, recommendation items to which a high evaluation is provided by the similar users u′ and average values of the evaluation values or association degrees provided to the recommendation items by the similar users u′. Then, the user similarity degree calculation section  222  supplies the determined information to the item evaluation prediction section  223  as indicated by an arrow mark  564  in  FIG. 65 . The item evaluation prediction section  223  acquires the average values of the evaluation values regarding the items provided by the user u from the user information database  302  as indicated by an arrow mark  565  in  FIG. 65  at step S 283 . Further, the item evaluation prediction section  223  predicts predictive evaluation values for the recommendation items i of the user u and supplies the predictive evaluation values to the item information presentation processing section  224  as indicated by an arrow mark  566  in  FIG. 65 . When the predictive evaluation values are acquired, the item information presentation processing section  224  acquires information of the recommendation items from the item information database  303  as indicated by an arrow mark  567  in  FIG. 37  and produces presentation information based on the acquired information at step S 284 . 
     The item explanation application section  225  acquires the importance degrees of the recommendation items from the item information database  303  as indicated by an arrow mark  568  in  FIG. 65  at step S 285 . Then at step S 286 , the item explanation application section  225  acquires the liking information of the user of the requesting source from the user information database  302  as indicated by an arrow mark  569  in  FIG. 65 . Then at step S 287 , the item explanation application section  225  selects a template based on the item importance degrees and the liking information of the user and supplies the selected template to the item information presentation processing section  224  as indicated by an arrow mark  570  in  FIG. 65 . 
     In particular, the item explanation application section  225  analyzes a role of the importance degrees of the items in the liking information of the user to whom the information is to be presented. For example, if those items which are included in the purchase history of the user are one-sided to those items which provide a high importance degree in music contents, it can be considered that the user generally likes wide and popular (major) artists (albums or musical pieces). On the other hand, if items included in the purchase history of the user are one-sided to those items which provide a low importance degree, it can be considered that the user likes (minor) artists who are popular among only restricted people. Further, if those items which are included in the purchase history of the user are not particularly one-sided but exhibit a uniform expansion (great variance), then it can be considered that the user does not particularly care whether or not artists are famous, that is, whether artists are major or minor. In this manner, the item explanation application section  225  selects an explanation suitable for an item to be presented from among the templates prepared in advance, for example, as seen in a table shown in  FIG. 66  based on the importance degree of the item and the position in importance degree of the item in the user liking. 
     For example, if an item of a low importance degree is to be presented to a user whose liking is one-sided to items having a low importance degree as seen in  FIG. 66 , then the item explanation application section  225  applies an explanatory sentence of “To you who are maniac, how about this!” to the presentation information. On the other hand, an item having a high importance degree is to be presented to the same user, the item explanation application section  225  applies an explanatory sentence of “How about this occasionally?” to the presentation information. In this manner, the item explanation application section  225  can apply an appropriate explanation through selection of the explanation based on a relationship between the liking of the user and the importance degree of the item. 
     The item information presentation processing section  224  applies the explanation to the presentation information at step S 288 . Then at step S 289 , the item information presentation processing section  224  supplies the presentation information to the client  13  through the network  10  as indicated by an arrow mark  571  in  FIG. 65  so as to be presented to the user. Thereafter, the presentation process is ended. 
     In this manner, the server  11  can achieve preferable recommendation to a user of a client not only based on an express evaluation behavior of the user of the client but also by feedback of a relationship between information of recommendation objects. 
     In particular, by applying not only express evaluation of contents but also an association degree between items, a greater amount of feedback is obtained, and by utilizing information of the similarity degree between items which relies upon the sensitivity of the user, information which may not be represented by existing meta data can be extracted. Consequently, the item introduction system  1  (server  11 ) can achieve effective content recommendation or item recommendation to a user of a client. 
     It is to be noted that, while the series of processes described above can be executed by hardware, it may otherwise be executed by software. Where the series of processes is executed by software, a program which constructs the software is installed from a network or a recording medium. 
     The recording medium is formed, for example, as shown in  FIG. 3 , as a removable medium  121  such as a magnetic disk (including a flexible disk), an optical disc (including a CD-ROM (Compact Disk-Read Only Memory) and a DVD (Digital Versatile Disc)), or a magneto-optical disk (including an MD (Mini Disc)) (registered trademark), or a semiconductor memory which has the program recorded thereon or therein and is distributed in order to provide the program to a user separately from an apparatus body. Else, the recording medium is formed as a ROM  102 , a hard disc or the like included in the storage section  113  in which the program is recorded and which is distributed to a user in a state wherein the program is incorporated in an apparatus in advance. 
     It is to be noted that, in the present specification, the steps which describe the program recorded in a recording medium may be but need not necessarily be processed in a time series in the order as described, and include processes which are executed in parallel or individually without being processed in a time series. 
     Further, in the present specification, the term “system” is used to represent an entire apparatus composed of a plurality of devices or apparatus. 
     It is to be noted that any element which is described as a single apparatus in the foregoing description may be divided so as to be formed as a plurality of apparatus. On the contrary, any elements which are described as a plurality of apparatus in the foregoing description may be combined so as to be formed as a single apparatus. Further, any other element than those described hereinabove may be additionally provided to any of the elements described hereinabove. Furthermore, an element of some apparatus may be included in an element of another apparatus if the configuration or operation of the entire system is substantially same or equivalent. In other words, the present invention is not restricted to the embodiment described hereinabove, but can be carried out in various modified forms without departing from the spirit and scope of the present invention.