Patent Publication Number: US-2015074544-A1

Title: Information processing apparatus, information processing method, and program

Description:
TECHNICAL FIELD 
     The present technology relates to an information processing apparatus, an information processing method, and a program, and more particularly to an information processing apparatus, an information processing method, and a program suitable for offering social services. 
     BACKGROUND ART 
     In recent years, with rapid development of contents distribution services such as music and movie distribution services, various technologies for searching various items, including contents, have been proposed. 
     For example, such a method is known which vectorizes feature values of items, and uses Euclidean distances between vectors to search an item similar to a targeted item. Another known method employs a technique of item-based collaborative filtering which uses an evaluation history of a user to search an item similar to a targeted item (e.g., see Patent Document 1). 
     In addition, in offering information about other users, a certain service of conventional social services presents histories of item (such as contents) use of the users so as to clarify the tastes of the users. In this case, items recently used by the users, or items frequently used by the users are often selected, for example. 
     CITATION LIST 
     Patent Document 
     
         
         Patent Document Japanese Patent Application Laid-Open No. 2012-3359 
       
    
     SUMMARY OF THE INVENTION 
     Problems to be Solved by the Invention 
     However, when items recently used or items frequently used are selected for presentation, it is highly probable that only similar items are presented. In this case, biased tastes of the users may be given. 
     Accordingly, the present technology aims at offering more accurate tastes of users. 
     Solutions to Problems 
     An information processing apparatus according to an aspect of the present technology includes: an item selecting unit selecting a plurality of items dissimilar to each other from a group of items used by a presented user as a target presented to a user; and a presentation control unit allowing the plurality of selected items to be presented to the user together with information about the presented user. 
     The item selecting unit may be made to limit the number of items overlapping each other between the presented users when the plurality of presented users are presented to the user. 
     The item selecting unit may be made to calculate distances between a selected group of the items and the remaining items based on feature values of the items, and to select an item having the maximum distance when a new item is selected from the group of items used by the presented user. 
     The item selecting unit may be made to select a plurality of items from items previously used by the user and included in use group of items used by the presented user. 
     A recommended user selecting unit calculating an expected value of variation of an action of the user, which variation is produced by presentation of a plurality of choices of users to be recommended to the user, and selecting a recommended user to be recommended to the user from the choices of the users based on the calculated expected value may be further included. The item selecting unit may be made to select a plurality of items dissimilar to each other from a group of items used by the recommended user. The presentation control unit may be made to allow the plurality of selected items to be presented to the user together with information about the recommended user. 
     The recommended user selecting unit may be made to calculate the expected value based on a probability that the user accepts the choices of users, and variation of a prediction value of an evaluation given by the user for a predetermined group of items, which variation is produced by presentation of feedback given by the choices of users. 
     The recommended user selecting unit may be made to calculate the expected value further based on a probability that the choices of users give feedback for respective items included in the predetermined group of items. 
     An information processing method according to an aspect of the present technology performed by an information processing apparatus includes the steps of: selecting a plurality of items dissimilar to each other from a group of items used by a presented user as a target presented to a user; and allowing the plurality of selected items to be presented to the user together with information about the presented user. 
     A program implemented by a computer according to an aspect of the present technology includes the steps of: selecting a plurality of items dissimilar to each other from a group of items used by a presented user as a target presented to a user; and allowing the plurality of selected items to be presented to the user together with information about the presented user. 
     According to an aspect of the present technology, a plurality of items dissimilar to each other are selected from a group of items used by a presented user as a target presented to a user. The plurality of selected items are allowed to be presented to the user together with information about the presented user. 
     Effects of the Invention 
     According to an aspect of the present technology, more accurate tastes of users can be offered. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram showing an information processing system to which the present technology is applied according to an embodiment. 
         FIG. 2  is a block diagram showing a structure example of functions of a server. 
         FIG. 3  is a block diagram showing a structure example of functions of a contents link creation processing unit. 
         FIG. 4  is a block diagram showing a structure example of functions of a recommended user selection processing unit. 
         FIG. 5  is a block diagram showing a structure example of functions of a presented matters setting unit. 
         FIG. 6  is a block diagram showing a structure example of functions of a client. 
         FIG. 7  is a flowchart describing a contents link creating process. 
         FIG. 8  shows a structure example of user history data. 
         FIG. 9  shows an example of feature values of contents. 
         FIG. 10  shows an example of a result of calculation of regression coefficients of evaluation values given by respective users for contents from feature values of the contents. 
         FIG. 11  shows an example of a contents link creating screen. 
         FIG. 12  is a flowchart describing a user recommendation learning process. 
         FIG. 13  shows an example of feature values of CUF tuples. 
         FIG. 14  shows an example of weights given to respective feature values used for creating acceptance models by CBF. 
         FIG. 15  is a flowchart describing a contents link sharing process. 
         FIG. 16  is a flowchart describing the details of a recommended user selecting process. 
         FIG. 17  shows an example of a result of calculation of predicted evaluation values. 
         FIG. 18  shows an example of a result of calculation of a user acceptance probability. 
         FIG. 19  shows an example of a result of calculation of feedback predicted evaluation values. 
         FIG. 20  is a flowchart describing the details of a presented contents selecting process. 
         FIG. 21  shows an example of histories of reproduction number of times of contents. 
         FIG. 22  shows an example of feature values of contents. 
         FIG. 23  shows an example of a result of calculation of Euclidean distances between respective contents. 
         FIG. 24  shows a display example of a recommended user list. 
         FIG. 25  shows a display example of a contents link list. 
         FIG. 26  shows another display example of the contents link list. 
         FIG. 27  shows a display example of a history-related list. 
         FIG. 20  is a block diagram showing a structure example of a computer. 
     
    
    
     MODE FOR CARRYING OUT THE INVENTION 
     A mode for carrying out the present technology (hereinafter referred to as an embodiment) is hereinafter described. The description will be given in the following order.
     1. Embodiment   2. Modified Examples   

     1. EMBODIMENT 
     [Structure Example of Information Processing System  1 &gt; 
       FIG. 1  is a block diagram showing an information processing system to which the present technology is applied according to an embodiment. 
     An information processing system  1  is configured to include a server  11  and clients  12 - 1  through  12 - n.  The server  11  and the clients  12 - 1  through  12 - n  are connected with each other via a network  13 . 
     The clients  12 - 1  through  12 - n  are abbreviated as clients  12  when distinction between the clients  12 - 1  through  12 - n  is not needed. 
     The server  11  offers a service for providing distribution and recommendation of contents as a type of various items (hereinafter referred to as contents distribution service) for the respective clients  12 . 
     The contents distribution service includes not only distribution and recommendation of contents, but also various types of services associated with distribution and recommendation of contents. 
     For example, the contents distribution service offers a service for supporting users in giving feedback of comments, evaluations or others for contents, and a service for collecting given feedback and supplying the feedback to users for sharing, for example. 
     Moreover, the contents distribution service offers a service for assisting creation of contents links functioning as links between associated contents, and a service for supplying created links to users for sharing, for example. 
     Furthermore, for example, the contents distribution service offers a social service function capable of establishing communicative relationships between users and other users. For example, users can follow other users, or establish friendship with other users through this function. 
     In this context, the phrase “users follow other users” refers to the case where a user corresponding to a following person establishes such a condition for automatically obtaining information about activities (such as comments and feedback for various contents) of another user corresponding to a followed person on the contents distribution service, for example. Additionally, the phrase “users establish friendship with other users” refers to the case where two users follow each other, for example. 
     Hereinafter, a user corresponding to a following person is referred to as a follower, while a user corresponding to a followed person is referred to as a followee. Accordingly, each of two users having established friendship with each other can become either a follower or a followee. 
     Moreover, the contents distribution service offers a service for recommending another user as well as contents, for example. 
     Chiefly described hereinafter is an example in which the server  11  performs distribution, recommendation or the like of music as a type of contents. 
     The clients  12  are constituted by devices which can use a contents distribution service offered by the server  12 , such as personal computers, personal digital assistants, cellular phones, smartphones, movie players, and audio players. 
     [Structure Example of Server  11 ] 
     Among functions of a server  11 ,  FIG. 2  illustrates mainly a structure example of functions of parts chiefly performing processes associated with contents links, and processes associated with presentation of information about other users. 
     The server  11  is configured to contain a communicating unit  31 , an information processing unit  32 , and a storing unit  33 . 
     The communicating unit  31  and respective parts of the information processing unit  32  can access each other. In addition, the respective parts of the information processing unit  32  can access respective parts of the storing unit  33 . 
     The communicating unit  31  communicates with the respective clients  12  via the network  13  to transmit and receive information, instructions and others associated with the contents distribution service. 
     The information processing unit  32  performs various processes associated with the contents distribution service. The information processing unit  32  is configured to contain a contents link creation processing unit  41 , a recommended user selection processing unit  42 , and a presentation control unit  43 . 
     The contents link creation processing unit  41  performs a process associated with creation of a contents link. For example, the contents link creation processing unit  41  selects choices of contents of a link source and of contents of a link destination, and choices of a feature value used for connecting the two contents, and supplies the selected choices to a display control unit  52 . 
     The contents of the link source and link destination of the contents link are hereinafter referred to as link source contents and link destination contents. The feature value used for connecting the link source contents and the link destination contents is hereinafter referred to as a link feature value. 
     In addition, the contents link creation processing unit  41  creates a contents link by determining the link source contents, the link destination contents, and the link feature value based on user&#39;s instructions or the like received from the client  12  via the communicating unit  31 , for example. The contents link creation processing unit  41  stores contents link information indicating the created contents link in a contents link storing unit  64  of the storing unit  33 . 
     The recommended user selection processing unit  42  performs a process associated with selection of recommended users who are to be recommended to users. The recommended user selection processing unit  42  supplies information indicating the result of selection of the recommended users to a presented matters setting unit  51  of the presentation control unit  43 . 
     The presentation control unit  43  controls presentation of various types of information to the respective clients  12 . The presentation control unit  43  is configured to contain the presented matters setting unit  51  and the display control unit  52 . 
     The presented matters setting unit  51  sets the matters of information presented to the respective clients  12 . The presented matters setting unit  51  supplies information indicating the set matters to the display control unit  52 . 
     The display control unit  52  creates display control data used for displaying various types of information, and transmits the data to the respective clients  12  via the communicating unit  31  to control display of various types of information displayed on the respective clients  12 . For example, the display control unit  52  controls display on the clients  12 , such as a screen for creating a contents link, and a screen for presenting a contents link and recommended users. 
     The storing unit  33  stores various types of information used for the contents distribution service. The storing unit  33  is configured to contain a contents information storing unit  61 , a user relationship storing unit  62 , a user history storing unit  63 , the contents link storing unit  64 , and a parameter storing unit  65 . 
     The contents information storing unit  61  stores contents information associated with the respective contents offered by the contents distribution service (such as feature values and metadata). 
     The user relationship storing unit  62  stores information about relationship between the respective users using the contents distribution service, such as friendship, and followee-follower relationship. 
     The user history storing unit  63  stores user histories indicating activity histories of the respective users in the contents distribution service. The user histories include use histories of the respective users, and information about feedback for contents, for example. 
     The contents link storing unit  64  stores contents link information associated with contents links created by the respective users. 
     The parameter storing unit  65  stores parameters of learning models used for selection of recommended users to be recommended to the respective users. 
     [Structure Example of Contents Link Creation Processing Unit  41 ] 
       FIG. 3  illustrates a structure example of the functions of the contents link creation processing unit  41  of the server  11 . The contents link creation processing unit  41  is configured to contain a link source contents selecting unit  101 , a feature value significance calculating unit  102 , a link feature value selecting unit  103 , a link destination contents selecting unit  104 , and a contents link creating unit  105 . 
     The link source contents selecting unit  101  selects at least one choice of the link source contents based on the user histories or the like stored in the user history storing unit  63 . The link source contents selecting unit  101  supplies information indicating the selected choice(s) of link source contents to the contents link creating unit  105  and the display control unit  52 . 
     The feature value significance calculating unit  102  calculates significance of respective feature values of contents based on the contents information stored in the contents information storing unit  61 , and the user histories or the like stored in the user history storing unit  63 . The feature value significance calculating unit  102  supplies information indicating the result of calculated significance of the respective feature values to the link feature value selecting unit  103 . 
     The link feature value selecting unit  103  selects at least one choice of the link feature value based on the calculated significance by the feature value significance calculating unit  102 . The link feature value selecting unit  103  supplies information indicating the selected choice(s) of the link feature value to the contents link creating unit  105  and the display control unit  52 . 
     The link destination contents selecting unit  104  selects at least one choice of the link destination contents based on the link feature value, and the contents information or the like stored in the contents information storing unit  61 . The link destination contents selecting unit  104  supplies information indicating the selected choice(s) of the link destination contents to the contents link creating unit  105  and the display control unit  52 . 
     The contents link creating unit  105  determines the link source contents, the link feature value, and the link destination contents from the respective choices based on user&#39;s instructions or the like received from the client  12  via the communicating unit  31 , and creates a contents link. The contents link creating unit  105  stores contents link information indicating the created contents link in the contents link storing unit  64 . 
     In addition, upon determination of the link source contents or the link feature value, the contents link creating unit  105  supplies information indicating the determined link source contents or link feature value to the link source contents selecting unit  101 , the feature value significance calculating unit  102 , the link feature value selecting unit  103 , the link destination contents selecting unit  104 , and the display control unit  52 . Furthermore, upon determination of the link destination contents, the contents link creating unit  105  supplies information indicating the determined link destination contents to the display control unit  52 . 
     [Structure Example of Recommended User Selection Processing Unit  42 ] 
       FIG. 4  illustrates a structure example of the functions of the recommended user selection processing unit  42  of the server  11 . The recommended user selection processing unit  42  is configured to contain a learning unit  131 , a predicting unit  132 , an expected value of variation calculating unit  133 , and a recommended user selecting unit  134 . 
     The learning unit  131  learns models for prediction of parameters used for selection of recommended users to be recommended to the respective users. The learning unit  131  is configured to contain an evaluation prediction learning unit  141 , a user relationship prediction learning unit  142 , a feedback prediction learning unit  143 , and a feedback evaluation prediction learning unit  144 . 
     The evaluation prediction learning unit  141  creates models (hereinafter referred to as evaluation prediction models) for prediction of evaluation values given by the respective users for contents based on the contents information stored in the contents information storing unit  61 , and the user histories or the like stored in the user history storing unit  63 . The evaluation prediction learning unit  141  stores parameters representing the created evaluation prediction models in the parameter storing unit  65 . 
     The user relationship prediction learning unit  142  creates models (hereinafter referred to as user relationship prediction models) for prediction of probabilities that the respective users accept other users based on user relationship information stored in the user relationship storing unit  62 , and the user histories or the like stored in the user history storing unit  63 . The user relationship prediction learning unit  142  stores parameters representing the created user relationship prediction models in the parameter storing unit  65 . 
     The feedback prediction learning unit  143  creates models (hereinafter referred to as feedback prediction models) for prediction of probabilities that the respective users give feedback of comments, evaluations or the like for respective contents based on the contents information stored in the contents information storing unit  61 , and the user histories or the like stored in the user history storing unit  63 . The feedback prediction learning unit  143  stores parameters representing the created feedback prediction models in the parameter storing unit  65 . 
     The feedback evaluation prediction learning unit  144  creates models (hereinafter referred to as feedback evaluation prediction models) for prediction of evaluation values given by the respective users for respective contents presented together with feedback from other users based on the contents information stored in the contents information storing unit  61 , and the user histories or the like stored in the user history storing unit  63 . In other words, the feedback evaluation prediction model is a model for prediction of an evaluation value given by an arbitrary user B other than an arbitrary user A for contents presented by the user A together with feedback. The feedback evaluation prediction learning unit  144  stores parameters representing the created feedback evaluation prediction models in the parameter storing unit  65 . 
     The predicting unit  132  predicts parameters used for selection of recommended users to be recommended to the respective users while referring to the models created by the learning unit  131 . The predicting unit  132  is configured to contain an evaluation predicting unit  151 , a user relationship predicting unit  152 , a feedback predicting unit  153 , and a feedback evaluation predicting unit  154 . 
     The evaluation predicting unit  151  predicts evaluation values given by the respective users for respective contents while referring to the evaluation prediction models stored in the parameter storing unit  65 . The evaluation predicting unit  151  supplies information indicating the result of prediction to the expected value of variation calculating unit  133 . 
     The user relationship predicting unit  152  predicts probabilities that the respective users accept other users while referring to the user relationship prediction models stored in the parameter storing unit  65 . The user relationship predicting unit  152  supplies information indicating the result of prediction to the expected value of variation calculating unit  133 . 
     The feedback predicting unit  153  predicts probabilities that the respective users give comments, evaluations or the like for respective contents while referring to the feedback prediction models stored in the parameter storing unit  65 . The feedback predicting unit  153  supplies information indicating the result of prediction to the expected value of variation calculating unit  133 . 
     The feedback evaluation predicting unit  154  predicts evaluation values given by the respective users for respective contents presented together with feedback from other users while referring to the feedback evaluation prediction models stored in the parameter storing unit  65 . The feedback evaluation predicting unit  154  supplies information indicating the result of prediction to the expected value of variation calculating unit  133 . 
     The expected value of variation calculating unit  133  calculates expected values of variation produced by differences between actions of the respective users before recommendation of other users to the respective users and actions of the respective users after the recommendation (hereinafter referred to as expected values of variation) based on the result of predictions obtained by the respective units of the predicting unit  132 . The actions of the users in this context include determination whether contents are to be used or not, and evaluation for contents, for example. The expected value of variation calculating unit  133  supplies information indicating the result of calculation to the recommended user selecting unit  134 . 
     The recommended user selecting unit  134  selects recommended users to be recommended to the respective users based on the result of calculation of the expected values of variation. In addition, the recommended user selecting unit  134  creates a recommended user ranking which lines up the selected recommended users in descending order of the expected value of variation. The recommended user selecting unit  134  supplies information indicating the created recommended user ranking to the presented matters setting unit  51 . 
     [Structure Example of Presented Matters Setting Unit  51 ] 
       FIG. 5  illustrates a structure example of the functions of the presented matters setting unit  51  of the server  11 . The presented matters setting unit  51  is configured to contain a recommended user list creating unit  171 , a contents link list creating unit  172 , a history-related list creating unit  173 , and a presented contents selecting unit  174 . 
     The recommended user list creating unit  171  creates a recommended user list corresponding a list of the recommended users based on the recommended user ranking. The recommended user list will be detailed later. The recommended user list creating unit  171  supplies the created recommended user list to the contents link list creating unit  172 , the history-related list creating unit  173 , the presented contents selecting unit  174 , and the display control unit  52 . 
     The contents link list creating unit  172  creates a contents link list corresponding to a list of contents links based on the contents link information stored in the contents link storing unit  64 . The contents link list will be detailed later. The contents link list creating unit  172  supplies the created contents link list to the presented contents selecting unit  174  and the display control unit  52 . 
     The history-related list creating unit  173  creates a history-related list integrating contents links into contents use histories of the users based on the user histories stored in the user history storing unit  63 , and the contents link information stored in the contents link storing unit  64 . The history-related list will be detailed later. The history-related list creating unit  173  supplies the created history-related list to the presented contents selecting unit  174  and the display control unit  52 . 
     The presented contents selecting unit  174  selects contents presented as contents use histories of other users (hereinafter referred to as presented contents) based on the contents information stored in the contents information storing unit  61  and the user histories or the like stored in the user history storing unit  63  when information about other users are presented to the users. The presented contents selecting unit  174  supplies information indicating the result of selection of the presented contents to the display control unit  52 . 
     [Structure Example of Clients  12 ] 
       FIG. 6  illustrates a structure example of the functions of each of the clients  12 . Each of the clients  12  is configured to contain a communicating unit  201 , a reproducing unit  202 , an output control unit  203 , an output unit  204 , an input unit  205 , and a contents link creating unit  206 . 
     The communicating unit  201  communicates with the server  11  via the network  13  to transmit and receive various types of information, instructions and others associated with the contents distribution service. 
     The reproducing unit  202  receives contents from the server  11  via the network  13  and the communicating unit  201 , and reproduces the received contents. The reproducing unit  202  supplies reproduction data obtained as a result of reproduction to the output control unit  203 . 
     The output control unit  203  receives, from the server  11  via the network  13  and the communicating unit  201 , information to be presented to the users, such as information associated with contents and comments given for contents, and information to be displayed together with reproduction of contents. Moreover, the output control unit  203  controls display of dynamic images and still images, and output of voice and the like displayed on and outputted from the output unit  204 . Furthermore, the output control unit  203  controls display of various types of information received from the server  11  and displayed on the output unit  204 . 
     The output unit  204  is constituted by a display device of various types, such as a display, and an audio output device of various types, such as a speaker and an audio output terminal, for example. 
     The input unit  205  is constituted by an input device of various types, such as a keyboard, a mouse, a touch panel, and a microphone. The input unit  205  supplies information, instructions and the like inputted from the users to the reproducing unit  202 , the output control unit  203 , and the contents link creating unit  206 . 
     The contents link creating unit  206  performs processes associated with creation of contents links while transmitting and receiving information, various types of instructions and others to and from the server  12  via the network  13  and the communicating unit  201 . 
     [Processes Performed by Information Processing System  1 ] 
     The processes performed by the information processing system  1  are hereinafter described with reference to  FIGS. 7 through 27 . 
     (Contents Link Creating Process) 
     Initially, a contents link creating process performed by the server  11  is described with reference to a flowchart shown in  FIG. 7 . 
     This process is started when a request for creation of a contents link is transmitted to the server  11  from any one of the clients  12  via the network  13 , for example. 
     A user who creates a contents link in this process is hereinafter referred to as an active user. 
     In step S 1 , the link source contents selecting unit  101  of the contents link creation processing unit  41  selects choices of link source contents. For example, the link source contents selecting unit  101  selects contents suited for the taste of the active user to determine the selected contents as choices of link source contents based on the user histories stored in the user history storing unit  63 . The contents suited for the taste of the active user in this context include contents frequently used or highly evaluated by the active user, for example. 
     A specific example of the method for selecting the choices of link source contents is now discussed with reference to  FIG. 8 . 
       FIG. 8  shows a structure example of a part of data of the user histories stored in the user history storing unit  63 . According to this example, the user histories include evaluation values given by the respective users for respective contents, and the number of times of reproduction of the contents. 
     For example, when the requirement set for the selection of choices of link source contents is reproduction of contents ten times or more, contents C 1 , C 2 , and C 4  are selected for choices of link source contents in case of an active user of a user U 1 . 
     Then, the link source contents selecting unit  101  supplies information indicating the selected choices of link source contents to the contents link creating unit  105  and the display control unit  52 . 
     In step S 2 , the server  11  presents the choices of link source contents. More specifically, the display control unit  52  creates display control data for displaying the choices of link source contents, and transmits the data to the client  12  of the active user via the communicating unit  31 . 
     The output control unit  203  of the client  12  of the active user receives the display control data from the server  11  via the network  13  and the communicating unit  201 . The output control unit  203  allows the output unit  204  to display the choices of link source contents based on the display control data. As a result, the choices of link source contents are presented to the active user. 
     In step S 3 , the server  11  determines link source contents. 
     For example, the active user operates the input unit  205  of the client  12  to select a desired one from the presented choices of link source contents. At this time, the active user may be allowed to select link source contents from contents other than the presented choices of contents. The contents link creating unit  206  obtains information indicating the result of selection of the link source contents from the input unit  205 , and transmits the information to the server  11  via the communicating unit  201 . 
     The communicating unit  31  of the server  11  receives information indicating the result of selection of the link source contents from the client  12  via the network  13 , and supplies the information to the contents link creating unit  105 . The contents link creating unit  105  determines the contents selected by the active user as link source contents. 
     The contents link creating unit  105  may select link source contents from the respective choices regardless of the selection by the active user. 
     For example, the contents link creating unit  105  may select the contents exhibiting the highest rate of achievement of criteria for the selection of choices from the respective choices, and determine the selected contents as link source contents. For example, in the example discussed with reference to  FIG. 8 , the contents C 1  reproduced largest number of times are selected as link source contents when the user U 1  is the active user. 
     Alternatively, the contents link creating unit  105  may randomly select link source contents from the respective choices, for example. 
     The contents link creating unit  105  supplies information indicating the determined link source contents to the link source contents selecting unit  101 , the feature value significance calculating unit  102 , the link feature value selecting unit  103 , the link destination contents selecting unit  104 , and the display control unit  52 . 
     In step S 4 , the feature value significance calculating unit  102  calculates significance of the respective feature values on the basis of at least either the active user or the link source contents. 
     Initially, an example of a method for calculating the significance of the respective feature values on the basis of the active user is discussed. 
     For example, it is considered that a feature value effective for prediction of an evaluation value given by the active user for contents is such a feature value which has a large effect on an evaluation given by the active user for contents and is therefore highly significant for the active user. Accordingly, the feature value significance calculating unit  102  executes regression analysis of evaluations given by the active user for contents from feature values of the contents based on the contents information stored in the contents information storing unit  61  and the user histories stored in the user history storing unit  63 . Then, the feature value significance calculating unit  102  sets calculated regression coefficients for significance given by the active user for the respective feature values. When L 1  regularization is applied to regression analysis (e.g., see Non-Patent Document 1), regression coefficients not contributing to prediction of evaluation values can be set to zero. 
     Non-Patent Document 1: Tibshirani, R., “Regression Shrinkage and Selection via the Lasso”, Journal of the Royal Statistical Society, Series B, Vol. 58, No. 1, 1996 
     A specific example of a method for obtaining significance of feature values using regression analysis is now described with reference to  FIGS. 9 and 10 , and  FIG. 8  described above. 
       FIG. 9  shows a specific example of feature values of contents contained in the contents information stored in the contents information storing unit  61 . According to this example, three types of feature values of tempo, sound density, and rhythm instruments ratio have been obtained. 
       FIG. 10  shows an example of regression coefficients obtained by regression analysis of evaluation values given by the respective users for contents from the feature values of the contents based on the user histories shown in  FIG. 8  and the contents feature values shown in  FIG. 9 .  FIGS. 8 and 9  show only a part of the user histories and the contents feature values. Accordingly, the data shown in  FIGS. 8 and 9  does not completely correspond to the result of calculation shown in  FIG. 10 . 
     Then, the respective regression coefficients shown in  FIG. 10  are determined as significance given by the respective users for the respective feature values. For example, according to this example, the tempo is the feature value to which the user U 1  gives the highest significance, while the sound density is the feature value to which the user U 1  gives the lowest significance. On the other hand, the sound density is the feature value to which a user U 2  gives the highest significance, while the rhythm instruments ratio is the feature value to which the user U 2  gives the lowest significance. Further, the rhythm instruments ratio is the feature value to which a user U 3  gives the highest significance, while the tempo is the feature value to which the user U 3  gives the lowest significance. 
     Alternatively, considering that the contents links already created by the users as criteria of similarities between the users, the significance of the feature values may be learned based on those contents links. 
     For example, such a method is known which learns Mahalanobis distances or the like between a plurality of similar pairs when these pairs are given (e.g., see Non-Patent Document 2). 
     Non-Patent Document 2: Yang, L., “Distance Metric Learning: A Comprehensive Survey”, Michigan State University, 2006 
     According to the technique of this Non-Patent Document 2, elements corresponding to respective feature values in a covariance matrix can be applied as they are as significance of the respective feature values when the covariance matrix is limited to a diagonal matrix. 
     Alternatively, for example, significance given by the active user for respective feature values may be established based on the histories of the link feature values used by the active user for creating contents links. For example, significance may be raised for the feature values more frequently used by the active user as the link feature values for creating previous contents links, and may be lowered for the feature values less frequently used by the active user as the link feature values for creating previous contents links. 
     An example of a method for calculating significance of respective feature values on the basis of link source contents is now discussed. 
     For example, such a method is conceivable which gives higher significance to a feature value showing a more remarkable feature of link source contents, and gives lower significance to a feature value not showing a remarkable feature of link source contents. 
     Examples of the feature value showing a more remarkable feature of link source contents in this context includes a feature value exhibited by the link source contents but exhibited by other contents with low probability, and on the contrary, a feature value exhibited by other contents with high probability but not exhibited by the link source contents. For example, when the link source contents belong to an extremely rare category, it is considered that category information provided as one feature value is given high significance. 
     In addition, when the feature values are expressed by successive values, examples of the feature value showing a remarkable feature of link source contents include a feature value of link source contents representing large deviation from the average value of the whole values. For example, when the tempo of link source contents is extremely fast or extremely slow in comparison with other average contents, it is considered that the tempo of the musical composition corresponding one of the feature values is given high significance. 
     The method for calculating significance of feature values discussed herein is only an example, and other methods may be employed for this calculation. Moreover, significance of feature values may be calculated on the basis of both the active user and the link source contents. 
     Then, the feature value significance calculating unit  102  supplies information indicating the result of calculation of significance of the respective feature values to the link feature value selecting unit  103 . 
     In step S 5 , the link feature value selecting unit  103  selects choices of the link feature value. More specifically, the link feature value selecting unit  103  selects a predetermined number of feature values for choices of the link feature value in descending order of significance calculated in step S 4 . The link feature value selecting unit  103  supplies information indicating the selected choices of the link feature value to the contents link creating unit  105  and the display control unit  52 . 
     In step S 6 , the choices of the link feature value are presented on the client  12  of the active user in a manner similar to the process in step S 2 . 
     In step S 7 , the server  11  determines the link feature value. 
     For example, the active user operates the input unit  205  of the client  12  to select one or more desired choices from the presented choices of the link feature value. In this case, the active user may be allowed to select a feature value other than the presented choices of the feature value for the link feature value by directly inputting text data or by other methods. Moreover, in this case, the active user may be allowed to input information for explaining the link feature value. This information can be displayed in a balloon  422   a  or the like shown in  FIG. 25  described later, for example. 
     The contents link creating unit  206  obtains information indicating the result of selection of the link feature value from the input unit  205 , and transmits the information to the server  11  via the communicating unit  201 . 
     The communicating unit  31  of the server  11  receives the result of selection of the link feature value from the client  12  via the network  13 , and supplies the information to the contents link creating unit  105 . The contents link creating unit  105  determines the feature value selected by the active user as the link feature value. 
     The contents link creating unit  105  may select a link feature value from the respective choices regardless of the selection by the active user. 
     For example, the contents link creating unit  105  may select one or more feature values in descending order of significance and determine the selected feature value(s) as the link feature value(s). 
     Alternatively, for example, the contents link creating unit  105  may randomly select link feature value(s) from the respective choices. 
     The contents link creating unit  105  supplies information indicating the determined link feature value to the link source contents selecting unit  101 , the feature value significance calculating unit  102 , the link feature value selecting unit  103 , the link destination contents selecting unit  104 , and the display control unit  52 . 
     In step S 8 , the link destination contents selecting unit  104  selects choices of link destination contents. 
     More specifically, the link destination contents selecting unit  104  calculates similarities between the link source contents and the other contents only based on the determined link feature value. For example, the link destination contents selecting unit  104  calculates Euclidean distances between the link source contents and the other contents only by using the link feature value based on the contents information stored in the contents information storing unit  61 . Then, the link destination contents selecting unit  104  determines the calculated Euclidean distances as similarities between the link source contents and the respective contents. Moreover, the link destination contents selecting unit  104  selects a predetermined number of contents in descending order of similarity to the link source contents, and determines the selected contents as choices of the link destination contents. 
     At this time, the choices of the link destination contents may be selected from the contents previously used by the active user for facilitating selection by the active user. In this case, the range of selection of the choices of the link destination contents may be arbitrarily established in accordance with the level of the targeted use. For example, the range of selection may be constituted by a set of contents previously reproduced by the active user, or may be expanded to a set of contents previously browsed by the active user. 
     Then, the link destination contents selecting unit  104  supplies information indicating the selected choices of the link destination contents to the contents link creating unit  105  and the display control unit  52 . 
     In step S 9 , the choices of the link destination contents are presented on the client  12  of the active user in a manner similar to the process in step S 2 . 
     In step S 10 , the server  11  determines the link destination contents. 
     For example, the active user operates the input unit  205  of the client  12  to select one desired choice from the choices of the presented link destination contents. In this case, the active user may be allowed to select link destination contents from contents other than the presented choices. The contents link creating unit  206  obtains information indicating the result of selection of the link destination contents from the input unit  205 , and transmits the information to the server  11  via the communicating unit  201 . 
     The communicating unit  31  of the server  11  receives information indicating the result of selection of the link destination contents from the client  12  via the network  13 , and supplies the information to the contents link creating unit  105 . The contents link creating unit  105  determines the contents selected by the active user as the link destination contents. 
     The contents link creating unit  105  may select link destination contents from the respective choices regardless of selection by the active user. 
     For example, the contents link creating unit  105  may select from the respective choices the contents having the highest similarity to the link source contents with regard to the link feature value, and determine the selected contents as the link destination contents. 
     Alternatively, for example, the contents link creating unit  105  may randomly selects link destination contents from the respective choices. 
     Then, the contents link creating unit  105  supplies information indicating the determined link destination contents to the display control unit  52 . 
     In step S 11 , the contents link creating unit  105  stores the created contents link. More specifically, the contents link creating unit  105  stores in the contents link storing unit  64  the link source contents and the link destination contents constituting the created contents link, the used link feature value, and information about the active user who has created the contents link (such as user ID) in association with one another. 
     Thereafter, the content link creating process ends. 
     As in the manner described above, the respective choices of the link source contents, the link feature value, and the link destination contents are presented in accordance with the taste, action and the like of the active user. Accordingly, the highly satisfactory contents link can be easily created. Moreover, the contents similar to the link source contents are presented as choices of the link destination contents based on the selected link feature value. Thus, the active user is allowed to find link destination contents associated with the link source contents from various points of view. As a consequence, contents link constituted by an appropriate combination of contents can be easily created. 
     According to the foregoing description, the example which sequentially presents choices of the link source contents, the link feature value, and the link destination contents, and sequentially determines the respective contents and value. However, these contents and value may be presented at a time, and determined at a time. 
       FIG. 11  shows an example of a contents link creating screen displayed on the client  12  when choices of the link source contents, the link feature value, and the link destination contents are presented at a time and determined at a time. Dotted lines in the figure are additional lines for explanation, and not displayed in practical cases. 
     This contents link creating screen is divided into a link source contents display area  301 , a link feature value display area  302 , and a link destination contents display area  303 . Furthermore, a decision button  304  is disposed below the link destination contents display area  303 . 
     Icons  311 - 1  through  311 - 7 , and buttons  312 L and  312 R are shown in the link source contents display area  301 . The icons  311 - 1  through  311 - 7  represent respective choices of the link source contents, and are expressed by jackets of albums or singles containing the corresponding contents (musical compositions), or by images of artists, for example. 
     The icons  311 - 1  through  311 - 7  are abbreviated as icons  311  when distinction between the respective icons  311 - 1  through  311 - 7  is not needed. 
     When the button  312 L is pressed, the respective icons  311  scroll in the left direction. At this the icon  311  displayed at the left end disappears, and the icon  311  corresponding to a choice of the link source contents and positioned out of the screen, if any, appears at the right end. On the other hand, when the button  312 R is pressed, the respective icons  311  scroll in the right direction. At this time, the icon  311  displayed at the right end disappears, and the icon  311  corresponding to a choice of the link source contents and positioned out of the screen, if any, appears at the left end. In case of display on a touch screen, the icons  311  may be made to scroll by flick operation. 
     The contents corresponding to the icon  311  displayed in a large size at the center become the link source contents selected at that time. Accordingly, selection of the link source contents can be switched by scrolling the icons  311  to the left or to the right to change the icon  311  displayed at the center. In addition, the title and the artist name of the contents corresponding to the icon  311  displayed at the center are displayed below the corresponding icon. 
     Icons  313 - 1  through  313 - 3 , and buttons  314 L and  314 R are displayed in the link feature value display area  302 . Each of the icons  313 - 1  through  313 - 3  represents a choice of the link feature value, and expressed by display of a character string representing a link feature value, for example. 
     The icons  313 - 1  through  313 - 3  are abbreviated as icons  313  when distinction between the respective icons  313 - 1  through  313 - 3  is not needed. 
     When the button  314 L is pressed, the respective icons  313  scroll in the left direction. At this time, the icon  313  displayed at the left end disappears, and the icon  313  corresponding to a choice of the link feature value and positioned out of the screen, if any, appears at the right end. On the other hand, when the button  314 R is pressed, the respective icons  313  scroll in the right direction. At this time, the icon  313  displayed at the right end disappears, and the icon  313  corresponding to a choice of the link feature value and positioned out of the screen, if any, appears at the left end. In case of display on a touch screen, the icons  313  may be made to scroll by flick operation. 
     The feature value corresponding to the icon  313  displayed in a large size at the center become the link feature value selected at that time. Accordingly, selection of the link feature value can be switched by scrolling the icons  313  to the left or to the right to change the icon  313  displayed at the center. 
     Icons  315 - 1  through  315 - 7 , and buttons  316 L and  316 R are shown in the link destination contents display area  303 . The icons  315 - 1  through  315 - 7  represent respective choices of the link destination contents, and are expressed by jackets of albums or singles containing the corresponding contents (musical compositions), or by images of artists, for example. 
     The icons  315 - 1  through  315 - 7  are abbreviated as icons  315  when distinction between the respective icons  315 - 1  through  315 - 7  is not needed. 
     When the button  316 L is pressed, the respective icons  315  scroll in the left direction. At this time, the icon  315  displayed at the left end disappears, and the icon  315  corresponding to a choice of the link destination contents and positioned out of the screen, if any, appears at the right end. On the other hand, when the button  316 R is pressed, the respective icons  315  scroll in the right direction. At this time, the icon  315  displayed at the right end disappears, and the icon  315  corresponding to a choice of the link destination contents and positioned out of the screen, if any, appears at the left end. In case of display on a touch screen, the icons  315  may be made to scroll by flick operation. 
     The contents corresponding to the icon  315  displayed in a large size at the center become the link destination contents selected at that time. Accordingly, selection of the link destination contents can be switched by scrolling the icons  315  to the left or to the right to change the icon  315  displayed at the center. In addition, the title and the artist name of the contents corresponding to the icon  315  displayed at the center are displayed below the corresponding icon. 
     When selection of the link source contents is switched, for example, the choices of the link destination contents are changed based on the link source contents and the link feature value selected at that time. In accordance with this change, the icons  315  in the link destination contents display area  303  are renewed to icons representing choices of the link destination contents after the change. 
     When the choices of the link feature value are selected on the basis of the link source contents, the display of the choices of the link feature value may be renewed in accordance with the change of the link source contents. Then, the display of the choices of the link destination contents may be renewed in accordance with the changes of the link source contents and the link feature value. 
     Similarly, when selection of the link feature value is switched, the choices of the link destination contents are renewed based on the link source contents and the link feature value selected at that time. In accordance with this change, the icons  315  in the link destination contents display area  303  are renewed to icons representing the choices of the link destination contents after the change. 
     Then, the active user presses the decision button  304  when a desired combination are obtained in accordance with individual changes of the link source contents, the link feature value, and the link destination contents. The press of this button allows creation of a contents link constituted by the combination of the link source contents, the link feature value, and the link destination contents selected at that time. 
     In the manner discussed above, the active user can create a contents link while checking the combination of the link source contents, the link feature value, and the link destination contents on one screen. Moreover, until press of the decision button, the active user can freely change the link source contents, the link feature value, and the link destination contents. Accordingly, the active user can create a desired contents link more easily. 
     (User Recommendation Learning Process) 
     A user recommendation learning process performed by the server  11  is hereinafter described with reference to a flowchart shown in  FIG. 12 . 
     This process is executed regularly, or when predetermined requirements are met, for example. The predetermined requirements in this context include addition or deletion of a user, and input of instructions to the server  11  to request execution of the user recommendation learning process, for example. 
     In step S 101 , the evaluation prediction learning unit  141  performs evaluation prediction learning. More specifically, the evaluation prediction learning unit  141  creates evaluation prediction models for prediction of evaluation values (such as likes and dislikes, and five-grade evaluations) given by the respective users for contents by using a predetermined learning method, based on the contents information stored in the contents information storing unit  61 , and the user histories or the like stored in the user history storing unit  63 . 
     The learning method employed by the evaluation prediction learning unit  141  may be an arbitrary method, including a method described in following Non-Patent Document 3, for example. 
     Non-Patent Document 3: Su, X., Khoshgoftaar, T. M., “A survey of Collaborative Filtering Techniques”, Advances in Artificial Intelligence, vol. 2009, 2009 
     The evaluation prediction learning unit  141  stores parameters representing the created evaluation prediction models in the parameter storing unit  65 . 
     In step S 102 , the user relationship prediction learning unit  142  performs user relationship prediction learning. More specifically, the user relationship prediction learning unit  142  creates user relationship prediction models for prediction of probabilities that the respective users accept other users by using a predetermined learning method, based on the user relationship information stored in the user relationship storing unit  62 , and user histories or the like stored in the user history storing unit  63 . 
     The probability that a user A accepts a user B in this context means the probability that the user A establishes friendship with the user B, or follows the user B on the contents distribution service, for example. In other words, the probability in this context means the probability that the user A becomes a follower of the user B. 
     The learning method employed by the user relationship prediction learning unit  142  may be an arbitrary method, including a method described in following Non-Patent Document 4, for example. 
     Non-Patent Document 4: Hasan, M. A., Zaki, M. J., “A Survey of Link Prediction in Social Networks”, Social Network Data Analytics, Springer, 2011 
     The user relationship prediction learning unit  142  stores parameters representing the created user relationship prediction models in the parameter storing unit  65 . 
     In step S 103 , the feedback prediction learning unit  143  performs feedback prediction learning. More specifically, the feedback prediction learning unit  143  creates feedback prediction models for prediction of probabilities that the respective users give feedback such as comments and evaluations for respective contents by using a predetermined learning method, based on the contents information stored in the contents information storing unit  61 , and the user histories or the like stored in the user history storing unit  63 . 
     The learning method employed by the feedback prediction learning unit  143  may be an arbitrary method, including a method described in following Non-Patent Document 5, for example. 
     Non-Patent Document 5: Zhang, Y., et al., “Sentiment Analysis for Online Reviews Using an Author-Review-Object Model”, Asia Information Retrieval Societies Conference, 2011 
     The feedback prediction learning unit  143  stores parameters representing the created feedback prediction models in the parameter storing unit  65 . 
     In step S 104 , the feedback evaluation prediction learning unit  144  performs feedback evaluation prediction learning. More specifically, the feedback evaluation prediction learning unit  144  creates feedback evaluation prediction models for prediction of evaluations given by the respective users for respective contents presented together with feedback from other users by using a predetermined learning method, based on the contents information stored in the contents information storing unit  61 , and the user histories or the like stored in the user history storing unit  63 . 
     The learning method employed by the feedback evaluation prediction learning unit  144  may be an arbitrary method, including a method described in Japanese Patent Application No. 2011-168975 (hereinafter referred to as Patent Document 2) filed by the same applicant as that of the present application, for example. 
     The method described in Patent Document 2 is briefly described herein. 
     For example, when feedback for contents and a user giving the feedback are presented together with the contents, the feedback evaluation prediction learning unit  144  collects data of the feedback (hereinafter referred to as meta-feedback) given by the presented user for the presented combination (hereinafter referred to as CUF tuple). Moreover, the feedback evaluation prediction learning unit  144  determines whether the collected meta-feedback is positive or negative. 
     Then, the feedback evaluation prediction learning unit  144  creates an acceptance model for the CUF tuple by employing a determining method such as support vector machine and logistic regression (e.g., see Non-Patent Document 6), assuming that each of the feature value of the user, the feature value of the contents, and the feature value of the feedback as one vector, and that the positive determination result and the negative determination result of the meta-feedback as a positive example and a negative example, respectively. 
     Non-Patent Document 6: Bishop C. M., “Pattern Recognition and Machine Learning”, Springer-Verlag, 2006 
     For example, feature value vectors of CUF tuples are expressed as shown in  FIG. 13 . Each value in the columns in  FIG. 13  represents a feature value contained in the corresponding evaluation target tuple shown in the first row for the corresponding item shown in the second or the following row of the contents, user, and feedback. For example, the first record in  FIG. 13  shows feature values contained in a (C 1 , U 2 , F 2 ) tuple as an evaluation target tuple for the contents, user, and feedback. More specifically, feature values of contents C 1  for the respective items of “category rock”, “category pops”, “category jazz”, “tempo”, “sound volume”, and “rhythm instruments ratio” are 1, 0, 0, 40, 55, and 40, respectively. The feature values of a user U 1  for the respective items of “male”, “female”, “20s or under”, “30s”, and “40s or over” are 1, 0, 0, 1, 0, respectively. The feature values of feedback F 1  for the respective items of “cool”, “!”, a feature value 4, and a feature value 5 are 1, 1, 0, and 0, respectively. 
     Then, a weight to be given to each feature value for calculating a predicted degree of acceptance of the corresponding CUF topic by the respective users is obtained by learning which use logistic regression or other methods, based on the result of determination of the meta-feedback as shown in  FIG. 14 . 
     Each value in the respective columns in  FIG. 14  shows a weight given to the corresponding user shown in the first row for the corresponding item shown in the second or the following row. For example, the first record in  FIG. 14  shows the weights given to a user A 1  for the respective items. More specifically, the weights given to the user A 1  for the respective items of “category rock”, “category pops”, “category jazz”, “tempo”, “sound volume”, and “rhythm instruments ratio” for contents are 0.85, 0.20, −0.42, 0.021, 0.152, and 0.002, respectively. The weights given to the user A 1  for the respective items of “male”, “female”, “20s or under”, “30s”, and “40s or over” for users are 0.51, 0.22, 0.11, 0.53, and 0.33, respectively. The weights given to the user A 1  for the respective items of “cool”, “!”, the feature value 4, and the feature value 5 concerning feedback are 0.79, 0.35, 1.24, and 0.80, respectively. 
     An expression of addition using the respective weights shown in  FIG. 14  generates an acceptance model. More specifically, the sum of the respective feature values contained in the CUF tuple for the contents, user, and feedback, and multiplied by the corresponding weights in  FIG. 14  becomes the predicted degree of acceptance of the CUF tuple by the users. 
     The acceptance models of the CUF tuples by the respective users can be used as feedback evaluation prediction models for the respective users. 
     When the result of determination of the meta-feedback is expressed by three values or more, such as five grades, linear regression or other methods may be employed instead of support vector machine and logistic regression mentioned above. 
     Then, the feedback evaluation prediction learning unit  144  stores parameters representing the created feedback evaluation prediction models in the parameter storing unit  65 . 
     Thereafter, the user recommendation learning process ends. 
     (Contents Link Sharing Process) 
     A contents link sharing process performed by the server  11  is hereinafter described with reference to a flowchart shown in  FIG. 15 . 
     This process is started when a request for display of a screen containing a contents link is transmitted to the server  11  from any one of the clients  12  via the network  13 , for example. 
     A user requesting display of a screen containing a contents link in this process is hereinafter referred to as an active user. 
     In step S 201 , the server  11  performs a recommended user ranking creating process. The details of the recommended user ranking creating process are now explained with reference to a flowchart in  FIG. 16 . 
     In step S 231 , the evaluation predicting unit  151  predicts evaluation values of the active user for respective contents. More specifically, the evaluation predicting unit  151  calculates predicted evaluation values r uc  given by an active user u for respective contents c by using evaluation prediction models stored in the parameter storing unit  65 . The evaluation predicting unit  151  supplies information indicating the result of calculation to the expected value of variation calculating unit  133 . 
     In step S 232 , the predicting unit  132  selects a user not yet selected for a target user from users not followed by the active user (hereinafter referred to as not-followed users). The target user in this context refers to a user targeted for the processes in step S 233  through S 235 . 
     In step S 233 , the user relationship predicting unit  152  predicts a probability that the active user accepts the target user. More specifically, the user relationship predicting unit  152  calculates a user acceptance probability P (v|u) corresponding to a probability that the active user u accepts a target user v by using user relationship prediction models stored in the parameter storing unit  65 . The user relationship predicting unit  152  supplies information indicating the result of calculation to the expected value of variation calculating unit  133 . 
     In step S 234 , the feedback predicting unit  153  predicts a probability that the target user gives feedback for the respective contents. More specifically, the feedback predicting unit  153  calculates a feedback giving probability P (c, f|v) corresponding to the probability that the target user v gives feedback f for the respective contents c by using user relationship prediction models stored in the parameter storing unit  65 . The feedback predicting unit  153  supplies information indicating the result of calculation to the expected value of variation calculating unit  133 . 
     In step S 235 , the feedback evaluation predicting unit  154  predicts evaluation values given by the active user for the respective contents presented together with feedback from the target user. More specifically, the feedback evaluation predicting unit  154  calculates feedback predicted evaluation values r uc  (f, v) given by the active user u for the respective contents c presented together with the feedback f from the target user v by using user relationship prediction models stored in the parameter storing unit  65 . The feedback evaluation predicting unit  154  supplies information indicating the result of calculation to the expected value of variation calculating unit  133 . 
     In step S 236 , the predicting unit  132  determines whether any user not selected for a target user remains in the users not followed by the active user. When it is determined that there remains any user not selected for a target user in the users not followed by the active user, the flow returns to step S 232 . 
     Thereafter, the processes in step S 232  through S 236  are repeatedly executed until it is determined that there remains no user not selected for a target user in the users not followed by the active user in step S 236 . By this method, the foregoing user acceptance probability P(v|u), the feedback giving probability P(c, f|v), and the feedback predicted evaluation value r uc (f, v) are calculated for each of the not followed users v of the active user u. 
     On the other hand, when it is determined that there remains no user not selected for a target user in the users not followed by the active user in step S 236 , the flow proceeds to S 237 . 
     In step S 237 , the expected value of variation calculating unit  133  calculates expected values of variation. 
     In general, an expected value of variation of a user&#39;s action varied by a certain incentive given to the user can be calculated by (probability of acceptance of incentive)×(variation of action varied by incentive). The expected value of variation of the user&#39;s action is maximized when an incentive corresponding to an argmax incentive  (probability of acceptance of incentive)×(variation of action varied by incentive) is given to the user. 
     Therefore, the expected value of variation calculating unit  133  calculates an expected value of variation E(v|u) of the active user u for the respective not-followed users v by using following Equation (1) and Equation (2). 
     [Mathematical Formula 1] 
     In Equation (1), P(v|u) is a probability that the active user u accept an incentive (user v), and Σ c P(c, f|v)Δ(r uc ,r uc (f,v)) is variation of an active user&#39;s action varied by the incentive. In addition, the expected value of variation E(v|u) represents a difference between the actions of the active user u when the incentive corresponding to the user v (recommendation of user v) is given and when the incentive is not given. Accordingly, the probability that the action of the active user u varies after recommendation of the user v rises as an absolute value of the expected value of variation E(v|u) increases. On the other hand, the probability that the action of the active user u after recommendation of the user v varies lowers as the absolute value of the expected value of variation E(v|u) decreases. 
     On the other hand, Δ(r uc ,r uc (f,v)) represents variation of the predicted evaluation value given by the active user u for the contents c as a result of presentation of feedback from the user v. In this case, the expected value of variation E(v|u) represents an expected value of variation of the evaluation value given by the active user u for each of the contents when the incentive corresponding to the user v is given. Accordingly, in the case of a positive value of the expected value of variation E(v|u), the larger the absolute value of the expected value of variation E(v|u) becomes, the higher the probability becomes that the evaluation value given by the active user for the contents generally rises after recommendation of the user v. On the other hand, in the case of a negative value of the expected value of variation E(v|u), the larger the absolute value of the expected value of variation E(v|u) becomes, the higher the probability becomes that the evaluation value given by the active user for the contents generally lowers after recommendation of the user v. 
     Then, the expected value of variation calculating unit  133  supplies information indicating the result of calculation to the recommended user selecting unit  134 . 
     In step S 238 , the recommended user selecting unit  134  creates recommended user ranking. Initially, the recommended user selecting unit  134  selects recommended users to be recommended to the active user from choices of users corresponding to users not followed by the active user. More specifically, the recommended user selecting unit  134  selects a predetermined number of users having the expected values of variation E(v|u) equivalent to or larger than a predetermined threshold, or in descending order of the expected value of variation E(v|u), from users not followed by the active user to determine the selected users as recommended users. 
     Then, the recommended user selecting unit  134  creates recommended use ranking where selected recommended users are lined up in descending order of the expected value of variation E(v|u). The recommended user selecting unit  134  supplies information indicating the created recommended user ranking to the recommended user list creating unit  171  of the presented matters setting unit  51 . 
     Thereafter, the recommended user ranking creating process ends. 
     When the user v corresponding to another user is recommended to the active user u in a typical social service, for example, it is expected that the following flow of actions occurs.
     1. The active user u accepts the recommended user v. That is, the active user u follows the user v or establishes friendship with the user v.   2. The user v gives the feedback f for the certain contents c.   3. The active user u reads the feedback f from the user v and accepts the contents c. For example, the active user u listens to the sample of the contents c, reproduces the contents c, or purchases the contents c.   

     As discussed above, the probability that the active user u accepts the user v and then gives a higher evaluation value for each of the contents becomes higher as the expected value of variation E(v|u) of the recommended user v increases (when the expected value of variation E(v|u) is positive and has a large absolute value). As a result, the probability that the active user u accepts the new contents rises. Accordingly, acceptance of new contents by the active user u is more efficiently realized by recommendation of users in the upper grades of the recommended user ranking to the active user u. 
     In lieu of foregoing Equation (1), the expected value of variation E(v|u) may be calculated using following Equation (3). 
     [Mathematical Formula 2] 
     In Equation (3), C v  represents a set of contents for which the user v dives feedback. 
     According to this modified example, the expected value of variation E(u,v) is calculated not using the feedback giving probability P(c,f|v), but only using a set of contents for which the user v actually gives feedback. Accordingly, the feedback prediction learning in step S 103  in  FIG. 12 , and the calculation of the feedback giving probability P(c,f|v) in step S 234  in  FIG. 16  can be omitted. 
     A specific example of selection of recommended users using the expected value of variation E(v|u) obtained by Equation (3) is now described with reference to  FIGS. 17 through 19 . 
       FIG. 17  shows an example of the result of calculation of a predicted evaluation value r uc  given by the active user u. According to this example, the predicted evaluation values r uc  given by the active user u for contents C 11  through C 14  are 4.13, 2.21, 5.46, and 3.30, respectively. 
       FIG. 16  shows an example of the result of calculation of the user acceptance probability P(v|u) of the user v by the active user u. According to this example, the user acceptance probabilities P(v|u) of users U 11  through U 14  by the active user u are 0.49, 0.11, 0.63, and 0.61, respectively. 
       FIG. 19  shows an example of the result of calculation of the feedback predicted evaluation value r uc (f,v) given by the active user u for the contents c for which feedback is given from the user v. According to this example, the feedback predicted evaluation values r uc (f,v) given by the active user u for the contents C 11 , C 12 , and C 14  for which feedback is given from the user U 11  are 5.24, 2.54, and 3.02, respectively. Moreover, the feedback predicted evaluation values r uc (f, v) given by the active user u for the contents C 12  and C 13  for which feedback is given from the user U 12  are 5.73 and 5.31, respectively. 
     Concerning the feedback predicted evaluation value r uc (c,v) having a value of N/A, no feedback is given for targeted contents from the user v. Thus, this feedback predicted evaluation value is not included in the calculation of the expected value of variation E(v|u). 
     For example, the expected value of variation E(U 11 |u) given by the active user u for the user U 11  is calculated as 0.568 (=0.49×(5.24−4.13+2.54−2.21+3.02−3.30)) using Equation (3). On the other hand, the expected value of variation E(U 12 |u) given by the active user u for the user U 12  is calculated as 0.371 (=0.11×(5.73−2.21+5.31−5.46) using Equation (3). 
     Accordingly, in comparison between the user U 11  and the user U 12 , it is considered that the user U 11  is more suited for a user to be recommended to the active user u. Thus, when either one of the users U 11  and U 12  is to be determined as a recommended user, the user U 11  is selected for a recommended user. 
     Returning to  FIG. 15 , in step S 202 , the recommended user list creating unit  171  creates a recommended user list based on the recommended user ranking. More specifically, the recommended user list creating unit  171  selects recommended users to be actually recommended to the active user from users contained in the recommended user ranking on the basis of predetermined criteria. 
     The criteria for selection of recommended users may be arbitrarily established. For example, recommended users may be users from the top to a predetermined rank in the recommended user ranking, or may be only followees of the active user included in the recommended user ranking. 
     Then, the recommended user list creating unit  171  creates a recommended user list where the elected recommended users are lined up in accordance with a predetermined rule. For example, the recommended user list creating unit  171  creates the recommended user list by listing the recommended users in descending order of the expected value of variation E(v|u). The recommended user list creating unit  171  supplies the created recommended user list to the contents link list creating unit  172 , the history-related list creating unit  173 , the presented contents selecting unit  174 , and the display control unit  52 . 
     Users to be presented to the active user, such as followees of the active user and users included in the recommended list are hereinafter referred to as presented users. 
     In step S 203 , the contents link list creating unit  172  creates a contents link list. More specifically, the contents link list creating unit  172  creates a contents link by reading contents links created by the presented users from the contents link storing unit  64 , and arranging the read contents links in accordance with a predetermined rule. 
     The rule for arranging the contents links may be arbitrarily established. For example, the contents links may be arranged in time series in the order of the creation date from newest to oldest, or from oldest to newest. Alternatively, contents links created by presented users having similar feature values may be arranged adjacent to each other. Conversely, contents links created by presented users having dissimilar feature values may be arranged adjacent to each other. 
     The contents link list creating unit  172  supplies the created contents link list to the presented contents selecting unit  174  and the display control unit  52 . 
     In step S 204 , the history-related list creating unit  173  creates a history-related list. For example, the history-related list creating unit  173  extracts contents used by the active user in a predetermined period up to the present time based on the uses history of the active user stored in the user history storing unit  63 . 
     At this time, the range of extraction of contents may be arbitrarily established in accordance with the level of the targeted use. For example, the range of extraction may be a set of contents previously reproduced by the active user, or may be expanded to a set including contents browsed by the active user for obtaining information on the contents. 
     Moreover, the history-related list creating unit  173  extracts contents corresponding to link source contents or link destination contents of the contents links created by the presented users, and the corresponding contents links from the extracted contents, based on the contents link information stored in the contents link storing unit  64 . 
     Then, the history-related list creating unit  173  creates a history-related list based on the extracted contents and contents links. The history-related list creating unit  173  supplies the created history-related list to the presented contents selecting unit  174  and the display control unit  52 . 
     The history-related list will be detailed later. 
     In step S 205 , the represented contents selecting unit  174  performs a presented contents selecting process. The details of the presented contents selecting process are hereinafter discussed with reference to a flowchart shown in  FIG. 20 . 
     In step S 261 , the presented contents selecting unit  174  selects one user whose presented contents are not yet selected from the presented users. 
     In this process, the user selected in step S 261  is hereinafter referred to as a target user. 
     In step S 262 , the presented contents selecting unit  174  selects a group of contents which become presented choices. For example, the presented contents selecting unit  174  extracts a group of contents (hereinafter referred to as presented contents choice group) included in the upper ranks in the contents use history of the target user and also included in the contents use history of the active user based on the user histories stored in the user history storing unit  63 . 
     The contents included in the upper ranks in the contents use history of the target user in this context refer to contents to which the target user gives high significance, and are determined based on the number of times of use by the target user, or the evaluation values given by the target user, for example. 
     In addition, the contents included in the contents use history of the active user in this context refer to contents previously used by the active user. 
     The levels of use of contents included in the use histories of the target user and of the active user used in this step may be arbitrarily established. For example, the use history may include only contents previously reproduced, or may further include contents previously browsed for obtaining information on the contents. 
     An example of extraction of a presented contents choice group based on the number of times of reproduction of contents is hereinafter described with reference to  FIG. 21 .  FIG. 21  shows a history of the number of times of reproduction of respective contents by the active user U 1  and the target user U 11 . 
     For example, requirements for extraction of the presented contents choice group are established such that the number of times of reproduction by the target user U 11  is 10 times or larger, and that the contents are previously reproduced by the active user. In this case, contents C 11 , C 12 , C 14 , C 16 , and C 17  are extracted. 
     The contents included in the presented contents choice group are hereinafter referred to as presented contents choices as well. 
     In step S 263 , the presented contents selecting unit  174  calculates distances between the respective contents included in the extracted contents group (i.e., presented contents choices). 
     Assuming that a distance between a contents c i  and a contents c j  is d(c i ,c j ), an arbitrary method may be employed for calculation of the distance d(c i ,c j ). For example, Euclidean distance in the feature value space of contents, or cosine similarity based on the evaluation histories of the users may be used for calculating the distance d(c i ,c j ). 
     A specific example of the calculation method of the distances between the respective contents is hereinafter discussed with reference to  FIGS. 22 and 23 .  FIG. 22  shows an example of feature values of the contents C 11 , C 12 , C 14 , C 16 , and C 17  extracted in the example of  FIG. 21  mentioned above. According to this example, three types of feature values of tempo, sound density, and rhythm instruments ratio are used. For example, the value of tempo of the contents C 11  is 55, the value of sound density of the contents C 11  is 39, and the rhythm instruments ratio of the contents C 11  is 26. 
       FIG. 23  shows the result of calculation of Euclidean distances between the respective contents based on the feature values shown in  FIG. 22 . For example, distances between the contents C 11  and the respective contents of the contents C 12 , C 14 , C 16 , and C 17  are 23.5, 41.8, 35.6, and 33.3. 
     In step S 264 , the presented contents selecting unit  174  selects contents to be presented at the head. More specifically, the presented contents selecting unit  174  selects one piece of contents (hereinafter referred to as head presented contents) to be presented at the head together with the target user from the presented contents choice group by a predetermined method. The presented contents selecting unit  174  adds the selected head presented contents to a presented contents group of the target user. 
     The presented contents group in this context refers to a set of contents selected for contents to be presented to the active user together with information of the target user from the presented contents choice group. 
     The method for selecting the head presented contents may be an arbitrarily determined as example, contents included in the highest rank in the use history of the target user may be selected from the presented contents choice group. Alternatively, the head presented contents may be randomly selected. 
     For example, when the head presented contents of the target user U 11  are selected based on the number of times of reproduction in the case of the example in  FIG. 21 , the contents C 12  reproduced the highest number of times in the presented contents choice group are selected for the head presented contents. 
     It is preferable that the head presented contents are selected from the contents not yet selected for the presented contents of the other presented users. 
     In step S 265 , the presented contents selecting unit  174  selects contents dissimilar to the presented contents group. More specifically, the presented contents selecting unit  174  selects one piece of contents whose distances from the presented contents group become the maximum from the presented contents choices not yet selected for presented contents. 
     For example, assuming that contents not yet selected for presented contents are ca i  (i=1, 2, . . . , M), and that contents already selected for presented contents are cb j  (j=1, 2, . . . , N), the presented contents selecting unit  174  selects a piece of contents ca i  meeting argmax i Σ j d(ca i ,cb j ). As a result, contents dissimilar to the presented contents already selected are selected. 
     For example, according to this example, the contents C 14  whose distance from the contents C 12  already selected for presented contents becomes the maximum are selected. When the contents C 12  and contents C 14  are selected for presented contents, the contents C 16  whose average value of the distances from the contents C 12  and from the contents C 14  becomes the maximum are selected for presented contents. 
     At this time, the presented contents already selected and presented by the other presented users may be included in the presented contents group used for calculation of distances. In this case, contents dissimilar to the presented contents of the other presented users as well as the presented contents of the target user can be selected. 
     In step S 266 , the presented contents selecting unit  174  determines overlap between the selected contents and the (already selected) presented contents of the other presented users. 
     In step S 267 , the presented contents selecting unit  174  determines whether the selected contents overlap with the presented contents of the other presented users based on the result of determination in step S 266 . When it is determined that the selected contents do riot overlap with the presented contents of the other presented users, the flow proceeds to step S 268 . 
     In step S 268 , the presented contents selecting unit  174  adds the selected contents to the presented contents group of the target user. 
     Then, the flow proceeds to step S 269 . 
     On the other hand, when it is determined that the selected contents overlap with the presented contents of the other presented users in step S 267 , the process in step S 268  is skipped. Thereafter, the flow proceeds to step S 269 . In other words, the selected contents are not added to the presented contents group of the target user. In addition, the selected contents are excluded from the presented contents choice group of the target user. 
     In step S 269 , the presented contents selecting unit  174  determines whether the number of the presented contents reach a predetermined number. When it is determined that the number of the presented contents of the target user does not reach the predetermined number, the flow returns to step S 265 . 
     Thereafter, the processes in steps S 265  through S 269  are repeatedly executed until it is determined that  the number of the presented contents reach the predetermined number in step S 269 . By this method, a predetermined number of contents not coinciding with the presented contents of the other presented users are selected from the contents included in the upper ranks in the use history of the target user and included in the use history of the active user, and determined as presented contents of the target user. Moreover, the presented contents thus selected have the least possible similarities to each other. Accordingly, a wide variety of contents types are presented by each presented user. 
     On the other hand, when it is determined that the number of the presented contents reach the predetermined number in step S 269 , the flow proceeds to step S 270 . 
     In step S 270 , the presented contents selecting unit  174  determines whether the presented contents of all the presented users are selected. When it is determined selection of the presented contents of all the presented users is not completed, the flow returns to step S 261 . 
     Thereafter, the processes in steps S 261  through S 270  are repeatedly executed until it is determined that the presented contents of all the presented users are selected in step S 270 . By this method, the predetermined number of the presented contents are selected for each of the entire presented users. 
     On the other hand, when it is determined that the presented contents of all the presented users are selected in step S 270 , the flow proceeds to step S 271 . 
     In step S 271 , the presented contents selecting unit  174  supplies information indicating the result of selection of the presented contents of the respective users to the display control unit  52 . 
     Thereafter, the presented contents selecting process ends. 
     Returning to  FIG. 15 , the server  11  presents contents links and others in step S 206 . More specifically, the display control unit  52  creates display control data for displaying a screen containing at least one of the recommended user list, the contents link list, and the history-related list. The combination of the recommended user list, the contents link list, and the history-related list varies depending on requests from the clients  12 , for example. The display control unit  52  transmits the created display control data to the client  12  of the active user via the communicating unit  31 . 
     The output control unit  203  of the client  12  of the active user receives the display control data from the server  11  via the network  13  and the communicating unit  201 . The output control unit  203  allows the output unit  204  to display a screen containing at least one of the recommended user list, the contents link list, and the history-related list based on the display control data. 
     A specific example of the recommended user list, the contents link list, and the history-related list displayed on the client  12  is hereinafter discussed with reference to  FIGS. 24 through 27 . 
       FIG. 24  illustrates a display example of the recommended user list. Dotted lines in the figure are additional lines added for explanation, and not displayed in practical cases. 
     According to this example, users A through C are vertically lined up and presented as recommended users. 
     User images  401   a  through  401   c  are images representing the users A through C, respectively, and expressed by pictures or avatars of the respective users. In addition, the name, nickname or the like of the user A is displayed on the right of the user image  401   a.    
     A presented contents display area  402  is provided on the right of the user image  401   a  and below the name or the like of the user A. Images representing the presented contents of the user A are horizontally lined up in the presented contents display area  402 . These images representing the contents are expressed by jackets of albums or singles containing the respective contents, or by pictures of artists, for example. This applies to images representing other contents in  FIGS. 24 through 27 . 
     A contents link display area  403   a   1  is provided below the presented contents display area  402 . A contents link created by the user A is displayed within the contents link display area  403   a   1 . More specifically, an image representing the link source contents is displayed on the left side of an arrow directed to the right, while an image representing the link destination contents is displayed on the right of the arrow. 
     A feedback button  404 - 1  is displayed on the left of the contents link display area  403   a   1 . The feedback button  404 - 1  is a button pressed to give favorable feedback when the active user likes the contents link within the contents link display area  403   a   1 . Displayed on the right of the feedback button  404 - 1  is the number of users giving feedback up to the present time (hereinafter referred to as feedback user number) for the contents link within the contents link display area  403   a   1 . 
     A contents link display area  403   a   2  is provided below the contents link display area  403   a   1 . The contents link display area  403   a   2  displays a contents link created by the user A and different from the contents link within the contents link display area  403   a   1 . 
     Moreover, a feedback button  404 - 2  similar to the feedback button  404 - 1  is displayed on the left of the contents link display area  403   a   2 . Furthermore, the feedback user number for the contents link within the contents link display area  403   a   2  is displayed on the left of the feedback button  404 - 2 . 
     A contents link display area  403   b  is provided on the right of the user image  401   b.  A contents link created by the user B is displayed in the contents link display area  403   b.    
     A contents link display area  403   c  is provided on the right of the user image  401   c.  A contents link created by the user C is displayed in the contents link display area  403   c.    
     The order of display of the recommended users may be arbitrarily determined. For example, the recommended users may be lined up in descending order of the expected value of variation E(v|u). Alternatively, users having similar feature values may be arranged adjacent to each other. Conversely, users having dissimilar feature values may be arranged adjacent to each other. 
       FIG. 25  shows an example of display of the contents link list. Dotted lines in the figure are additional lines added for explanation, and not displayed in practical cases. 
     According to this example, contents links created by the users A through C corresponding to followees of the active user, information about the users A through C, and others are displayed in three rows in the vertical direction. 
     A contents link display area  421   a  is provided in the left part of the first row. The displayed matters in the contents link display area  421   a  are substantially similar to those of the contents link display areas  403   a   1  through  403   c  in  FIG. 24 , but are different in that artist names of the respective contents are displayed below images representing the respective contents. 
     The balloon  422   a  is displayed on the right of the contents link display area  121   a.  Displayed within the balloon  422   a  is information showing the link feature value used for creation of the contents link within the contents link display area  421   a,  i.e., information showing the relationship between the two contents constituting the contents link. This information may be formed by labels or the like of the link feature values provided by the server  11 , or may be information created by the user who has created the contents link. In addition, a feedback button  423   a  for the contents link within the contents link display area  421   a  is displayed within the balloon  422   a.    
     A user image  424   a  representing the user who has created the contents link within the contents link display area  421   a  is displayed on the right of the balloon  422   a.  The name, nickname or the like of the user A is displayed on the right of the user image  424   a.    
     A presented contents display area  425   a  similar to the presented contents display area  402  in  FIG. 24  is provided on the right of the user image  424   a  and below the name or the like of the user A. Images representing the presented contents of the user A are displayed in the presented contents display area  425   a.    
     A contents link created by the user B and information about the user B are displayed on the second row in a manner similar to the display on the first row. A contents link created by the user C and information about the user C are displayed on the third row in a manner similar to the display on the first and second rows. 
     The order of display of the contents links may be arbitrarily determined. For example, the contents links may be arranged in time series of the creation date from newest to oldest or from oldest to newest. Alternatively, contents links created by users having similar feature values may be arranged adjacent to each other. Conversely, contents links created by users having dissimilar feature values may be arranged adjacent to each other. 
     In addition, as illustrated in  FIG. 26 , the contents links of the recommended users may be mixed and displayed together with the contents links of the followees of the active user. 
     More specifically, the contents link list shown in  FIG. 26  includes a shaded row inserted between the contents links of the second row and the third row of  FIG. 25 . This inserted row contains a contents link of a user D corresponding to a recommended user, and information about the user D displayed in the inserted row in the same manner as the display of the other rows. Moreover, a star mark representing that the user D is a recommended user is displayed on the upper left of an image  424   d  representing the user D. 
     The contents links of the recommended users may be made distinguishable from the contents links of the followees as illustrated in this figure, or may be made undistinguishable from the contents links of the followees. Besides, the contents links of the recommended users may be displayed adjacent to the contents links of the folly having feature values exhibiting highest similarity to the feature values of the contents links of the recommended users. 
       FIG. 27  shows a display example of the history related list. The history-related list displays contents links and the like containing the contents included in the use history of the active user (hereinafter referred to as history contents) together with the previous use history of the active user. 
     More specifically, according to this example, information about history contents and others are displayed in two rows. The order of display of the history contents may be arbitrarily determined. For example, the history contents may be arranged in the order of the use date from newest to oldest, or may be arranged in descending order of number of times of use (number of times of reproduction in this example). 
     A history contents display area  451   a  is provided in the upper left part of the first row. The history contents display area  451   a  displays information about one piece of the history contents. More specifically, the history contents display area  451   a  displays an image representing the history contents, the title of the history contents, the name of the artist, the evaluation value, and the number of times of reproduction. The evaluation value displayed may be the evaluation value given by the active user, or may be the average value of the evaluation values given by the respective users. In addition, the number of times of reproduction displayed may the number of times of reproduction by the active user, or the total number of times of reproduction by all the users. 
     Link contents display areas  452   a   1  and  452   a   2  are lined up in the vertical direction and displayed below the history contents display area  451   a.  Displayed in the link contents display areas  452   a   1  and  452   a   2  are the history contents displayed in the history contents display area  451   a,  and information about contents constituting the contents link. 
     More specifically, an image representing contents corresponding to the link destination of the history contents displayed in the history contents display area  451   a,  and the name of the artist are displayed in the link contents display area  452   a   1  where an arrow directed to the right is displayed. This display presents to the active user a contents link determining the history contents within the history contents display area  451   a  as the link source, and the contents within the link contents display area  452   a   1  as the link destination. 
     A balloon  453   a   1  is displayed on the right of the link contents display area  452   a   1 . Displayed within the balloon  453   a   1  is information indicating the link feature value used for creation of the contents link constituted by the history contents within the history contents display area  451   a  and the contents within the link contents display area  452   a   1 . 
     Displayed on the right of the balloon  453   a   1  is a user image  454   a   1  representing a user B who has created the contents link constituted by the history contents within the history contents display area  451   a  and the contents within the link contents display area  452   a   1 . In addition, displayed on the right of the user image  454   a   1  is the name, nickname or the like of the user B. 
     A presented contents display area  455   a   1  similar to the presented contents display area  402  in  FIG. 24  is provided on the right of the user image  454   a   1  and below the name or the like of the user B. Images representing the represented contents of the user B are displayed in the presented contents display area  455   a   1 . 
     On the other hand, an image representing the contents corresponding to the link source of the history contents displayed in the history contents display area  451   a,  and the name of the artist are displayed in a link contents display area  452   a   2  where an arrow directed to he left is displayed. This display presents to the active user a contents link which determines the contents within the link contents display area  452   a   2  as the link source, and the history contents within the history contents display area  451   a  as the link destination. 
     A balloon  453   a   2  is displayed on the right of the link contents display area  452   a   2 . Displayed within the balloon  453   a   2  is information indicating the link feature value used for creation of the contents link constituted by the history contents within the history contents display area  452   a   2  and the contents within the link contents display area  452   a   2 . 
     Displayed on the right of the balloon  453   a   2  is a user image  454   a   2  representing a user C who has created the contents link constituted by the history contents within the history contents display area  451   a  and the contents within the link contents display area  452   a   2 . In addition, displayed on the right of the user image  454   a   2  is the name, nickname or the like of the user C. 
     A presented contents display area  455   a   2  similar to the presented contents display area  402  in  FIG. 24  is provided on the right of the user image  454   a   2  and below the name or the like of the user C. Images representing the represented contents of the user C are displayed in the represented contents display area  455   a   2 . 
     In a manner similar to the first row, displayed in the second row are information about different history contents of the active user, contents links including the history contents, information about the users who have created the contents links and others. 
     The contents links presented together with the history contents may be limited to contents links created by the followees of the active user or may include contents links created by recommended users. 
     Then, one or more of the screens shown in  FIGS. 24 through 27  are combined and displayed on the client  12  of the active user. 
     Thereafter, the contents link sharing process ends. 
     In the manner discussed herein, the active user can use the contents links created by the other users. As a result, the active user can find associated contents based on the viewpoints of the other users. Particularly, in the history-related list, the active user can find contents associated with the contents used by the active user. Moreover, the active user can expand his/her view concerning the contents, or can easily find contents suited for his/her taste through the contents links created by the other users. 
     Furthermore, users who can change the action of the active user with high probability are recommended. Therefore, the active user accepts these users and accepts new contents with high probability. When users simply similar to the active user (such as users having similar contents use histories) are recommended, the active user does not discover a lot from these users. On the other hand, when users who can change the action of the active user with high probability are recommended, the active user discovers novel matters (that is, the active user experiences contents in new categories) with high probability. 
     In addition, contents dissimilar to the other contents are presented in the contents use histories (i.e., presented contents) presented together with the recommended users. In this case, accurate tastes of the presented users can be offered without vias of the tastes. Accordingly, the active user can easily grasp tastes of the respective presented users, and easily recognize similarities and dissimilarities between the active user and the presented users. As a result, the active user more easily feels a sense of similarity with other users, and therefore more easily finds users suited for the taste of the active user. Furthermore, the active user obtains opportunities for experiencing a wider variety of contents. 
     In addition, overlaps between the contents presented by the respective presented users are reduced, wherefore similarities and dissimilarities of the tastes of the respective presented users are more clearly conceived. Furthermore, the active user obtains opportunities for experiencing a wider variety of contents. 
     2. MODIFIED EXAMPLES 
     Modified examples of the embodiment according to the present technology discussed above are hereinafter described. 
     Modified Example 1 
     Modified Examples as to Contents Link 
     Discussed in the above description is the example of sharing created contents links between users. However, the present technology is applicable to creation of a contents link by a user within the local environment of the user for personal use of the contents link, for example. 
     Moreover, the present technology is applicable to creation of a contents link connecting a part of contents (such as introduction or chorus of musical composition) and a part of other contents, or a contents link connecting the whole contents and a part of other contents. In creating a link connecting a part of contents, such a contents link may be created which connects different parts within the same contents. 
     Furthermore, discussed in the above description is the example of presenting information about a user who has created a contents link together with the contents links. However, the information about the user having created the contents link may be omitted. 
     Furthermore, the present technology is applicable to creation of a contents ink connecting one piece of contents to multiple pieces of contents, or connecting multiple pieces of contents to one piece of contents, and further applicable to creation of a contents link connecting multiple pieces of contents to multiple pieces of contents. 
     Furthermore, the present technology is applicable to creation of a contents link connecting three or more contents. 
     Furthermore, the present technology is applicable to creation of links of various types of contents including books, games, software, websites, news, advertisements and the like in the form of characters, voice, images and the like, as well as music and movies discussed above. 
     Furthermore, the present technology is applicable to creation of links of various types of items other than contents, including various types of commodities, and users and celebrities on social services. 
     Modified Example 2 
     Modified Examples as to Presentation of User and Contents 
     Discussed in the above description is the example of presenting a plurality of users. However, the present technology is applicable to presentation of one presented user. More specifically, advantages similar to the foregoing advantages can be offered when a plurality of pieces of contents dissimilar to each other are presented together with one presented user. 
     Moreover, discussed in the above description is the example of presenting a plurality of pieces of presented contents by each of presented users. However, the present technology is applicable to presentation of one piece of contents by each of presented users. More specifically, in presenting a plurality of presented users, for example, advantages similar to the foregoing advantages can be offered when one piece of presented contents is presented by each of multiple presented users such that the presented contents do not overlap with one another. 
     Furthermore, discussed in the above description is the example of prohibiting overlap of presented contents between respective presented users. However, overlap of presented contents may he allowed within a predetermined range. In other words, the number of presented contents overlapping between presented users may be limited to a predetermined range of numbers. 
     For example, the total number of presented contents overlapping with one another may be limited to a predetermined range of numbers. For example, when ten pieces of presented contents are presented in total by all presented users, overlap of up to two pieces of contents in total is allowed. 
     Alternatively, for example, the number of contents presented by each presented user and overlapping with contents of other presented users may be limited to a predetermined range of numbers. For example, when three pieces of presented contents are presented by each presented user, only one piece of contents presented by each presented user and overlapping with other presented contents is allowed. 
     Alternatively, for example, the number of presented users presented for one piece of presented contents may be limited. For example, up to two presented users are allowed to be presented for one piece of presented contents. 
     Discussed in the above description is the example of presenting a contents link created by a presented user and presented contents together with the presented user. However, the presented matters may be only either one of the contents link and the presented contents. More specifically, only the contents link may be presented together with the presented user, or only the presented contents may be presented together with the presented user. 
     Furthermore, the present technology is applicable to presentation of links of various types of contents, including books, games, software, websites, news, advertisements and the like in the form of characters, voice, images and the like, as well as music and movies discussed above, together with presented users. 
     Furthermore, the present technology is applicable to presentation of links of various types of items other than contents, including various types of commodities, and users and the like on social services, together with presented users. 
     Modified Example 3 
     Other Modified Examples 
     Definitions of use of items according to the present technology vary in accordance with targeted types of items and the like. For example, when the item is music, the use of the item corresponds to reproduction or the like. When the item is a movie, the use of the item corresponds to sample listening or the like. Moreover, when the item is a commodity, the use of the item corresponds to purchase, browsing of information or the like. Furthermore, when the item is another user, the use of the item corresponds to communication or the like with the user. 
     Discussed in the above description is the example of using an expected value of variation of an evaluation value given by a user for contents as an expected value of variation. However, an expected value of variation of other parameters representing an action of a user may be employed. 
     [Structure Example of Computer] 
     A series of the foregoing processes may be implemented by hardware, or may be implemented by software. When the series of the processes are implemented by software, a program constituting the software is installed in a computer. Examples of the computer in this context include a computer incorporated in dedicated hardware, and a computer capable of performing various types of functions under various types of programs installed in the computer, such as a general-purpose computer, for example. 
       FIG. 28  is a block diagram showing a structure example of hardware of a computer implementing a series of the foregoing processes under a program. 
     According to the computer, a CPU (Central Processing Unit)  601 , a ROM (Read Only Memory)  602 , and a RAM (Random Access Memory)  603  are connected with one another via a bus  604 . 
     An input/output interface  605  is further connected with the bus  604 . An input unit  606 , an output unit  607 , a memory unit  608 , a communication unit  609 , and a drive  610  are connected with the input/output interface  605 . 
     The input unit  606  is constituted by a keyboard, a mouse, a microphone and others. The output unit  607  is constituted by a display, a speaker and others. The memory unit  608  is constituted by a hard disk, a non-volatile memory and others. The communication unit  609  is constituted by a network interface and others. The drive  610  drives a removable medium  611  such as a magnetic disk, an optical disk, a magneto-optical disk, and a semiconductor memory. 
     According to the computer thus constructed, the CPU  601  loads a program stored in the memory unit  608  into the RAM  603  via the input/output interface  605  and the bus  604 , and implements the program to perform the series of the foregoing processes, for example. 
     The program implemented by the computer (CPU  601 ) may be recorded on the removable medium  611  and provided in the form of a package medium, for example. In addition, the program may be provided via a wired or wireless transmission medium such as a local area network, the Internet, and digital satellite broadcasting. 
     According to the computer, the program may be installed into the memory unit  608  via the input/output interface  605  by attaching the removable medium  611  to the drive  610 . Alternatively, the program may be received by the communication unit  359  via a wired or wireless transmission medium, and installed into the memory unit  603 . Instead, the program may be installed in the ROM  602  or the memory unit  608  beforehand. 
     The program implemented by the computer may be a program under which the processes are performed in time series in the order discussed in this specification, or may be a program under which the processes are performed in parallel or at necessary timing such as calls. 
     Moreover, in the present specification, the system refers to a collection of a plurality of constituent elements (such as devices and modules (parts)), and is not limited to a structure which contains all the constituent elements within the same housing. Therefore, a plurality of devices housed in individual housings and connected via a network, and a device which contains a plurality of modules within one housing are both defined as a system. 
     Furthermore, embodiments of the present technology are not limited to the embodiment described herein. Various changes may be made without departing from the scope of the present technology. 
     For example, the present technology may have a form of cloud computing which shares and jointly processes one function between a plurality of devices via a network. 
     Moreover, the respective steps described in conjunction with the foregoing flowcharts may be executed by one device, or may be executed jointly by a plurality of devices. 
     Furthermore, when a plurality of processes are contained in one step, the plurality of processes contained in the one step may be executed by one device, or may be executed jointly by a plurality of devices. 
     In addition, the present technology may have the following configurations.
     (1)   

     An information processing apparatus, including: 
     an item selecting unit selecting a plurality of items dissimilar to each other from a group of items used by a presented user as a target presented to a user; and 
     a presentation control unit allowing the plurality of selected items to be presented to the user together with information about the presented user.
     (2)   

     The information processing apparatus according to (1), wherein the item selecting unit limits the number of items overlapping each other between the presented users when the plurality of presented users are presented to the user.
     (3)   

     The information processing apparatus according (1) or (2), wherein the item selecting unit calculates distances between a selected group of the items and the remaining items based on feature values of the items, and selects an item having the maximum distance when a new item is selected from the group of items used by the presented user.
     (4)   

     The information processing apparatus according to any one of (1) through (3), wherein the item selecting unit selects a plurality of items from items previously used by the user and included in the group of items used by the presented user.
     (5)   

     The information processing apparatus according to any one of (1) through (4), further including: 
     a recommended user selecting unit calculating an expected value of variation of an action of the user, which variation is produced by presentation of a plurality of choices of users to be recommended to the user, and selecting a recommended user to be recommended to the user from the choices of the users based on the calculated expected value, 
     wherein
         the item selecting unit selects a plurality of items dissimilar to each other from a group of items used by the recommended user, and   the presentation control unit allows the plurality of selected items to be presented to the user together with information about the recommended user.       (6)   

     The information processing apparatus according to (5), wherein the recommended user selecting unit calculates the expected value based on a probability that the user accepts the choices of users, and variation of a prediction value of an evaluation given by the user for a predetermined group of items, which variation is produced by presentation of feedback given by the choices of users.
     (7)   

     The information processing apparatus according to (6), wherein the recommended user selecting unit calculates the expected value further based on a probability that the choices of users give feedback for respective items included in the predetermined group of items.
     (8)   

     An information processing method performed by an information processing apparatus, including the steps of: 
     selecting a plurality of items dissimilar to each other from a group of items used by a presented user as a target presented to a user; and 
     allowing the plurality of selected items to be presented to the user together with information about the presented user.
     (9)   

     A program implemented by a computer, including the steps of: 
     selecting a plurality of items dissimilar to each other from a group of items used by a presented user as a target presented to a user; and 
     allowing the plurality of selected items to be presented to the user together with information about the presented user. 
     REFERENCE SIGNS LIST 
     
         
           1  Information processing system 
           11  Server 
           12 - 1  through  12 - n  Client 
           32  Information processing unit 
           33  Storing unit 
           41  Contents link creation processing unit 
           42  Recommended user selection processing unit 
           43  Presentation control unit 
           51  Presented matters setting unit 
           52  Display control unit 
           61  Contents information storing unit 
           62  User relationship storing unit 
           64  Contents link storing unit 
           65  Parameter storing unit 
           101  Link source contents selecting unit 
           102  Feature value significance calculating unit 
           103  Link feature value selecting unit 
           104  Link destination contents selecting unit 
           105  Contents link creating unit 
           131  Learning unit 
           132  Predicting unit 
           133  Expected value of variation calculating unit 
           134  Recommended user selecting unit 
           141  Evaluation prediction learning unit 
           142  User relationship prediction learning unit 
           143  Feedback prediction learning unit 
           144  Feedback evaluation prediction learning unit 
           151  Evaluation predicting unit 
           152  User relationship predicting unit 
           153  Feedback predicting unit 
           154  Feedback evaluation predicting unit 
           171  Recommended user list creating unit 
           172  Contents link list creating unit 
           173  History-related list creating unit 
           174  Presented contents selecting unit 
           203  Output control unit 
           204  Output unit 
           205  Input unit 
           206  Contents link creating unit