JP5937939B2 - Server apparatus and information processing method - Google Patents

Description

  The present invention relates to a technique for evaluating content.

  In order to evaluate content, there is a technique that uses a predetermined action performed by a user who has viewed the content. For example, in Patent Document 1, for the same combination of a set of keywords included in a bookmark and a web page associated with the bookmark from which the set is extracted, the higher the number of associated users, the higher the priority. A technique is described that makes it easy to search (that is, raises the evaluation) by increasing. In other words, this technique uses an action of creating a user bookmark.

JP 2009-282593 A

By the way, even if the number of users who created bookmarks is the same, only one user who specializes in the same field as his / her own creates bookmarks, and the other is only a user whose field of specialization has nothing to do with himself. Is the content for which the bookmark is created, the former content is often highly appreciated for me. However, in the technique of Patent Document 1, if the number of users associated with the above is the same, both the former content (web page) and the latter content are evaluated the same.
Therefore, an object of the present invention is to evaluate content reflecting the kind of user who has browsed the content.

  In order to solve the above-described problem, the present invention provides a server device that evaluates content on the Internet based on a predetermined action by a user, wherein at least one of the first users who has performed the action on the content The calculation means for calculating an index representing the strength of the relationship between the attribute and the attribute of a certain user, and the evaluation based on the attribute of the content for the certain user is calculated by the calculation means. There is provided a server device comprising an evaluation unit that performs operations according to an index.

  Further, the evaluation unit performs an evaluation based on the attribute of the content, and the total value, average value, median value, or maximum value of the index calculated by the calculation unit for the first user is smaller than the content. It may be higher than evaluation based on attributes.

The present invention is a server device that evaluates content to which a character string on the Internet is assigned based on a predetermined action by a user, and the action target of the first user who has performed the action on the content at least one or more content became, a calculation means tendency of the content that is the subject of the action of a certain user to calculate a degree of approximation, is the subject of the said action of a certain user The user who is the target of the action with the content to which the approximate character string approximated to the character string given to the content is the first user, and for the approximate character string, the type of the approximate character string, The first user assigns a weight according to the number of approximate character strings or the degree of approximation of the approximate character strings, and the first user sets the pair of actions. And the calculation means for the degree calculated based on the weight of the applied approximation string content and evaluation based on the tendency of the contents of the for certain users, depending on the degree calculated by said calculation means There is provided a server device comprising an evaluation means for performing

In addition, the evaluation unit performs an evaluation based on the tendency of the content, and the total value, average value, median value, or maximum value of the degree calculated by the calculation unit for the first user is smaller than the content. You may make it high compared with the evaluation based on a tendency.
Further, a list including at least one content information related to the content, the list reflecting the evaluation of the content for the certain user by the evaluation unit, and a generation unit Output means for outputting the processed list.
Further, the action may be an operation of creating access destination data indicating an access destination for accessing the content as content information related to the content.

According to the present invention, a control device that controls a server device that evaluates content on the Internet based on a predetermined action by a user performs the action on the content according to a program stored in a storage device. A calculation step for calculating an index representing the strength of the relationship between at least one attribute of the user and an attribute of a certain user, and the control device according to a program stored in the storage device for the certain user. An information processing method comprising: an evaluation step of performing an evaluation based on the attribute of the content according to the index calculated in the calculation step.

According to the present invention, a control device that controls a server device that evaluates content to which a character string on the Internet is assigned based on a predetermined action by a user performs the action on the content according to a program stored in a storage device. A calculation step of calculating a degree of approximation of a tendency between at least one content that is the target of the action of the first user who has performed and the content that is the target of the action of a certain user , The user who is the target of the action with the content given the approximate character string approximated to the character string given to the content targeted by the action of the certain user is the first user, and the approximate character For a column, the type of the approximate character string, the number of the approximate character strings, or the degree of approximation of the approximate character string With a weight corresponding the calculation step of the degree calculation the first user based on the weight of the subject and the approximate string assigned to the content of the action, the control device, stored in the storage device There is provided an information processing method comprising: an evaluation step of performing an evaluation based on the tendency of the content for the certain user according to the program according to the degree calculated in the calculation step.

  According to the present invention, it is possible to evaluate content reflecting the type of user who browsed the content.

It is a figure which shows the whole structure of a bookmark service provision system. It is a figure which shows an example of a bookmark. It is a figure which shows the hardware constitutions of a bookmark server apparatus. It is a table | surface which shows an example of the data stored in registration user DB. It is a table | surface which shows an example of the data stored in bookmark DB. It is a figure which shows the hardware constitutions of a user apparatus. It is a figure which shows the function structure which a bookmark server apparatus implement | achieves. It is a figure which shows an example of a web page. It is a table | surface which shows an example of an attribute of a certain user and a 1st user. It is a table | surface showing the example of the calculated relationship parameter | index. It is a figure which shows an example of a bookmark list. It is a sequence diagram which shows an example of the operation | movement in an accumulation | storage process. It is a sequence diagram which shows an example of the operation | movement in an evaluation process. It is a figure which shows the function structure which a bookmark server apparatus implement | achieves. It is a figure for demonstrating the calculation method of a tendency approximation degree. It is a figure which shows the function structure which a bookmark server apparatus implement | achieves. It is a figure for demonstrating accumulation | storage of the character string by an accumulation | storage part. It is a figure which shows an example of the tendency tendency calculated. It is a figure which shows the hardware constitutions of a server apparatus. It is a table | surface which shows an example of the data stored in access history DB. It is a sequence diagram which shows an example of operation | movement of an accumulation | storage process.

[First Embodiment]
A first embodiment of the present invention will be described below with reference to the drawings.
[Constitution]
FIG. 1 is a diagram showing an overall configuration of a bookmark service providing system 1. The bookmark service providing system 1 is a system for providing a service (hereinafter referred to as “bookmark sharing service”) that allows a bookmark created by a user to be shared with other users. The bookmark here refers to an access destination (for example, a URL (Uniform Resource) for accessing the content in order to facilitate subsequent browsing when the user is interested in the content after browsing the content. Locator), IP (Internet Protocol) address, etc.). This registration is performed, for example, when the user performs an operation for creating a bookmark (hereinafter referred to as “bookmark creation operation”). The content browsed by the user is expressed by text, images, moving images, sounds, etc. provided on the Internet, for example, a web page. The bookmark may include a part of the content in order to indicate what kind of content the registered access destination is.

  FIG. 2 is a diagram illustrating an example of a bookmark shared in the bookmark service providing system 1. FIG. 2 shows a bookmark A1 displayed on the browser. Bookmark A1 is a bookmark of content a1. The bookmark A1 includes a title D1 and a text D3 that are part of the content a1 (web page in this example), and an access destination (URL in this example) D2 of the content a1. A text D3 indicates a part of the text of the content a1. When the user who has viewed the content a1 performs the bookmark creation operation described above, the text displayed on the browser including a part of the content a1 and the access destination (see FIG. 2). Data indicating that a moving image or the like may be included) is generated. The data thus generated is a bookmark. As described above, the user creates a bookmark of the content viewed by the user by performing a bookmark creation operation.

  Note that the title D1 and the text D3 are examples of a part of the content indicated by the bookmark, and there are cases where an image, a moving image, audio, and the like are indicated as part of the content. Also, the bookmark as shown in FIG. 2 is sometimes called a clip or clipping. That is, the services provided by this system include what are called social clipping services and social clipping services.

  In the bookmark service providing system 1, users who use the bookmark sharing service are registered. In the following, unless otherwise specified, “user” refers to a registered user who has been registered. The bookmark service providing system 1 provides a bookmark sharing service by collecting bookmarks created by registered users and allowing them to be shared by other registered users. Further, the bookmark service providing system 1 evaluates the contents indicated by the bookmarks based on the collected bookmarks, and transmits information reflecting the evaluation (hereinafter referred to as “evaluation information”) to the registered user. provide. Thus, the bookmark service providing system 1 is a system that provides both the bookmark sharing service and the evaluation information to registered users.

  The bookmark service providing system 1 includes a bookmark server device 10, a plurality of user devices 20, a web server device 40, and a network 2. The network 2 includes a mobile communication network or the Internet. The plurality of user devices 20 communicate with the bookmark server device 10 and the web server device 40 via the network 2, respectively.

  The bookmark server device 10 is a device that acquires and accumulates bookmarks created by a user. That is, in the bookmark service providing system 1, bookmarks are collected in the bookmark server device 10. The bookmark server device 10 stores identification information (such as a user ID) for identifying a user and a password as information for authenticating a registered user (hereinafter referred to as “authentication information”), and based on the authentication information. To authenticate registered users. Further, the bookmark server device 10 creates a list including at least one accumulated bookmark (hereinafter referred to as “bookmark list”). The created bookmark list includes bookmarks created by various users. The bookmark server device 10 transmits the created bookmark list to the user device 20 for which the registered user is authenticated. Users can share bookmarks created by other users by browsing the transmitted bookmark list.

  The bookmark server device 10 is an example of a server device that evaluates content on the Internet based on a predetermined action by a user. The predetermined action is predetermined in the present system as a user action used for evaluating content, and is, for example, the bookmark creation operation described above. In the present embodiment, the bookmark server device 10 accumulates bookmarks created by a user performing a bookmark creation operation, and evaluates content using the accumulated bookmarks. The bookmark server device 10 generates, for example, a bookmark list that represents a bookmark indicating the access destination of highly evaluated content, and transmits the generated bookmark list as evaluation information to the user device 20 that has been authenticated as a registered user. To do. In this way, evaluation information is provided to registered users.

The user device 20 is a mobile phone, a smartphone, or the like possessed by the user, and performs mobile communication via the network 2. The user device 20 stores a program of the browser 30 used in this system, and exchanges data with the bookmark server device 10 and the web server device 40 by executing this program, for example.
The web server device 40 is a so-called web server that holds content called a web page and provides the held content, that is, a web page. The web server device 40 transmits data indicating content (hereinafter referred to as “content data”) to the user device 20, whereby the content is provided to the user.

  FIG. 3 is a diagram illustrating a hardware configuration of the bookmark server device 10. The bookmark server device 10 is a computer that includes a control device 11, a storage device 12, and a communication device 13. The control device 11 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and a real time clock. The CPU controls the operation of each device included in the bookmark server device 10 by executing a program stored in the ROM or the storage device 12 using the RAM as a work area. The real time clock has a function of calculating the current date and time. The communication device 13 includes a communication circuit for performing communication via the network 2 and exchanges data with the control device 11. The control device 11 exchanges data with the user device 20 via the communication device 13.

  The storage device 12 is storage means such as a flash memory or a hard disk, and stores data, programs, and the like used for control by the control device 11. The storage device 12 stores data indicating a predetermined numerical value such as a threshold value used in the bookmark server device 10. The storage device 12 stores a registered user DB (database) 121 and a bookmark DB 122. The registered user DB 121 is a database that stores authentication information data indicating authentication information (for example, identification information such as a user ID and a password) for authenticating the user and attributes of the users. The attribute here is an attribute determined in advance in the bookmark service providing system 1 as having a strong relationship with the interest of the user. In the present embodiment, a case where the registered user DB 121 stores authentication information in which a medical worker is a registered user will be described as an example. This is an example, and any user may be registered in the bookmark service providing system 1.

  FIG. 4 is a table showing an example of data stored in the registered user DB 121. In this table, “identification information” and “password”, which are “authentication information”, and “clinical department”, “conference”, “position”, and “age”, which are “attributes”, are associated with each other. For example, the “identification information” “ID001” includes “aaa”, “digestive organs”, “α society”, “leading director” and “50s” “password”, “clinical department”, “society” ”,“ Position ”, and“ Year ”are associated with each other. Similarly, the “identification information” “ID002” includes “bbbb”, “cardiology”, “beta society”, “medical staff”, and “20s”, and the “identification information” “ID003” includes “identification information”. “cccc”, “pediatrics”, “gamma society”, “dean” and “40s” have “identification information” of “ID004” as “dddd”, “cardiology”, “ζ society”, “medical office” “Member” and “30s” are associated with each other.

Thus, the registered user DB 121 stores the attributes of the registered users in association with the identification information for identifying the registered users. These attributes are registered through the user device 20 when the user applies for the provision of the bookmark service.
The attributes shown in FIG. 4 are merely examples. Besides, age, year of obtaining a doctor's license, area of a medical institution (clinic office, hospital, etc.), size of the medical institution and employment type (practitioner or working doctor) Etc. may be stored as attributes. These attributes are not limited to those stored in the registered user DB 121 via the user device 20, but are those that the operator of the bookmark server device 10 converts into data and stores the attributes entered on the paper by the user, It may be acquired from an external device that provides the service.

The bookmark DB 122 is a database that stores bookmarks created by the user in association with the created user attributes.
FIG. 5 is a table showing an example of data stored in the bookmark DB 122. In this table, “bookmark” is associated with “identification information” of the user who created the bookmark. In “Bookmark”, a bookmark such as “A1” shown in FIG. 2 is associated with identification information such as “ID017”. These bookmarks are stored in the bookmark DB 122 as binary files.

  FIG. 6 is a diagram illustrating a hardware configuration of the user device 20. The user device 20 is a computer including a control device 21, a storage device 22, an operation device 23, a display device 24, a voice input / output device 25, and a communication device 26. The control device 21 includes a CPU, a ROM, a RAM, and a real time clock. The CPU controls the operation of each device of the user device 20 by executing a program stored in the ROM or the storage device 22 using the RAM as a work area. The real time clock has a function of calculating the current date and time. The storage device 22 is storage means such as a flash memory or a hard disk, and stores data, programs, and the like used for control by the control device 21. The operation device 23 includes operation elements such as a plurality of keys and touch sensors, and supplies an operation signal corresponding to a user operation to the control device 21. The control device 21 performs processing according to the operation signal. The display device 24 is a display unit having a display surface, and displays an image or the like indicated by data stored in the storage device 22 on the display surface in response to an instruction from the control device 21. The voice input / output device 25 includes a speaker, a microphone, a voice processing circuit, and the like, and performs voice input / output related to a call. The communication device 26 includes a communication circuit for performing wireless communication based on a standard such as a mobile phone and a wireless LAN, and performs mobile communication and wireless LAN communication.

The bookmark service providing system 1 performs a providing process for providing evaluation information and a bookmark sharing service to registered users based on the above hardware configuration. The storage device 12 of the bookmark server device 10 stores a program for performing the providing process. The control device 11 executes the program to control each device shown in FIG.
FIG. 7 is a diagram illustrating a functional configuration realized by the bookmark server device 10. The bookmark server device 10 includes a calculation unit 101, an evaluation unit 102, a generation unit 103, and an output unit 104.

  The calculation unit 101 is an index (indicating the strength of the relationship between at least one attribute of a user who has performed a predetermined action on content (hereinafter referred to as “first user”) and the attribute of a certain user ( Hereinafter, it is a calculation means for calculating “related index”. The first user here is a user who can determine whether or not the bookmark server device 10 has performed a predetermined action, and is, for example, someone of the registered users described above. A certain user is a user whose content is evaluated by the bookmark server device 10, for example, a user who has requested a bookmark list. The calculation unit 101 includes, for example, a storage unit 105, and performs this calculation based on information stored in the storage unit 105.

  The storage unit 105 includes information related to content on the Internet (hereinafter referred to as “content information”) on which a predetermined action (bookmark creation operation in this embodiment) is performed by the user, and at least one of the users. The storage means stores the above attributes in association with each other. The content information is information on a content access destination (URL or the like), a title and a text, or a combination thereof, and is, for example, a bookmark. Hereinafter, a case where a bookmark is used as content information will be described as an example. The storage unit 105 is a function realized by the control device 11, the storage device 12, and the communication device 13 shown in FIG. 3 in cooperation. For example, the storage unit 105 stores bookmarks as content information by the following method.

  The control device 11 authenticates a user who uses the user device 20 by using the authentication information stored in the registered user DB 121 of the storage device 12. Specifically, when data indicating identification information and a password (hereinafter referred to as “login data”) is transmitted from the user device 20, the control device 11 stores authentication information data that matches the data in the registered user DB 121. If so, the user identified by the identification information is authenticated. The control device 11 transmits, to the user device 20 that has transmitted the login data, authentication notification data indicating notification of authentication and authentication information that has been authenticated. When the user device 20 stores the received authentication notification data and transmits the data to the bookmark server device 10 thereafter, the user device 20 transmits the stored authentication notification data together. The control device 11 determines that the user device 20 that has transmitted the authentication notification data is authenticated. The registered user who uses the user device 20 accesses, for example, the content (web page) held by the web server device 40 by the function of the browser 30 after the above authentication is performed.

  FIG. 8 is a diagram illustrating an example of a web page displayed on the browser 30. The browser 30 includes a menu E2 for selecting functions of the browser (in this example, “file”, “edit”, “display”, and “bookmark creation”), and a web page access destination E3 (“www.aaa”). .Aa.aa / aa / aa.html "). The browser 30 displays a web page title E1 and a web page body E4. When the user performs an operation of selecting “bookmark creation” while the web page is displayed, the user device 20 generates a bookmark of the web page as shown in FIG. The user device 20 stores the generated bookmark in its own device and transmits it to the bookmark server device 10 together with the authentication notification data.

  When the bookmark is supplied together with the authentication notification data, the control device 11 stores the bookmark in the bookmark DB 122 in association with the identification information indicated by the authentication notification data. Thereby, data as shown in FIG. 5 is stored in the bookmark DB 122. The identification information thus stored identifies the user who created the bookmark. The registered user DB 121 shown in FIG. 4 stores the attribute of the user identified by each piece of identification information in association with these pieces of identification information. That is, in the registered user DB 121 and the bookmark DB 122, the bookmark and the attribute of the user who created the bookmark are associated with each other via the identification information. In this way, the storage unit 105 stores the bookmark (content information in the present embodiment) in association with the attribute of the user who created the bookmark.

  The calculation unit 101 calculates a relationship index using an attribute that approximates the attribute of a certain user described above (hereinafter referred to as “approximation attribute”). Approximation of attributes includes not only the case where two attributes match each other but also the case where they do not match but are similar (similar) to a certain level or more. In the bookmark service providing system 1, it is determined in advance which range of attributes approximates a certain attribute. This range is hereinafter referred to as “approximate range”. In this system, for example, “neurology” and “neurosurgery” are approximated, and “neurosurgery” and “brain surgery” are not approximated. In this system, it is not essential to define the approximate range. If the approximate range is not defined, the approximate attribute is only obtained when the two attributes match each other.

Functions of the calculation unit 101 excluding the storage unit 105 are realized by the control device 11. A method by which the control device 11 calculates the relationship index will be described with reference to FIGS. 9 and 10.
FIG. 9 is a table showing an example of attributes of a certain user and the first user. In FIG. 9, “E” is a certain user, and “O”, “H”, “Ding”, and “H” are first users, and these users are associated with respective attributes. . In FIG. 9, the description of the first user other than “B”, “丙”, “Ding”, and “戊” is omitted. A and B have one attribute (clinical department) approximated (in this case), and A and B also have one attribute (clinical department) approximated (similar in this case). In addition, A and D have one attribute (academic society) approximated (in this case), and A and D have two attributes (title and age) approximated (both matched).

  FIG. 10 shows an example of a relationship index calculated for a certain user (in this example, “E”) and the first user (in this example, “O”, “丙”, “Ding”, and “戊”). It is a table. FIG. 10 shows that the users “B” and “丙” are users who respectively created bookmarks “A11” and “A12” of the content “a1”. , It is indicated that the user has created bookmarks “A21” and “A22” of the content “a2”. For example, the control device 11 assigns a value of “1” to an approximate attribute with a certain user among the attributes of the first user, and assigns a value of “0” to an attribute that is not approximate to a certain user. Then, the sum of the assigned values is calculated as the relationship index of the first user (this is called the first calculation method). FIG. 10A shows a relationship index calculated by the control device 11 using the first calculation method.

  In this case, the relationship indexes of the first users “B”, “丙”, “Ding”, and “戊” are calculated as “1”, “0”, “1”, and “2”, respectively. The calculation unit 101 uses such a relationship using an attribute of a certain user and an attribute associated with the bookmark stored in the first storage unit 105 (that is, the attribute of the first user who created the bookmark). The indicator is calculated. The calculation unit 101 calculates a relationship index for each first user by calculating a relationship index for each bookmark. The calculation unit 101 supplies relationship index data indicating the relationship index calculated in this way to the evaluation unit 102.

  Note that the calculation unit 101 may calculate the relationship index by another method. For example, the calculation unit 101 calculates a relationship index by weighting according to the type of attribute (this is referred to as a second calculation method). For example, the calculation unit 101 sets “3.0”, “2.0”, “1.5”, and “1.5” for the attribute types “medical department”, “conference”, “position”, and “age”, respectively. The relationship index is calculated with a weight of “1.5”. These weights are weights assigned to the approximate attributes. Attributes other than the approximate attribute are set to “0”, for example, as in FIG. FIG. 10B shows the relationship index calculated by the calculation unit 101 using the second calculation method. In this case, the relationship indexes of the first user “B”, “丙”, “Ding”, and “戊” are “3.0”, “3.0”, “2.0”, and “3.0”, respectively. It has been calculated.

  The calculation unit 101 may calculate the relationship index by weighting according to the degree of approximation of the attribute (this is referred to as a third calculation method). The degree of approximation is the degree to which attributes are approximated. For example, the calculation unit 101 assigns a weight of “2” when the attributes match, and calculates a relation index with a weight of “1” when the attributes are similar. FIG. 10C shows the relationship index calculated by the calculation unit 101 using the third calculation method. In this case, the relationship indexes of the first users “B”, “丙”, “Ding”, and “戊” are calculated as “2”, “1”, “2”, and “4”, respectively.

  Further, the calculation unit 101 may calculate the relationship index by weighting according to the number of approximate attributes (this is referred to as a fourth calculation method). When the number of approximate attributes is “1”, “2”, “3”, and “4”, the calculation unit 101 sets the relationship index to, for example, “1”, “2.5”, “4.5”, and The degree to which the relationship index increases with “8.0” gradually increases, and the degree to which the relationship index increases with “1”, “1.8”, “2.4”, and “2.8”. Are gradually reduced to calculate each. Further, the calculation unit 101 may calculate the above relationship index by setting values other than the approximate attribute to “0” but having values such as “0.5” and “1.0”. Good. The calculation unit 101 may calculate the relationship index by combining two or more of the first to fourth calculation methods described above. For example, the calculation unit 101 sums up the values of the attributes shown in FIG. 10A and the weights of the attributes shown in FIG. 10B, and uses them for each bookmark (that is, the first user who created each bookmark). The sum totaled for each) may be calculated as a related index. In short, since any calculation method may be used, the calculation unit 101 calculates an index (that is, a relationship index) representing the strength of the relationship between at least one attribute of the first user and an attribute of a certain user. Anything to do.

  The evaluation unit 102 is an evaluation unit that performs the above-described evaluation based on content attributes for a certain user (hereinafter referred to as “attribute standard evaluation”) according to the index (that is, the relationship index) calculated by the calculation unit 101. is there. The evaluation unit 102 is a function realized by the control device 11. Based on the relationship index indicated by the relationship index data from the calculation unit 101, the control device 11 performs attribute criteria evaluation as follows. For example, when the relationship index shown in FIG. 10A is calculated, the control device 11 sums up the relationship indexes calculated for the bookmarks of each content (that is, for the first user who created the bookmarks). The control device 11 calculates a total value “2” for the content “a1”, and calculates a total value “3” for “a2”. The control device 11 uses the total value thus calculated as a value representing the attribute reference evaluation of these contents (hereinafter referred to as “attribute reference evaluation value”). The larger the attribute criterion evaluation value, the higher the attribute criterion evaluation. The evaluation unit 102 performs the attribute reference evaluation in this way, and supplies the attribute reference evaluation value indicating the result to the generation unit 103 in association with the bookmark of each content.

  In the above example, the evaluation unit 102 has been described with respect to the case where the relationship index illustrated in FIG. 10A is calculated. However, the evaluation unit 102 is not limited to this, and the evaluation similar to the above is performed based on the relationship index calculated by the calculation unit 101. Just do it. Also, the method of attribute reference evaluation by the evaluation unit 102 is not limited to the above example (this is referred to as a first evaluation method). For example, the evaluation unit 102 may perform the attribute reference evaluation by calculating the average value of the relationship index as the attribute reference evaluation value instead of the total value of the relationship index (this is referred to as a second evaluation method). . In this case, the evaluation unit 102 calculates values “1” and “1.5” as the attribute reference evaluation values of the contents “a1” and “a2” based on the relationship index shown in FIG. 10A, for example. To do.

  In addition, the evaluation unit 102 calculates the median value of the relational indices (a value that is located in the center when the relational indices are arranged in order of magnitude. When there are an even number of relational indices, Average value) may be calculated as the attribute reference evaluation value (referred to as a third evaluation method), or the maximum value of the relationship index may be calculated as the attribute reference evaluation value (referred to as a fourth evaluation method). By using the first to fourth evaluation methods described above, the evaluation unit 102 performs the attribute attribute standard evaluation on the total value, average value, median value, or the relationship index calculated by the calculation unit 101 for the first user. The maximum value (that is, the attribute reference evaluation value) is set higher than the attribute reference evaluation of the content smaller than the content. In other words, the evaluation unit 102 determines whether the attribute criterion evaluation value calculated based on the relationship index of one or more first users who performed a predetermined action on a certain content is different from the predetermined action. When the attribute reference evaluation value is larger than the attribute reference evaluation value calculated based on the relationship index of one or more first users who have performed the above, the attribute reference evaluation of a certain content is made higher than the attribute reference evaluation of a different content.

  Note that the evaluation unit 102 may narrow down the relationship index used for calculating the attribute reference evaluation value. For example, the evaluation unit 102 calculates the attribute reference evaluation value using the number of relational indexes determined from the ones having a large value. In addition, the evaluation unit 102 may perform attribute criterion evaluation according to the time when a bookmark is created (that is, a predetermined action is performed). For example, the storage unit 105 stores the date and time when the bookmark was created in association with the bookmark. The evaluation unit 102 performs evaluation by the first to fourth evaluation methods using, for example, a relation index calculated for a bookmark created after a predetermined date and time (for example, date and time one year ago). Further, the evaluation unit 102 multiplies a relationship index calculated for a bookmark created before a predetermined date and time (for example, the date and time one year ago) by a predetermined value (for example, 0.5), and performs attribute criterion evaluation. A value may be calculated and attribute criteria evaluation may be performed. The evaluation unit 102 is not limited to using the calculated attribute standard evaluation value as an evaluation result, and ranks each content based on the calculated attribute standard evaluation value (the higher the attribute standard evaluation value, the higher the ranking). As an evaluation result).

  The calculation of the relationship index by the calculation unit 101 and the evaluation by the evaluation unit 102 are performed, for example, when a bookmark list is requested from the user device 20 in a certain authenticated state. In this case, the user device 20 transmits list request data for requesting a bookmark list and authentication notification data. The user identified by the identification information indicated by the authentication notification data transmitted in this way is a certain user described above. The calculation unit 101 and the evaluation unit 102 may perform the above calculation and evaluation in response to a request other than the request for the bookmark list. For example, the bookmark may be stored in the storage unit 105 as an opportunity. In this case, when the storage unit 105 stores the bookmark, authentication notification data transmitted from the user device 20 together with the bookmark is supplied to the control device 11. The control device 11 performs the above calculation and evaluation based on the authentication notification data. Further, the control device 11 may perform the calculation and evaluation at a predetermined time (for example, every day at 0:00) or at a predetermined time interval (for example, every day or every week). In this case, the control device 11 reads all the bookmarks stored in the bookmark DB 122 for all bookmarks or some bookmarks (for example, bookmarks that have not been subjected to attribute reference evaluation for a certain period of time), and read the bookmarks. Based on the above calculation and evaluation.

  The generation unit 103 is a generation unit that generates a list including at least one content information. Specifically, the generation unit 103 generates a list reflecting content evaluation for a certain user by the evaluation unit 102. The generation unit 103 generates, for example, a list showing content information at a higher rank than content information whose evaluation by the evaluation unit 102 is lower than the content information. In the present embodiment, the list is content information, that is, a bookmark list including bookmarks in the present embodiment. The generation unit 103 is a function realized by the control device 11, the storage device 12, and the communication device 13 in cooperation. The control device 11 reads the bookmark stored in the bookmark DB 122 stored in the evaluation unit 102. The control device 11 sets the bookmarks in the order of the content information indicated by the result data supplied from the evaluation unit 102, that is, from the attribute reference evaluation value associated with the bookmark having the larger rank (the rank is associated). Generate a bookmark list arranged in order. The generation unit 103 transmits the generated bookmark list to the user device 20, and the user device 20 displays the received bookmark list on the display device 24 illustrated in FIG.

  FIG. 11 is a diagram illustrating an example of the bookmark list displayed on the display device 24. In FIG. 11, a bookmark list including eight bookmarks A1 to A8 is displayed. In the bookmark list C1 shown in FIG. 11A, bookmarks A1 to A8 are arranged in this order. The bookmark list C1 does not reflect the result of evaluation by the evaluation unit 102. In the bookmark list C2 shown in FIG. 11B, the bookmarks are arranged from the top in the order of bookmarks A5, A2, A4, A1, A3, A6, A7, and A8. The generation unit 103 supplies list data indicating the bookmark list generated in this way to the output unit 104.

  Note that the list generated by the generation unit 103 is not limited to the bookmark list as shown in FIG. The generation unit 103 may generate, for example, a list in which the content information is indicated by characters that are larger or thicker than the content information that the evaluation by the evaluation unit 102 is lower than the content information. The generation unit 103 may generate a list in which the attribute reference evaluation value calculated by the evaluation unit 102 is added to the content information. In short, the generation unit 103 only needs to generate a list reflecting content evaluation for a certain user by the evaluation unit 102.

  The output unit 104 is an output unit that outputs the list generated by the generation unit 103 (a bookmark list in the present embodiment). The output unit 104 is a function realized by the control device 11, the storage device 12, and the communication device 13 in cooperation. For example, the control device 11 outputs the list data supplied from the generation unit 103 to the user device 20 that has transmitted the list request data described above. When the user device 20 displays the bookmark list indicated by the output list data, evaluation information is provided to the user.

  The output destination of the list data is not limited to the user device 20. For example, the registered user's mail address may be stored in association with the identification information of the registered user DB, and the output unit 104 may output list data for the mail address. For example, when the bookmark server device 10 includes a display device, the output unit 104 may output list data to the display device. Further, the output unit 104 may output the list data to the storage device 12 and store the list data, and may cause the user to copy the list data via a network, or may copy the list data to a memory key or the like offline.

[Operation]
Based on the above configuration, the bookmark service providing system 1 performs the above-described providing process (a process for providing evaluation information and a bookmark sharing service to registered users). This providing process is divided into an accumulation process for accumulating content information (bookmark in this embodiment) and attributes, and an evaluation process for evaluating content.
FIG. 12 is a sequence diagram illustrating an example of the operation of each device in the accumulation process. The accumulation process is started when a user who possesses the user device 20 inputs authentication information from the browser 30 or the like. When this input is performed, the user device 20 receives the input of the authentication information, and transmits login data indicating the received authentication information to the bookmark server device 10 (step S11).

  When receiving the login data, the bookmark server device 10 authenticates the registered user based on the authentication information indicated by the login data (step S12). When the registered user is authenticated, the bookmark server device 10 transmits the above-described authentication notification data to the user device 20 (step S13). After receiving the authentication notification data, the user device 20 acquires content (web page) from the web server device 40, for example, by a user operation (step S14). Here, when the user performs a bookmark creation operation (operation for selecting “bookmark creation” in the menu E2 shown in FIG. 9), the user device 20 accepts the bookmark creation operation (step S15), and accepts this operation. The bookmark of the content currently displayed is generated (step S16). And the user apparatus 20 memorize | stores the produced | generated bookmark (step S17), and also transmits to a bookmark server apparatus 10 with authentication notification data (step S18). The bookmark server device 10 stores the received bookmark in association with the identification information indicated by the authentication notification data received together (step S19). Step S19 is an operation performed by the storage unit 105 of the calculation unit 101. The above is the operation of each device in the accumulation process.

  FIG. 13 is a sequence diagram illustrating an example of the operation of each device in the evaluation process. The evaluation process is started, for example, when the user who owns the user device 20 performs an operation (referred to as “list request operation”) for requesting a bookmark list after authenticating a registered user. The Upon receiving this operation (step S31), the user device 20 transmits list request data for requesting a bookmark list to the bookmark server device 10 together with authentication notification data (step S32). Upon receiving the list request data, the bookmark server device 10 designates the user identified by the identification information indicated by the received authentication notification data as a certain user, and designates the user who created the bookmark stored in the bookmark DB 122 as the first user. As a result, the above-described relationship index is calculated (step S33). Step S33 is an operation performed by the calculation unit 101.

  When the bookmark index is calculated in step S33, the bookmark server apparatus 10 evaluates the content based on the calculated relation index (step S34). Step S34 is an operation performed by the evaluation unit 102. Next, the bookmark server device 10 generates a bookmark list reflecting the evaluation performed in step S34 (step S35). Step S35 is an operation performed by the generation unit 103. For example, the bookmark server device 10 generates a bookmark list as shown in FIG. 11B, and outputs the generated bookmark list to the user device 20 (step S36). Step S <b> 36 is an operation performed by the output unit 104. The user device 20 displays the bookmark list received in step S36 (step S37). The above is the operation of each device in the evaluation process.

  When content such as a web page provided by the web server device 40 is evaluated, the number of pieces of content information (for example, bookmarks) indicating that the user has performed a predetermined action (for example, bookmark creation operation) on the content. There is a method of evaluating a large number of items higher than a small number. However, even if the content information has the same number of content information, one is a content that only a user who specializes in the same field as himself performs a predetermined action, and the other is a user whose field of specialization has nothing to do with himself In the case where only the content that has undergone the predetermined action is the content, the former content can often be evaluated highly for me.

  In the example shown in FIG. 9 and FIG. 10, the user A is a medical worker who specializes in nerves belonging to the neurology department. Content that has undergone a predetermined action by a specialized neurosurgery medical worker can often be evaluated more highly than content that has undergone a predetermined action by a medical worker who does not specialize in nerves. The same can be said about the academic society, position, and age to which they belong. In the above-described method of evaluating only by the number of content information, regardless of the user who has performed a predetermined action on the content indicated by the content information, the content is evaluated only by that number. In the present embodiment, an attribute reference evaluation is performed by the evaluation unit 102 according to the relationship index calculated by the calculation unit 101, so that a user having a strong attribute relationship (that is, a relationship index is large) The content on which the action has been performed is more highly evaluated than the content on which the user who has not performed the predetermined action has performed. According to the present embodiment, the content can be evaluated by reflecting what kind of user the user (that is, the first user) who has performed the predetermined action on the content is. .

  Further, in the present embodiment, as in the example shown in FIG. 10, the attribute reference evaluation can be performed by assigning weights to the attribute type and the attribute approximation degree. As a result, even if the number of approximate attributes of a certain user and the first user is the same, for example, the relationship index calculated is different if the type and the degree of approximation of the approximate attributes are different. The evaluation value will also be different. As described above, according to the present embodiment, when the evaluation is performed by reflecting what kind of user the first user is, the degree of reflection is changed according to the type of approximate attribute, the degree of approximation, and the like. be able to.

[Second Embodiment]
Hereinafter, the second embodiment of the present invention will be described focusing on differences from the first embodiment. In the first embodiment, content evaluation (attribute standard evaluation) based on the user's attribute is performed. In the second embodiment, the content trend (hereinafter, “content trend”) that the user has targeted for a predetermined action is used. Content evaluation (hereinafter referred to as “trend criteria evaluation”).
FIG. 14 is a diagram showing a functional configuration realized by the bookmark server device 10a of the present embodiment. The bookmark server device 10a includes a calculation unit 101a, an evaluation unit 102a, a generation unit 103, and an output unit 104. The calculation unit 101a has a tendency between the content that is the target of the predetermined action of the first user, that is, the user who has performed the predetermined action on the content, and the content that is the target of the predetermined action of a certain user. It is a calculation means for calculating the degree of approximation (hereinafter referred to as “trend approximation degree”). The calculation unit 101a includes, for example, a storage unit 105a, and performs this calculation based on information stored in the storage unit 105a.

  The accumulating unit 105a is an accumulating unit that accumulates the above-described content information (information indicating content on which a predetermined action has been performed by the user) and identification information for identifying the user in association with each other. That is, unlike the storage unit 105 illustrated in FIG. 7, the storage unit 105a does not store attributes. Therefore, in the present embodiment, the registered user DB 121 shown in FIG. 3 does not have to store the attributes as shown in FIG. When the bookmark and authentication notification data are transmitted from the user device 20, the storage unit 105a stores the bookmark in the bookmark DB 122 in association with the identification information indicated by the authentication notification data. In the accumulation process described with reference to FIG. 12, the accumulation unit 105a accumulates bookmarks and identification information instead of accumulating bookmarks and attributes in step S19.

  The calculation unit 101a calculates the tendency approximation degree using content that is similar to each other (hereinafter, referred to as “approximate content”) among the content that the certain user and the first user have respectively targeted for the predetermined action. The case where the contents are close to each other includes not only the case where the two contents match each other but also the case where they do not match but are more similar (similar) than a certain level. In the bookmark service providing system 1, for example, when the host name and the path name of the access destinations (URLs) of both contents match, the approximate range is determined so that the contents are similar to each other. As a specific example, the calculation unit 101a may determine whether the access destination contents are “http://aaa.com/sports/bbb.html” and “http://aaa.com/medical/drug/ccc.html”. Then, since the host name “aaa.com” and the path name “/ medical / drug /” match, it is determined that the content is similar (ie, approximate). In this system, it is not essential to define an approximate range. When the approximate range is not defined, only the case where two access destinations match each other is approximate content.

A method by which the calculation unit 101a calculates the tendency approximation degree will be described with reference to FIG.
FIG. 15 is a diagram for explaining a method of calculating the tendency approximation degree. FIG. 15A shows an example of identification information “ID011” to “ID014” accumulated by the accumulation unit 105a, bookmarks associated with the identification information, and contents indicated by each bookmark. ing. In this example, “ID011” is associated with a bookmark A31 having a content “a3” and a bookmark A32 having a content “a4”. Similarly, the bookmark A41 of the content “a5” is associated with “ID012”, and the bookmarks A51, A52, and A53 of “a3”, “a4 ′”, and “a6” are associated with “ID013”. Each is associated. Note that “a4 ′” indicates content that approximates “a4”. Further, bookmarks A61 and A62 of “a3” and “a6” are associated with “ID014”, respectively.

For example, a case will be described in which the calculation unit 101a calculates a tendency approximation degree with a user identified by “ID011” as a certain user. In this case, the calculation unit 101a identifies A31 and A32 as bookmarks stored in the storage unit 105a in association with “ID011”. The calculation unit 101a assigns a value of “1” to a bookmark that indicates content that approximates the content that the specified bookmark indicates an access destination, and assigns a value of “0” to a bookmark that indicates content that does not approximate. The calculation unit 101a calculates a value obtained by totaling the assigned values for each piece of identification information as a tendency approximation (this is referred to as a first calculation method in the present embodiment).
FIG. 15B shows the tendency approximation degree calculated by the first calculation method in the present embodiment. In this case, the tendency approximation degrees of the first user identified by the identification information “ID012,” “ID013,” and “ID014” are calculated as “0”, “2”, and “1”, respectively. The calculation unit 101a supplies trend approximation degree data indicating the tendency approximation degree calculated in this way to the evaluation unit 102a.

  The first calculation method in the present embodiment described above corresponds to the first calculation method described in the first embodiment. In this embodiment, by replacing the approximate attribute with the approximate content, the tendency approximation degree is calculated instead of the relationship index. Similarly, the calculation unit 101a is not limited to the above calculation method, and may calculate the degree of trend approximation by the second to fifth calculation methods described in the first embodiment or a combination method. . In this case, the calculation unit 101a assigns a weight according to the “content type” instead of the “attribute type”, or a weight according to the “content approximation degree” instead of the “attribute approximation degree”. Or attach. For example, in the bookmark service providing system, when a specific host name (a path name may be added) is determined in advance and included in the access destination, the calculation unit 101a weights the content of the access destination.

  In the present system, a plurality of combinations of specific host names (path names may be added) are determined, and these combinations are divided into a plurality of groups (for example, groups A and B). For each group, a corresponding degree of approximation (for example, group A has a degree of approximation “1” and group B has a degree of approximation “0.5”) is determined. In this case, if the host name of the combination is included in the access destination indicated by the bookmark created by a certain user and the first user, the bookmark created by the first user is calculated as the content indicating the access destination. The unit 101a adds weight. At this time, the calculation unit 101a assigns a weight according to the degree of approximation determined for the group to which the combination belongs. For example, the calculation unit 101a assigns a weight of “2.0” when the access destinations match, and assigns a weight of “1.0” when the host name of the combination of group A is included, to the combination of group B If the host name is included, a weight of “0.5” is assigned.

  Note that the calculation unit 101a calculates a trend approximation by dividing the sum of the values and weights assigned as described above in each piece of identification information by the number of bookmarks stored in the storage unit 105a in association with each piece of identification information. It may be calculated as a degree. In the example of FIG. 15B, the calculation unit 101a calculates a trend approximation of 2/3 for “ID013” and calculates a trend approximation of 1/2 for “ID014”. As a result, it is possible to avoid the tendency approximation tendency of a user having a large number of bookmarks from becoming large.

  The evaluation unit 102a calculates the degree (hereinafter referred to as “trend criterion evaluation”) based on the above-described content tendency of content for a certain user (the tendency of content for performing a predetermined action) (hereinafter referred to as “trend criterion evaluation”). That is, it is an evaluation means performed according to the tendency approximation degree). Specifically, the evaluation unit 102a calculates a trend reference evaluation value in the same manner as in the first to fourth evaluation methods described above, instead of using the trend approximation as the relationship index in the first embodiment, and calculates the calculated trend reference evaluation. A trend-based evaluation is performed based on the value. The total value, average value, median value, or maximum value of the tendency approximation degree is a value indicating the height of the tendency reference evaluation of each content, and is hereinafter referred to as “trend reference evaluation value”. The larger the trend criterion evaluation value, the higher the trend criterion evaluation.

  For example, in the example illustrated in FIG. 15, when the evaluation unit 102 a evaluates the content “a4 ′”, “a5”, and “a6” excluding the bookmark content created by a certain user, the evaluation unit 102 a creates a bookmark for each content. The total value of the trend approximations calculated for the first user is calculated as the trend reference evaluation value. In this example, the evaluation unit 102a calculates the tendency reference evaluation values of “a4 ′”, “a5”, and “a6” as “2”, “0”, and “3”, respectively. When the average value is set as the trend reference evaluation value, the evaluation unit 102a sets the trend reference evaluation values of “a4 ′”, “a5”, and “a6” to “2”, “0”, and “1.5”, respectively. Is calculated.

  The evaluation unit 102a performs a trend standard evaluation of the content, and the total value, average value, median value, or maximum value (that is, the trend reference evaluation value) of the trend approximation degree calculated by the calculation unit 101a for the first user is smaller than that content. Higher than the content trend criteria evaluation. In other words, the evaluation unit 102a has a tendency criterion evaluation value calculated based on the tendency approximation degree of one or more first users who have performed a predetermined action on a certain content for a content different from that. When the tendency reference evaluation value is larger than the tendency reference evaluation value calculated based on the tendency approximation degree of one or more first users who have performed an action, the tendency reference evaluation of a certain content is higher than the tendency reference evaluation of a different content. To do. The evaluation unit 102a performs the trend criterion evaluation in this way, and supplies the trend criterion evaluation value indicating the result to the generation unit 103 in association with the bookmark of each content.

  Note that, similarly to the evaluation unit 102 of the first embodiment, the evaluation unit 102a may narrow down the relationship index used for calculating the trend reference evaluation value, or a bookmark has been created (that is, a predetermined action has been performed). You may perform tendency standard evaluation according to time. In the above example, the evaluation unit 102a performs the trend criterion evaluation excluding the bookmark content created by a certain user. However, the evaluation unit 102a may perform the trend criterion evaluation including it. In this case, the evaluation unit 102a performs the trend reference evaluation as a certain first user (the first user having the maximum tendency approximation).

  In the present embodiment, the tendency reference evaluation by the evaluation unit 102a is performed according to the tendency approximation degree calculated by the calculation unit 101a, so that the tendency of content to perform a predetermined action with a certain user (that is, the content tendency) is approximated. Content that has been subjected to a predetermined action by a user who has a high degree (that is, tendency approximation degree) is likely to be highly evaluated as compared to content that has been subjected to a predetermined action by a user who is not. According to this embodiment, as in the first embodiment, it is possible to evaluate the content reflecting what kind of user the user who has performed the predetermined action on the content is. it can. Also in this embodiment, since the trend criterion evaluation is performed by weighting the content type and the degree of content approximation, what kind of user the first user is, as in the first embodiment. When the evaluation is performed with reflection, the degree of reflection can be changed.

[Third Embodiment]
Hereinafter, the third embodiment of the present invention will be described focusing on differences from the first and second embodiments. In the third embodiment, character strings are added to the content information, and the tendency approximation degree calculated in the second embodiment is calculated based on those character strings. Hereinafter, this character string is referred to as “given character string”.
FIG. 16 is a diagram illustrating a functional configuration realized by the bookmark server device 10b according to the present embodiment. The bookmark server device 10b includes a calculation unit 101b, an evaluation unit 102b, a generation unit 103, and an output unit 104. The calculation unit 101b uses, as a first user, a user who is a target of a predetermined action for content to which an approximate character string that approximates a character string that has been assigned to the content that has been the target of a certain user's predetermined action. Calculate the trend approximation. The calculation unit 101b includes, for example, a storage unit 105b, and performs this calculation based on information stored in the storage unit 105b.

  The accumulating unit 105b accumulates content information (information indicating content on which a predetermined action has been performed by the user), identification information for identifying the user, and a character string assigned to the content information in association with each other. It is the storage means to do. The accumulation unit 105b accumulates content information and identification information by the same method as in the second embodiment. In addition to this, a method in which the storage unit 105b stores the added character string will be described with reference to FIG.

  FIG. 17 is a table for explaining accumulation of a given character string by the accumulation unit 105b. In FIG. 17, data stored in the bookmark DB 122 is shown. In this example, “identification information” and “bookmark” similar to those in FIG. 15 are shown. Each of the bookmarks A31 to A62 stored in the bookmark DB 122 is a bookmark as shown in FIG. 2, and each includes a part of content indicating an access destination. For example, the storage unit 105b stores in the storage device 12 added character string data in which a plurality of added character strings (for example, technical terms related to medical care) are stored. When accumulating the bookmark as shown in FIG. 17, the accumulating unit 105 b determines whether or not a part of the content included in the bookmark includes the added character string stored in the given character string data. Judge using matching technology. When determining that the assigned character string is included, the accumulating unit 105b stores the added character string in the bookmark DB 122 in association with the bookmark including the content determined to include the added character string. For example, the storage unit 105b determines that the bookmark A31 includes the appended character strings “AAA”, “BBB”, and “CCC”, and associates these appended character strings with the bookmark A31. Store.

Similarly, the storage unit 105b stores each assigned character string shown in FIG. 17 in association with each bookmark. In this way, the accumulation unit 105b accumulates the character strings assigned to the content information in association with the content information and the identification information.
Note that the storage unit 105b may store the assigned character string by another method. For example, when a bookmark is selected in the user device 20 and an operation for adding a character string to the selected bookmark is performed by the user, the grant data indicating the selected bookmark and the character string to be given is the bookmark. It is transmitted to the server device 10. The accumulating unit 105b receives the grant data, and stores the grant character string indicated by the same grant data in association with the bookmark indicated by the grant data. In addition, the storage unit 105b transmits to the user device 20 the assigned character string that is determined to be included in the bookmark as described above and the assigned character string data indicating the bookmark, and the user device of the given character string The item selected by the user's operation in 20 may be stored in association with the bookmark.

  The calculation unit 101b calculates a tendency approximation using a given character string (hereinafter referred to as “approximate character string”) that approximates a given character string of content that is a target of a predetermined action of a certain user. Approximation of character strings includes not only a case where two character strings match each other but also a case where they do not match but are more than a certain level (similarity or one includes the other). In the bookmark service providing system, a condition for determining whether or not two character strings are approximated (referred to as “approximate condition”) is predetermined. The approximate condition is, for example, a condition that is satisfied when two character strings are in common with each other by a predetermined number of characters or more or a predetermined number of characters. In addition, the case of uppercase and lowercase letters of the same alphabet may be approximated, or the case of kanji and its readings may be approximated. In this system, it is not indispensable to define such an approximate condition. When the approximate condition is not defined, the approximate character string is obtained only when the two attributes match each other.

  A method by which the calculation unit 101b calculates the tendency approximation will be described with reference to an example illustrated in FIG. For example, a case will be described in which the calculation unit 101b calculates the tendency approximation degree with the user identified by “ID011” as a certain user. In this case, the calculation unit 101b includes “AAA”, “BBB”, “CCC”, and the added character strings stored in association with “ID011” among the added character strings stored in the storage unit 105b. An approximate character string of “DDD” and “EEE” is specified. In the example of FIG. 17, the calculation unit 101b identifies “BBBB” associated with “ID013” and “aaa”, “CCC”, and “EEE” associated with “ID014”. “Aaa” is a given character string similar to “AAA”, and “BBBB” is a given character string including “BBB”.

The calculation unit 101b assigns a value of “1” to the identified approximate character string, and assigns a value of “0” to the assigned character string other than the approximate character string. The calculation unit 101b calculates a value obtained by totaling the assigned values for each piece of identification information as a tendency approximation degree (this is referred to as a first calculation method in the present embodiment).
FIG. 18 is a diagram illustrating an example of the tendency approximation degree calculated by the calculation unit 101b. In this case, the tendency approximation degrees of the first user identified by the identification information “ID012,” “ID013,” and “ID014” are calculated as “0”, “2”, and “3”, respectively. The calculation unit 101b supplies trend approximation degree data indicating the tendency approximation degree calculated in this way to the evaluation unit 102b.

  The first calculation method in the present embodiment described above corresponds to the first calculation method described in the first embodiment. In this embodiment, by replacing the approximate attribute with the approximate character string, the tendency approximation degree is calculated instead of the relationship index. Similarly, the calculation unit 101b may calculate the degree of trend approximation not only by the above calculation method but also by the second to fifth calculation methods described in the first embodiment or a calculation method that combines them. . In this case, the calculation unit 101b assigns a weight according to the “approximate character string type” instead of the “attribute type”, or “approximate character string approximation degree” instead of the “attribute approximation degree”. Or give weights according to

  For example, in a bookmark service providing system, a specific character string (for example, a specific character string such as a medical term) is determined in advance, and the calculation is performed when the approximate character string is the specific character string or includes the specific character string. The part 101b weights the approximate character string. For example, the calculation unit 101b assigns a weight of “3” when the given character strings match each other, and “2” when the given character strings are similar and have the same number of characters (for example, uppercase and lowercase letters). If the assigned character strings are similar and the number of characters is different, a weight of “1” may be assigned.

Note that the calculation unit 101b stores the sum of the values and weights assigned as described above in the identification information in the storage unit 105b in association with the identification information, as in the calculation unit 101a of the second embodiment. A value divided by the number of bookmarks that are present may be calculated as the tendency approximation. In the example of FIG. 18, the calculation unit 101b calculates a tendency approximation of 2/3 for “ID013” and calculates a trend approximation of 3/2 for “ID014”. Thereby, also in this embodiment, it can avoid that the tendency approximation degree of a user with many bookmark numbers becomes large easily.
The evaluation unit 102b performs the trend reference evaluation in the same manner as the evaluation unit 102a based on the tendency approximation degree calculated by the calculation unit 101b.

  The tendency approximation degree calculated by the calculation unit 101b represents the degree to which the tendency of the character string assigned to the content approximates a certain user. The tendency of the character string given to this content represents the content tendency in this embodiment. In the present embodiment, the trend reference evaluation by the evaluation unit 102b is performed according to such a tendency approximation degree, so that a predetermined action is performed by the first user whose degree of content tendency is close to a certain user. The content is likely to be highly evaluated as compared to content in which a predetermined action is performed by a user who is not. That is, according to the present embodiment, as in the first and second embodiments, the content is evaluated by reflecting what kind of user is the user who has performed the predetermined action on the content. be able to. Also in this embodiment, since the trend criterion evaluation is performed by weighting the type of approximate character string and the approximation degree of the approximate character string, as in the above embodiments, the first user is any user. When the evaluation is performed reflecting whether or not there is, the degree of the reflection can be changed.

  Note that the evaluation unit 102b may calculate the number of pieces of content information including the approximate character string as the tendency approximation degree. In this case, in the example of FIG. 17, the evaluation unit 102 b has the bookmarks (A 51, A 52, and A 53) that are stored in association with “ID 013” to which an approximate character string is attached (A 51 and The number of A53) (2 in this case) is calculated as the tendency approximation. In addition, the evaluation unit 102b calculates the number of bookmarks (A62) to which an approximate character string is assigned (in this case 1) among the bookmarks stored in association with “ID014” as the tendency approximation degree. Note that the evaluation unit 102b may calculate the ratio of the number of bookmarks to which approximate character strings are assigned to the total number of bookmarks accumulated as the tendency approximation degree. Also in this case, the evaluation unit 102b may calculate the tendency approximation degree by reflecting the weight for the approximate character string described above.

  For example, when the evaluation unit 102b calculates the number of pieces of content information including the approximate character string as the tendency approximation degree, in the example of FIG. 17 described above, the content (approximate character) indicated by the content information stored in association with “ID013” is shown. The tendency criterion evaluation of the number of columns (2) is higher than the tendency criterion evaluation of the contents (the number of approximate character strings is three) indicated by the content information stored in association with “ID014”. As a result, when the approximate character strings are distributed evenly in any content information, the tendency criterion evaluation can be made higher than in the case where the approximate character strings are provided in a concentrated manner. For example, when the evaluation unit 102b calculates the number of approximate character strings as the tendency approximation degree, on the contrary, when approximate character strings are concentrated and given to certain content information, they are scattered. It is possible to make the trend criterion evaluation higher than These methods may be used properly depending on the target for which the trend criterion evaluation is performed, or when the result of the trend criterion evaluation tends to be high or low.

[Modification]
Each embodiment mentioned above is only an example of implementation of the present invention, and may be changed as follows. Moreover, you may implement combining each embodiment mentioned above and each modification shown below as needed.

(Modification 1)
In the above-described embodiments, the content information is information related to content for which a predetermined action called bookmark creation operation has been performed by the user, but is not limited thereto. The content information may be, for example, information related to content that has been browsed by a user and that has been browsed for a certain period of time. In this case, the server device accumulates a history of access to content (referred to as “access history”). In this modification, the server device does not need to accumulate bookmarks.
FIG. 19 is a diagram illustrating a hardware configuration of the server device 10c according to the present modification. Similarly to the bookmark server device 10, the server device 10 c includes a control device 11, a storage device 12, and a communication device 13. The storage device 12 stores an access history DB 123 for storing an access history in the storage device 12 instead of the bookmark DB 122 shown in FIG. Below, the case where the server apparatus 10c performs attribute reference | standard evaluation similarly to 1st Embodiment is demonstrated.

  FIG. 20 is a table showing an example of data stored in the access history DB 123. In this table, “access destination” and “identification information” are associated with each other. “Access destination” is the URL of the content accessed by the user. The user device 20 stores, for example, the start time at which the content data of the content accessed by the user is acquired and the display is started by the function of the browser 30, and measures the time elapsed from the start time when the display is ended. And record the measured time in the authentication notification data. At this time, the user device 20 also records the access destination of the content in the authentication notification data, and transmits the authentication notification data to the server device 10c.

  When the connection time indicated by the received authentication notification data is equal to or longer than a predetermined time (for example, 1 minute or 5 minutes), the storage unit according to the present modification records in the authentication notification data that a predetermined action has been performed. The access destination thus obtained is stored as content information in the access history DB 123 in association with the identification information indicated by the authentication notification data. In this way, the storage unit stores content information in association with attributes, as in the first embodiment.

  The accumulation unit may perform this accumulation by other methods. For example, instead of recording the time elapsed from the start time in the authentication notification data, when the time elapsed since the user device 20 acquired the content data and started displaying becomes a predetermined time or more, Is recorded in the authentication notification data in association with the access to the content indicated by the content data. When the user device 20 performs the recording, the user device 20 transmits the authentication notification data on which the recording has been performed to the server device 10c. When the storage unit receives the authentication notification data, the storage unit determines whether or not the fact that a predetermined action has been performed is recorded in the received authentication notification data. Are stored in the access history DB 123 as content information. Further, the storage unit may store the identification information based on the authentication notification data as described above even when storing the identification information in the same manner as in the second and third embodiments instead of the attribute.

  FIG. 21 is a sequence diagram showing an example of the operation of each device in the accumulation process of the present modification. In this example, operations up to step S14 (content acquisition by the user device 20) shown in FIG. 15 are performed. Next, the user device 20 records the acquired content in the authentication notification data together with the access destination of the content as a connection time from the display to the end of the display (step S41). The user device 20 transmits authentication notification data recording the connection time and access destination to the server device 10c (step S42). The server device 10c accumulates content information based on the received authentication notification data (step S43). Step S43 is an operation performed by the storage unit. In step S43, the accumulation unit accumulates attributes as in the first embodiment, accumulates identification information as in the second embodiment, or accumulates identification information and a given character string as in the third embodiment. .

  The server apparatus 10c performs an evaluation process in the same manner as in each of the above embodiments, and instead of generating a bookmark list, for example, acquires a part of the evaluated content, includes a part of the content, and the evaluation result Is generated, and the generated content list is provided to the user device 20 as evaluation information. As described above, in this modification, content can be evaluated based on content information using an access history.

  In the bookmark service providing system, in addition to the above-described actions, the bookmark service providing system can be added to a list to be read later, shared by e-mail, shared via SNS (Social Networking Service), or saved. It may be determined to do. These actions are performed when the user operates the operation device 23 to execute a function such as a browser plug-in program or a CGI (Common Gateway Interface) provided by a web page. When these actions are performed, for example, the user device 20 associates the fact that the actions have been performed with the access destination of the content that was accessed when the actions were performed. To record. As a result, the storage unit stores the content information in association with the attribute as in the case of storing using the other method described above.

(Modification 2)
The bookmark server device is not limited to performing the attribute criterion evaluation described in the first embodiment and the trend criterion evaluation described in the second and third embodiments, but two or three of these are combined. You may do it. In that case, the accumulating unit accumulates information necessary for each criterion evaluation, and the calculating unit calculates the relationship index and the trend approximation degree based on the information (attributes and identification information) accumulated by the accumulating unit. Calculations are performed sequentially or in parallel. Further, the evaluation unit may evaluate the content by treating all the standard evaluations equally based on the calculated relational index and tendency approximation degree, or evaluate the content with different weights for each standard evaluation. May be.

(Modification 3)
In the first embodiment described above, the storage unit 105 illustrated in FIG. 7 acquires the user attribute based on the attribute stored in the registered user DB 121 stored in the bookmark server device 10, but is not limited thereto. For example, the user device 20 stores a user attribute in association with the user identification information. Then, when transmitting the authentication notification data to the bookmark server device 10, the user device 20 transmits attribute data indicating the attribute stored in association with the identification information indicated by the authentication notification data together. Good. Further, the storage unit 105 may request the user device 20 for attributes, and the authentication notification data and attribute data may be transmitted in response thereto.

(Modification 4)
In each embodiment described above, the calculation unit calculates the relationship index and the tendency approximation degree based on information stored in the storage unit. However, the calculation unit may perform this calculation based on other information. For example, the calculation unit stores content information and authentication notification data transmitted from the user device 20, and based on the identification information indicated by the stored content information and authentication notification data when performing the above calculation. 9, 10, 15, and 17 are acquired to calculate the relationship index and the trend approximation. Further, the calculation unit may acquire these pieces of information stored in the external device, and calculate the relationship index and the trend approximation degree. In this way, the calculation unit may accumulate information (content information, attributes, identification information, etc.) for calculating the relationship index and the tendency approximation degree, or other information stored in the own device. Alternatively, it may be acquired from an external device or the like.

(Modification 5)
In each embodiment described above, the generation unit 103 generates a list of content information such as a bookmark list shown in FIG. 11B, but is not limited thereto. For example, the generation unit 103 converts a character string (such as “high”, “medium”, and “low”, “A”, “B”, and “C”) representing a predetermined stage from the evaluation result to each content information. A list of contents information may be generated. The generation unit 103 may generate a list using numerical values, figures, symbols, and the like instead of these character strings. In any case, the generation unit 103 may generate any content information list as long as the user can grasp the evaluation result.

(Modification 6)
In addition to a server device such as the bookmark server device 10 and an information processing system such as the bookmark service providing system 1, the present invention can be understood as an information processing method for realizing processing performed by the bookmark server device 10. Is. The processing here is, for example, provision processing (specifically, accumulation processing and evaluation processing) shown in FIGS. 12, 13, and 21. The present invention can also be understood as a program for causing a computer such as the bookmark server device 10 to function as each means shown in FIGS. These programs are provided in the form of a recording medium such as an optical disk in which these programs are stored, or downloaded in a computer via a network such as the Internet, and installed and made available for use. It may be a thing to do.

DESCRIPTION OF SYMBOLS 1 ... Bookmark service provision system, 10 ... Bookmark server apparatus, 10c ... Server apparatus, 20 ... User apparatus, 30 ... Browser, 40 ... Web server apparatus, 11, 21 ... Control apparatus, 12, 22 ... Storage apparatus, 13, 26 ... Communication device, 23 ... Operating device, 24 ... Display device, 25 ... Voice input / output device, 101, 101a, 101b ... Calculation unit, 102, 102a, 102b ... Evaluation unit, 103 ... Generation unit, 104 ... Output unit, 105 105a, 105b ... accumulating unit, 121 ... registered user DB, 122 ... bookmark DB, 123 ... access history DB

Claims (8)

A server device that evaluates content on the Internet based on a predetermined action by a user,
Calculating means for calculating an index representing the strength of a relationship between at least one attribute of a first user who has performed the action on the content and an attribute of a certain user;
An evaluation unit that performs evaluation based on the attribute of the content for the certain user according to the index calculated by the calculation unit. The evaluation unit assigns an evaluation based on the attribute of the content to the attribute of the content whose total value, average value, median value, or maximum value of the index calculated by the calculation unit for the first user is smaller than the content. The server device according to claim 1, wherein the server device is higher than an evaluation based on the server device. A server device that evaluates content provided with a character string on the Internet based on a predetermined action by a user,
A degree of approximation of a tendency between at least one content that is the target of the action of the first user who has performed the action on the content and the content that is the target of the action of a certain user is calculated. Calculating means for performing the action on a content to which an approximate character string that approximates the character string assigned to the content targeted by the action of the certain user is the first user. And weighting the approximate character string according to the type of the approximate character string, the number of the approximate character strings, or the degree of approximation of the approximate character string, and the first user sets the target of the action Calculating means for calculating the degree based on the weight of the approximate character string assigned to the content ,
An evaluation unit that performs evaluation based on the tendency of the content for the certain user according to the degree calculated by the calculation unit. The evaluation means evaluates the content based on the tendency to the tendency of the content whose total value, average value, median value, or maximum value calculated by the calculation means for the first user is smaller than the content. The server device according to claim 3, wherein the server device is higher than an evaluation based on the server device. Generating means for generating a list that includes at least one content information related to the content, and that reflects the evaluation of the content for the certain user by the evaluation means;
The server apparatus according to any one of claims 1 to 4, characterized in that an output means for outputting a list generated by the generation unit. The action describes an access destination data indicating an access destination for accessing the content, to any one of claims 1 to 5, characterized in that an operation of creating a content information related to the contents Server device. Based on a predetermined action by the user, at least one of the first users who has performed the action on the content by the control device that controls the server device that evaluates the content on the Internet according to the program stored in the storage device A calculation step for calculating an index representing the strength of the relationship between the above attribute and the attribute of a certain user;
The control device includes an evaluation step of performing an evaluation based on the attribute of the content for the certain user according to the index calculated in the calculation step according to a program stored in the storage device. An information processing method characterized by the above. A control device that controls a server device that evaluates content to which a character string on the Internet is assigned based on a predetermined action by a user performs the action on the content according to a program stored in a storage device . A calculation step of calculating a degree of approximation of a tendency between at least one content that is the target of the action of one user and the content that is the target of the action of a certain user, the certain user The user who is the target of the action with the content given the approximate character string approximated to the character string given to the content that is the target of the action is the first user, and for the approximate character string , The type of the approximate character string, the number of the approximate character strings, or the overlap according to the degree of approximation of the approximate character string A calculation step of calculating the degree based on the assigned weights of the approximate character string first user is assigned to a content that is a target for the action,
An evaluation step in which the control device performs an evaluation based on the tendency of the content for the certain user according to the degree calculated in the calculation step according to a program stored in the storage device. An information processing method characterized by the above.

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