JP4625365B2 - Recommendation rank selection device and recommendation rank selection program - Google Patents

Recommendation rank selection device and recommendation rank selection program Download PDF

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JP4625365B2
JP4625365B2 JP2005134272A JP2005134272A JP4625365B2 JP 4625365 B2 JP4625365 B2 JP 4625365B2 JP 2005134272 A JP2005134272 A JP 2005134272A JP 2005134272 A JP2005134272 A JP 2005134272A JP 4625365 B2 JP4625365 B2 JP 4625365B2
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friend
recommendation
importance
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JP2006309660A (en
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幹大 上野
武史 木村
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日本放送協会
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  The present invention relates to a recommendation rank selection apparatus and a recommendation rank selection program, and more particularly to a recommendation rank selection apparatus and a recommendation rank selection program for selecting a recommendation rank of content with high accuracy based on a relationship between a user and a partner.

  Conventionally, as a technique for recommending provided information such as a program to a user, history information of a program that the user has reserved for recording in the past or a program that has been viewed has been recorded. Based on the user's own attribute and keyword information preset in advance, even if the user does not actively perform a recording reservation operation, a program that the user seems to like can be searched by referring to EPG (Electric Program Guide) etc. However, there is a technology that automatically records and presents it as recommended information.

  In addition, at stores on the Internet, a plurality of products purchased by customers at one time are recorded, and on the screen that introduces the products, many customers who have purchased the products in the past have purchased at the same time. There is a technique for displaying other products and recommending products to customers.

Also, in the program editing system, in order to provide information that takes into account both user preference and interest and objective topicality, the program information storage means is based on other target information related to targets other than the user. There is a technique having program editing means for searching program information from the inside and editing a program provided to the user based on the program information searched by the searching means (see, for example, Patent Document 1).
Japanese Patent Laid-Open No. 2003-199084

  By the way, in the prior art, as described above, when recording a program automatically based on history information in a user's recording reservation operation, user attributes, and keyword information, the user's own hobbies and preferences are met. Recorded programs are automatically recorded. However, if there is a program that many of the user's friends, etc. have reserved recording or a program that many of the user's friends actually watched, such a program is not in the user's own hobbies and preferences, such a program Is not automatically recorded. Therefore, for example, a program that has been discussed by a friend at a later date has already been broadcast, and as a result, a situation occurs in which many of his / her friends can watch and view the program that has been discussed.

  On the other hand, in the above-mentioned examples of stores on the Internet, it is possible to know products purchased by other people who are not related to users who have similar hobbies and preferences, but it is unknown whether other people are friends. Therefore, it is impossible to distinguish and know the products purchased by many of the user's friends.

  In addition, the technology described in Patent Document 1 cannot know contents such as programs, songs, products, etc. that are popular among the close friends of the user with the prior art. It was necessary to have the other party communicate through direct information transmission, such as actually talking or exchanging via e-mail or bulletin boards on the Internet. For this reason, there is a problem in which a situation where only oneself does not know the program or music that many of his close friends like and the products purchased by many of his close friends occurs. there were.

  Here, recently, there is a social network service in which a friendship / acquaintance relationship is established on a communication network represented by the Internet, etc., and in the social network service, “friend”, “friend of friend”, “friend” It is possible to clearly grasp the distance of the human relationship between the user (user) and the other party (friend), such as “friends of friends” and “friends of friends of friends”. Therefore, if content such as a program, music, or product can be recommended based on the distance information, it is conceivable to provide highly accurate information. However, conventionally, the above-described network has not been used.

  The present invention has been made in view of the above problems, and a recommendation rank selection device and a recommendation rank selection program for selecting a recommendation rank of content with high accuracy based on a relationship between a user and a partner. The purpose is to provide.

  In order to solve the above problems, the present invention employs means for solving the problems having the following characteristics.

According to the first aspect of the present invention, information is acquired from a predetermined person connected to the social network using a social network that forms a human relationship by connecting friends, and content information is obtained from the acquired information. In the recommendation order selection device for selecting a recommendation order, an importance calculation information acquisition management unit that acquires and manages importance calculation information for the predetermined person to calculate the importance of the content by user input; , and the predetermined importance calculation information acquired from the person, based on the weighting value for the predetermined person to be preset, possess a recommendation order selection unit for selecting the recommendation order of the content, the recommended The rank selection unit includes a network configuration information acquisition unit that acquires configuration information of the social network, and the network configuration information acquisition. A friend information selection unit that selects a friend to be used from the configuration information of the social network obtained by: an importance calculation information acquisition unit that acquires the importance calculation information for each friend selected by the friend information selection unit; A recommendation rank calculation for calculating a recommendation rank based on a weighting processing section for setting a weight value for each friend selected by the friend information selection section, the importance calculation information for each friend, and the weight value. The weighting processing unit sets weighting for the friend based on a distance of the human relationship between the friend and the user on the social network .

According to the first aspect of the present invention, based on the relationship between the user and the other party (predetermined person), the content recommendation order can be selected with high accuracy. Also, it is possible to calculate the recommendation order with high accuracy based on the information about friendship / acquaintances built on the network. Thereby, information such as contents viewed by many of his / her friends can be acquired. Furthermore, the content desired by the user can be acquired based on the distance of the human relationship with the friend.

  In the invention described in claim 2, the importance calculation information acquisition management unit includes an evaluation acquisition unit that acquires evaluation information evaluated by the user with respect to the content, and an operation content of the user with respect to the content. And an operation content acquisition unit to acquire.

  According to the second aspect of the present invention, the operation details and / or evaluation information for the content set by the user can be provided to another recommendation order selection device. Thereby, the importance in the content can be set with high accuracy.

In the invention described in claim 3 , the recommendation rank calculation unit calculates importance for the content for each friend based on the operation content and / or the evaluation information in the importance calculation information for each friend. It is characterized by that.

According to the third aspect of the present invention, it is possible to acquire the importance of the content for each friend. Thereby, it is possible to select the recommendation order of contents with higher accuracy.

In the invention described in claim 4 , the recommendation rank calculation unit ranks the contents based on a value obtained by summing the importance for each content, and sets the contents of the preset ranks. It is characterized by outputting information.

According to the fourth aspect of the present invention, it is possible to efficiently present only the information on the content required by the user to the user.

The invention described in claim 5 is a recommendation rank selection program which causes a computer to function as the recommendation rank selection device according to any one of claims 1 to 4 .

According to the fifth aspect of the present invention, it is possible to select the content recommendation order with high accuracy based on the relationship between the user and the other party (predetermined person). Also, it is possible to calculate the recommendation order with high accuracy based on the information about friendship / acquaintances built on the network. Thereby, information such as contents viewed by many of his / her friends can be acquired. In addition, content desired by the user can be acquired based on the distance of the human relationship with the friend. Furthermore , the recommendation rank selection process can be easily realized by installing the program.

  According to the present invention, it is possible to select a content recommendation order with high accuracy based on a relationship between a user and a partner.

  Hereinafter, embodiments in which a recommendation rank selection apparatus and a recommendation rank selection program according to the present invention having the above-described features are suitably implemented will be described in detail with reference to the drawings.

<Example>
FIG. 1 is a diagram illustrating a configuration example of a recommendation order selection system. A recommendation rank selection system 1 shown in FIG. 1 is configured to include one or more recommendation rank selection devices 2-1 to 2-n and a management server 3 that manages a social network described later. 2 and the management server 3 are connected via a communication network 4 represented by the Internet or the like in a state where data can be transmitted and received.

  Further, the recommendation rank selection devices 2-1 to 2-n are configured to include an importance calculation acquisition management unit 10, a recommendation rank selection unit 20, and a network interface unit 30.

  In the system configuration described above, a content server or the like that provides content such as programs and music to the recommendation order selection devices 2-1 to 2-n may be provided, and the management server 3 provides the content. May be.

  Here, in the recommendation rank selection device 2, the importance calculation acquisition management unit 10 is necessary for the other recommendation rank selection device 2 used by other users on the social network to select the recommendation rank of the content. Information is acquired and managed, and information is output in response to an information acquisition request from another recommendation order selection device 2.

  Further, the recommendation order selection unit 20 selects a recommendation order of content and presents it to the user via a display or the like, or outputs an instruction for automatic recording using a recording device or the like. Regarding the selection of the recommendation order of contents, the recommendation order is selected according to the distance within a social network service with a predetermined person such as a friend, the importance of each content for each friend, and the like.

  Further, the network interface unit 30 manages other recommendation rank selection devices 2 and the entire social network service via the communication network 4 in response to requests from the recommendation rank selection unit 20 and the importance calculation information acquisition management unit 20. It communicates with the server 3 to exchange information.

  Here, the management server 3 is for managing a social network service in a society / community classified into a certain category or field on the network. When a certain user participates in a certain community, a predetermined procedure is performed on the management server 3, and participation is permitted if a predetermined condition is satisfied. In addition, when participation is permitted, information such as introduction of friends who have already participated in the social network is registered. The management server 3 configures a social network based on the friend information.

<Structure of social network>
Here, the configuration of the social network will be described. FIG. 2 is a diagram illustrating a configuration example of the social network in the present embodiment. The social network shown in FIG. 2 shows an example of a human relationship (friend network configuration) in a social network service centered on a user who uses the above-described recommendation order selection device 2 as an example.

  That is, by requesting the current network configuration from the management server 3, a human relationship network set based on a friend who can connect to the social network and the user is acquired.

  Specifically, as shown in FIG. 2, the user has three direct “friends” A, B, and C, and two “friends of friends” D and E through the friend A. Furthermore, there are two “friends of friends of friends” named J and K via D, and three “friends of friends of friends of friends” named X1, X2 and X3 via J. Exists.

  Thus, if attention is paid only to the “distance” of the human relationship, as shown in FIG. 2, the user has three direct “friends” A to C, and “friends of friends” are D to I. There are 13 "friends of friends of friends" from J to V, and there are 35 "friends of friends of friends of friends" from X1 to X35.

  That is, the priority order in the social network relationship is set based on the relationship with the user (user), the information of each content is weighted according to the criterion, and the order of the recommendation information is selected. In addition, the social network as shown in FIG. 2 is constantly updated at a predetermined time, for example, at a predetermined timing such as participation / non-participation of a certain person in the social network, and in the management server 3 that manages the social network, etc. It is assumed that it is centrally managed.

  The user can acquire information on the social network by accessing the management server 3 described above through the network interface unit 30 of the recommendation order selection device 2.

  Next, the functional configuration of the recommendation order selection device 2 in the present embodiment will be specifically described with reference to the drawings. In the following description, the description is limited to the case where a program is handled as an example of content recommended to the user. However, the content in the present invention is not limited to this, and other than the program, there are various types of music, products, games, and the like. It may be the content.

<Importance calculation acquisition management unit 10>
First, the functional configuration of the importance calculation information acquisition management unit 10 will be described with reference to the drawings. FIG. 3 is a diagram illustrating an example of a functional configuration of the importance calculation information acquisition management unit. The importance calculation information acquisition management unit 10 shown in FIG. 3 is configured to include a user interface unit 101, an operation content acquisition unit 102, an active evaluation acquisition unit 103, and an importance calculation information management unit 104. Yes.

  The user interface unit 101 receives a recording reservation operation from a user, an operation during viewing, active evaluation information regarding a program to be viewed (or viewed), and the like. In addition, the user interface unit 101 outputs information input from the user to the operation content acquisition unit 102 and the active evaluation acquisition unit 103.

  The operation content acquisition unit 102 acquires information on the operation content for recording reservation or viewing by the user from the information obtained from the user interface unit 101, and outputs the information to the importance calculation information management unit 104. In addition, the active evaluation acquisition unit 103 acquires the active evaluation content performed by the user on the program via the user interface unit 101, and outputs the content to the importance calculation information management unit 104.

  Here, the active evaluation is, for example, before the broadcast of the program (or before the viewing), such as “recommend personally because this performer and this director must be absolutely interesting”. If it is a lot of information, or after the broadcast, “I personally recommend it because I watched it was very interesting,” or “I highly recommended it because it was the best program I watched.” “It was a very boring program that I watched, so I gave a low rating.” Note that the user's input of the evaluation for the program is not necessarily a necessary operation, but can be performed when each user wants to actively recommend to other friend users. Note that the above-described evaluation contents may be directly input, or a selection screen for selecting any of the above-described contents set in advance may be displayed to allow the user to select.

  The importance calculation information management unit 104 manages the operation information received from the operation content acquisition unit 102 and the active evaluation operation information received from the active evaluation acquisition unit 103. Here, an example of information managed by the importance degree calculation information management unit 104 will be described with reference to the drawings. FIG. 4 is a diagram illustrating an example of importance calculation information.

  As shown in FIG. 4, for example, information about programs A, B, and C is managed as information obtained from the operation content acquisition unit 102 such as when recording is actively reserved or when a program is viewed. In addition, as information obtained from the active evaluation acquisition unit 103, when personally recommending before program broadcast (or before viewing), when personally recommending after viewing, when personally recommending after viewing, Manage information such as personally disliked after viewing.

  The information managed by the importance degree calculation information management unit 104 is not limited to this, and the form of the managed information is also managed for each program, but the present invention is not limited to this. For example, during viewing of a program, since the program is boring midway, such information may be managed when viewing of the program is stopped or when the program is recorded and repeatedly viewed.

  Also, the information as shown in FIG. 4 is necessary when the recommendation rank selection unit 20 in the other recommendation rank selection device 2 wants to acquire importance calculation information for the user. That is, when there is a request for information acquisition from another recommendation order selection device 2, the importance calculation information management unit 104 transmits information from the network interface unit 30 via the communication network 4.

<Recommendation order selection unit 20>
Next, the functional configuration of the recommendation order selection unit 20 will be described with reference to the drawings. FIG. 5 is a diagram illustrating an example of a functional configuration of the recommendation order selection unit. The recommendation order selection unit 20 shown in FIG. 5 includes a friend network configuration information acquisition unit 201, a use friend selection unit 202, a setting status management unit 203, a weighting processing unit 204, an importance calculation information acquisition unit 205, And a recommendation rank calculation unit 206.

  The friend network configuration information acquisition unit 201 communicates with the management server 3 or the like that manages the entire social network service via the communication network 4 from the network interface unit 30, and the friend network configuration (social network in the user's social network service) Get information on relationships within the service. In addition, the friend network configuration information acquisition unit 201 outputs the acquired network configuration information to the use friend selection unit 202.

  The friend selection unit 202 sets which friend (predetermined person) information in the friend network is to be used in calculating the recommendation order. At this time, the use friend selection unit 202 sets the setting based on both the setting content preset by the user from the setting status management unit 203 and the content of the friend network configuration information obtained from the friend network configuration information acquisition unit 201. It can be carried out.

  For example, in the use friend selection unit 202, the setting content received from the setting status management unit 203 is up to “friends of friends of friends”, and the friend network configuration information received from the friend network configuration information acquisition unit 201 is described above. In the social network configuration shown in FIG. 2, only the friends (“friends”, “friends of friends”, and “friends of friends of friends”) necessary for calculating the recommendation rank are extracted from the network configuration of friends shown in FIG. The friend network configuration is as shown in FIG.

  In the example of the friend network configuration to be used shown in FIG. 6, a total of 22 people A to V are selected as “friends of the user's friends”. Therefore, the recommendation order of programs is selected based on information obtained from this friend.

  Note that the method of selecting a friend to be used is not limited to this. For example, only the friends A, D, K, and J in FIG. 6 may be selected. Only K and J may be selected. In addition, a group may be set in advance in a predetermined network range, and whether or not to use the group unit may be set.

  The use friend selection unit 202 outputs information on the selected friend to the importance calculation information acquisition unit 205.

  The weighting processing unit 204 performs weighting processing on each friend used for calculation of the recommendation order obtained from the setting status management unit 203 according to the distance between the user and each friend on the friend network. . Here, an example of weighting in the weighting processing unit 204 will be described with reference to the drawings.

  FIG. 7 is a diagram illustrating an example of setting a weighting value. In the example shown in FIGS. 7A to 7D, at least a network distance from the person (user), a relationship with the user, and a weighting value for the relationship with the user are set.

  Specifically, FIG. 7A shows an example in which weighting is set with emphasis on the user's direct friend. In other words, since the direct “friend” is important for the user, the “friend” has a weight of “5”, the “friend of the friend” is “2”, and the “friend of the friend of the friend” is “1”. The weighting is set lower as the distance from is increased. FIG. 7B shows an example in which the weighting value is set in proportion to “5” → “3” → “1” based on the distance from the user on the network. FIG. 7C shows an example in which a constant weight value is set regardless of the distance from the user. It should be noted that by making settings as shown in FIG. 7C, it is possible to search for programs that are generally popular and popular.

  Further, FIG. 7D shows a case where “B” is the best friend of the user among friends (A, B, C), a case where “B” is met in the near future, or other recommended information. When “B” is the most detailed among the friends in a certain field when obtaining the “B”, the weight of the friend B among the friends can be set particularly large. Similarly, the friend of the friend (D, E, F, G, H, I), for example, the friend of the friend (D, G, H) is weighted more than the friend of the friend (E, F, I). It can be set to increase.

  The user can acquire other recommendation information under various conditions by performing weighting as shown in FIGS. 7A to 7D in the weighting processing unit 204.

  Note that the weighting setting method is not limited to the content shown in FIG. The policy to be weighted is set based on the setting contents received in advance by the user from the setting state management unit 203. Further, the weighting processing unit 204 outputs the set weighting information to the recommendation order calculating unit 206.

  As described above, the setting status management unit 203 manages the setting contents preset by the user, such as the range of friends used for calculating the recommendation order and what policy is used for weighting. 202, information necessary for the weighting processing unit 204 and a recommendation rank calculation unit 206 described later is output.

  Also, the importance calculation information acquisition unit 205 receives information on which friend to use for calculating the recommendation order from the use friend determination unit 202, and the network interface unit 30 transmits the information via the communication network 4 according to this information. All of them communicate with the recommendation order selection device 2 of friends to acquire importance calculation information. When the management server 3 and other servers described above manage the importance calculation information of the recommendation order selection devices 2-1 to 2-n in a unified manner, the importance calculation is performed from these servers. Get information. The importance calculation information acquisition unit 205 outputs the acquired importance calculation information to the recommendation rank calculation unit 206.

  Here, the information that the importance calculation information acquisition unit 205 receives from the user's friend system and outputs to the recommendation ranking calculation unit 206 is the information shown in FIG. 4 described above. That is, information on general operations such as “recording reservation” operation and “program viewing” operation performed on each program by the user's friend, and active evaluation performed on the program by the user's friend. This is information on the attaching operation. The recommendation rank calculation unit 206 determines the importance of each program obtained for each friend set in the use friend selection unit 202 based on the importance setting criteria.

  It should be noted that the recommendation level calculation unit 206 sets the importance level for determining the importance level with reference to the setting content in the setting status management unit 203. Here, an example of the importance setting criterion will be described with reference to the drawings.

  FIG. 8 is a diagram illustrating an example of importance setting criteria. Note that the setting content of the importance setting criteria shown in FIG. 8 includes an operation time, an operation content of each user, and an importance level corresponding to the operation content. Here, FIG. 8 (a) shows an example of setting importance that emphasizes each person's active evaluation, and FIG. 8 (b) shows an example of setting importance that emphasizes each person's viewing trend. That is, it shows an example of setting the importance with an emphasis on whether or not each friend has watched the program. As shown in FIG. 8, the importance addition value and subtraction value are set based on the operation of each user (“0” when addition and subtraction are not performed).

  The recommendation information rank calculation unit 206 determines the importance value of each program for each friend set as described above from the operation information for each program by the user's friend received from the importance calculation information acquisition unit 205. Then, the recommendation rank is calculated from the weight value of each friend received from the weighting processing unit 204. Here, an example of recommendation rank calculation in the recommendation information rank calculation unit 206 will be described with reference to the drawings.

  FIG. 9 is a diagram illustrating an example of the relationship between the weighting value and importance of each friend. Although FIG. 9 shows an example in which the recommendation order is selected for five programs from program 1 to program 5, the number of programs and the content content are not limited to this in this embodiment.

  FIG. 9 shows the weighting of each friend received by the recommendation rank calculation unit 206 from the weighting processing unit 204 in each friend (persons A to V) corresponding to the network configuration shown in FIG. The importance of the program set from the value and the operation content of each friend is shown.

  In addition, the weighting value of each friend shown in FIG. 9 shows an example in which the weighting value shown in FIG. The importance of each program shown in FIG. 9 is determined based on the operation information for each program by each friend for five programs from program 1 to program 5 as shown in FIG. The importance value of each program for each friend set based on is shown.

  Here, in the case of FIG. 8A, for example, for the program 1 of person A, the recording reservation operation is actively performed (+1), and the program is actually viewed (+1), but the active evaluation operation is performed. If there is no (0), the importance value is calculated as “1 + 1 + 0 = 2”. In this way, the importance of each program of each friend is set.

  In the case of the example of FIG. 9, since person A is a direct friend to the user, the weight value is “5”, the importance value of program 1 for person A is “2”, and the importance value of program 2 Is “5”, the importance value of program 3 is “1”, the importance value of program 4 is “3”, and the importance value of program 5 is “5”.

  Next, the recommendation rank calculation unit 206 multiplies the weighting value based on the distance to the friend with respect to the above-mentioned friend by the importance of each program (weighting value × importance of each program), and calculates each program from the calculated value. Calculate the total for each.

  Here, FIG. 10 is a diagram illustrating an example of the calculation result of the importance level of the user based on FIG. In FIG. 10, it can be determined that a result having a large total value has a high evaluation among the user's friends or is viewed by many of the user's friends. Therefore, the recommendation rank calculation unit 206 sorts the programs based on the total value, and selects the recommendation rank in descending order of the total value. In the case of the example in FIG. 10, the recommendation order of the five programs is, in descending order of the total value, program 2 (178) → program 5 (169) → program 4 (128) → program 1 (97) → program 3 (82). Become.

  Using the recommendation order obtained as described above, for example, before broadcasting (or before viewing), a recording device or the like that automatically reserves a recording for a program that has a high recommendation order and is not reserved for recording. Control information can be output. In addition, after the broadcast, it is possible to recommend a program that has been recorded but has not been viewed by presenting it to the user according to the recommendation order. In addition, the recommendation rank calculation part 206 is the information of the content of the priority set beforehand, such as a program from the 1st rank to the 3rd rank, or only a 5th rank program among the ranked programs. It may be output. Thereby, only the content information required by the user can be efficiently presented to the user.

  As described above, the recommendation order selection device 2 can select the recommendation order of content with high accuracy based on the relationship between the user and the other party.

<Recommendation order selection program>
Here, the recommendation order selection device 2 according to the present invention can perform the recommendation order selection processing according to the present invention using the above-described dedicated device configuration or the like, but can execute the processing in each configuration by a computer. By generating a program and installing the program on, for example, a general-purpose personal computer or workstation, the above-described recommendation order selection process can be realized.

<Hardware configuration>
Here, a hardware configuration example of a computer capable of executing the recommendation order selection processing according to the present invention will be described with reference to the drawings. FIG. 11 is a diagram illustrating an example of a hardware configuration capable of realizing the recommendation order selection process according to the present invention.

  11 includes an input device 301, an output device 301, a drive device 303, an auxiliary storage device 304, a memory device 305, a CPU (Central Processing Unit) 306 for performing various controls, and a network connection device. 307, which are connected to each other via a system bus B.

  The input device 301 has a pointing device such as a keyboard and a mouse operated by a user, and inputs various operation signals such as execution of a program from the user. The output device 302 has a display (monitor) for displaying various windows and data necessary for operating the computer main body for performing the processing according to the present invention, and the execution of the processing according to the present invention by the control program of the CPU 306. Progress and results can be displayed.

  Here, in the present invention, the execution program installed in the computer main body is provided by, for example, a recording medium 308 such as a CD-ROM. The recording medium 308 on which the program is recorded can be set in the drive device 303, and the execution program included in the recording medium 308 is installed in the auxiliary storage device 304 from the recording medium 308 via the drive device 303.

  Further, the drive device 303 can record the recommendation order selection program according to the present invention on the recording medium 308. Thereby, using the recording medium 308, it can be easily installed in a plurality of other computers, and the recommendation rank selection process can be easily realized.

  The auxiliary storage device 304 is storage means such as a hard disk, and can store an execution program in the present invention, a control program provided in a computer, and the like, and can perform input / output as necessary. The auxiliary storage device 304 can also be used as a storage unit for storing various information managed by the importance calculation information management unit 104 and the setting status management unit 203 described above.

  Based on a control program such as an OS (Operating System) and an execution program read and stored by the memory device 305, the CPU 306 performs various operations and input / output of data with each hardware component, and the like. Each process in the recommendation order selection process can be realized by controlling the process. Various information necessary during the execution of the program can be acquired from the auxiliary storage device 304 and can be stored.

  The network connection device 307 is obtained by connecting to a communication network such as a telephone line or a LAN cable to obtain an execution program from another terminal connected to the communication network or executing the program. The execution result or the execution program in the present invention can be provided to another terminal or the like.

  With the hardware configuration described above, the above-described recommendation order selection process can be realized at a low cost without requiring a special device configuration. Also, the recommendation rank selection process can be easily realized by installing the program.

<Recommendation order selection procedure>
Next, a recommendation order selection processing procedure in the execution program will be described using a flowchart. In the processing procedure described later, a program is described as an example of recommended content. However, the present invention is not limited to this. Further, it is assumed that the operation content based on the viewing of the program by the user and the evaluation content actively evaluated by the user are already managed as the importance calculation information.

  FIG. 12 is a flowchart showing a recommendation order selection processing procedure in the present invention. First, the current social network configuration information is acquired (S01). The network configuration information can be acquired by making a request to, for example, a management server that manages the network configuration. Next, from the network configuration acquired in S01, it is determined which friend is to be used and a friend to be used is selected (S02).

  Next, a weighting value based on the distance to the friend (predetermined person, friend) selected for the friend selected in S02 is set (S03). Also, importance level information for calculating the importance level of each program for each friend used for the friend selected in S02 is acquired (S04).

  Next, based on the weighting value set in S03 and the importance of each program from each friend obtained in S04, the importance for each program of each friend is calculated, and the total is calculated for each program. (S05). Further, the programs are sorted (sorted) in descending order based on the calculated total value (S06), and the programs from the top to the preset order with respect to the recommended order of the selected program are presented as a recommended program on a display or the like. (S07).

  As described above, according to the recommendation order selection processing procedure, the recommendation order of contents can be selected with high accuracy based on the relationship between the user and the other party (predetermined person). Also, the recommendation rank selection process can be easily realized by installing the program.

  As described above, according to the present invention, it is possible to select a content recommendation order with high accuracy based on the relationship between a user and a partner. Specifically, the recommendation order of contents can be selected with high accuracy based on the distance between human relationships in the social network system. By calculating the importance for the user from the distance between himself / herself within the social network service and the importance of the content for each friend, and recommending the content to the user based on the calculated importance, Popular content among your friends is automatically recommended to users. In addition, it is possible to perform processing such as automatically recording a popular program among friends.

  The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to such specific embodiments, and various modifications, within the scope of the gist of the present invention described in the claims, It can be changed.

It is a figure showing an example of 1 composition of a recommendation rank selection system. It is a figure which shows one structural example of the social network in a present Example. It is a figure which shows an example of a function structure of an importance calculation information acquisition management part. It is a figure which shows an example of importance calculation information. It is a figure which shows an example of a function structure of a recommendation order selection part. It is a figure which shows an example of the friend network structure to utilize. It is a figure which shows an example of the setting of a weighting value. It is a figure which shows an example of the importance setting criteria. It is a figure which shows an example of the relationship between the weighting value in each friend, and importance. It is a figure which shows an example of the calculation result of the importance in the user himself / herself based on FIG. It is a figure which shows an example of the hardware constitutions which can implement | achieve the recommendation order selection process in this invention. It is a flowchart which shows the recommendation order selection processing procedure in this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Recommendation ranking selection system 2 Recommendation ranking selection device 3 Management server 4 Communication network 10 Importance calculation information acquisition management unit 20 Recommendation ranking selection unit 30 Network interface unit 101 User interface unit 102 Operation content acquisition unit 103 Active evaluation acquisition unit 105 Importance calculation information management unit 201 Friend network configuration information acquisition unit 202 Use friend selection unit 203 Setting status management unit 204 Weighting processing unit 205 Importance calculation information acquisition unit 206 Recommendation rank calculation unit 301 Input device 302 Output device 303 Drive device 304 Auxiliary storage device 305 Memory device 306 CPU
307 Network connection device 308 Recording medium

Claims (5)

  1. In a recommendation rank selection device that acquires information from a predetermined person connected to the social network using a social network that forms a human relationship by combining friends, and selects a recommendation rank of content based on the acquired information ,
    An importance calculation information acquisition management unit that acquires and manages importance calculation information for the predetermined person to calculate the importance of the content by a user input;
    Wherein the acquired importance degree calculation information from the predetermined person, on the basis of the weighting value for the predetermined person to be preset, possess a recommendation order selection unit for selecting the recommendation order of the content,
    The recommendation order selection unit includes:
    A network configuration information acquisition unit for acquiring configuration information of the social network;
    A friend information selection unit that selects a friend to use from the configuration information of the social network obtained by the network configuration information acquisition unit;
    An importance calculation information acquisition unit for acquiring the importance calculation information for each friend selected by the friend information selection unit;
    A weighting processing unit for setting a weighting value for each friend selected by the friend information selection unit;
    A recommendation rank calculation unit that calculates a recommendation rank based on the importance calculation information for each friend and the weighting value;
    The weighting processing unit
    A recommendation order selection device , wherein weighting is set for the friend based on a distance of the human relationship between the friend and the user on the social network .
  2. The importance calculation information acquisition management unit
    An evaluation acquisition unit for acquiring evaluation information evaluated by the user for the content;
    The recommendation order selection apparatus according to claim 1, further comprising an operation content acquisition unit configured to acquire an operation content of the user with respect to the content.
  3. The recommendation order calculation unit includes:
    The recommendation rank selection device according to claim 2, wherein the importance for the content for each friend is calculated based on the operation content and / or the evaluation information in the importance calculation information for each friend.
  4. The recommendation order calculation unit includes:
    4. The recommendation order according to claim 3 , wherein the contents are ranked based on a value obtained by summing the importance for each content, and information of contents having a preset order is output. Selection device.
  5. A recommendation rank selection program for causing a computer to function as the recommendation rank selection device according to any one of claims 1 to 4 .
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