Activity recommendation device and activity recommendation system

Abstract

The purpose of the present invention is to provide an activity recommendation device and an activity recommendation system that recommend, on the basis of the degree of activity between users constituting a community, appropriate activities to the users. The activity recommendation device comprises: a first classification unit that classifies a plurality of first users belonging to a first group, among a plurality of users, into a plurality of clusters including a first cluster, on the basis of first attribute information on the attribute of each of the plurality of first users; a second classification unit that classifies a plurality of second users belonging to a second group, among the plurality of users, into a plurality of clusters, on the basis of second attribute information on the attribute of each of the plurality of second users and the feature of each of the clusters classified by the first classification unit; and a recommendation unit that recommends activities of one or more of the first users belonging to the first cluster to one or more of the second users belonging to the first cluster. The degree of activity of each of the plurality of second users is lower than the degree of activity of each of the plurality of first users.

Description

Activity Promotion Device and Activity Promotion System

[0001] The present invention relates to an activity promotion device and an activity promotion system.

[0002] For example, there is an increasing need for a community composed of users with a common purpose, having a structure as a decentralized autonomous organization called DAO (Decentralized Autonomous Organization). In order to expand the community, a technique for recommending appropriate activities to the users constituting the community has been conventionally used.

[0003] For example, Patent Document 1 discloses an information providing method for clustering a plurality of users based on each of daily behavior information indicating the results of daily behavior and utilization result information indicating the results of using a sports facility, and recommending the use of the sports facility to a specific user based on the result of the clustering.

[0004] Japanese Patent Application Laid-Open No. 2024-101713

[0005] However, the technique according to Patent Document 1 did not recommend appropriate activities to specific users, taking into account the degree of activities among users.

[0006] An object of the present disclosure is to provide an activity promotion device and an activity promotion system that recommend appropriate activities to users, taking into account the level of activities among the users constituting the community.

[0007] The activity recommendation device relating to this disclosure comprises: a first classification unit that classifies a plurality of first users into a plurality of clusters, including a first cluster, based on first attribute information relating to the attributes of each of the plurality of first users belonging to a first group from among the plurality of users; a second classification unit that classifies the plurality of second users into the plurality of clusters based on second attribute information relating to the attributes of each of the plurality of second users belonging to a second group from among the plurality of users, and the characteristics of each cluster classified by the first classification unit; and a recommendation unit that recommends the activities of one or more first users belonging to the first cluster to one or more second users belonging to the first cluster, wherein the degree of activity of each of the plurality of second users is less than the degree of activity of each of the plurality of first users.

[0008] The activity recommendation system relating to this disclosure comprises a plurality of terminals corresponding to a plurality of users on a one-to-one basis, and an activity recommendation device, wherein the activity recommendation device comprises: a first classification unit that classifies the plurality of first users into a plurality of clusters including a first cluster based on first attribute information relating to the attributes of each of the plurality of first users belonging to a first group from among the plurality of users; a second classification unit that classifies the plurality of second users into the plurality of clusters based on second attribute information relating to the attributes of each of the plurality of second users belonging to a second group from among the plurality of users, and the characteristics of each cluster classified by the first classification unit; a recommendation unit that recommends the activities of one or more first users belonging to the first cluster to one or more second users belonging to the first cluster; and a display control unit that causes information representing the activities of one or more first users to be displayed on the terminal possessed by each of the one or more second users, wherein the degree of activity of each of the plurality of second users is less than the degree of activity of each of the plurality of first users.

[0009] According to this disclosure, it is possible to recommend appropriate activities to users, taking into account the degree of activity among the users who make up the community.

[0010] A diagram showing an example of the overall configuration of the activity recommendation system 1. A block diagram showing an example of the configuration of the activity recommendation device 10. A diagram showing an example of the configuration of the user database UDB. A diagram showing an example of the configuration of the first activity information database ADB1. An example of a network diagram NF corresponding to the first activity information database ADB1. A diagram showing an example of the configuration of the second activity information database ADB2. A diagram showing an example of the configuration of the third activity information database ADB3. A diagram showing an example of the operation of the first classification unit 115. An explanatory diagram of non-hierarchical clustering. An explanatory diagram of non-hierarchical clustering. A diagram showing an example of the operation of the second classification unit 116. An explanatory diagram of how the second classification unit 116 classifies multiple second users Us. An explanatory diagram of how the second classification unit 116 classifies multiple second users Us. A diagram showing an example of the operation of the extraction unit 117. A diagram showing an example of a stage ST to which a user U, who is a fan of a celebrity, belongs in a celebrity fan club. A flowchart showing an example of the operation of the activity recommendation device 10.

[0011] The activity recommendation system 1 according to this embodiment will be described below with reference to Figures 1 to 16.

[0012] 1: Configuration of the Embodiment 1-1: Overall Configuration Diagram 1 is a block diagram showing the overall configuration of the activity recommendation system 1 according to this embodiment. As shown in Figure 1, the activity recommendation system 1 comprises an activity recommendation device 10 and terminals 20[1] to 20[n]. The activity recommendation device 10 and terminals 20[1] to 20[n] are connected to each other via a communication network NET so that they can communicate with one another. n is an integer of 1 or more. k is an integer of 1 or more and less than or equal to n. In the following description, terminals 20[1] to 20[n] may be collectively referred to as "terminal 20". Note that in Figure 1, user U uses terminal 20. Also, user U[1] uses terminal 20[1]. User U[2] uses terminal 20[2]. User U[k] uses terminal 20[k]. User U[n] uses terminal 20[n]. There is a one-to-one correspondence between terminals 20[1] to 20[n] and users U[1] to users U[n]. In the following explanation, user U[k] may be described as a representative example of user U. Also, in the following explanation, terminal 20[k] may be described as a representative example of terminal 20.

[0013] Users U[1] to U[n] belong to a single community C. Community C is, for example, a community composed of students of a school or employees of a company. Another example of Community C is a community composed of members who participate in the same SNS (Social Networking Service). Yet another example of Community C is a community composed of members who participate in a common game or virtual space. Yet another example of Community C is a fan club for a celebrity. Community C has, for example, an autonomous decentralized organization called a DAO.

[0014] Terminal 20[k] is a device used by user U[k]. By using terminal 20[k], user U[k] can, for example, communicate with other users U belonging to community C. Also, by using terminal 20[k], user U[k] can, for example, purchase goods or services related to the activities of community C. For example, items used in the above-mentioned game or virtual space are an example of goods or services related to the activities of community C.

[0015] The activity recommendation device 10 recommends activities to each of the multiple users U. Specifically, in order to increase the overall activity level of community C, the activity recommendation device 10 displays recommended activities for user U[k] on the terminal 20[k] used by user U[k]. Here, "overall activity level of community C" refers to the sum of the activity levels of users U[1] to U[k] belonging to community C. An example of each of the multiple users U's activities is communication between one user U and the other users U. In this case, the sum of the activity levels of users U[1] to U[k] belonging to community C refers to, for example, the amount of data used for communication between the multiple users U. Another example of each of the multiple users U's activities is the purchase of goods or services related to the activities of community C. In this case, the sum of the activity levels of users U[1] to U[k] belonging to community C refers to, for example, the total amount of purchases of such goods or services by the multiple users U.

[0016] 1-2: Diagram 2 of the Activity Recommendation Device Configuration is a block diagram showing an example configuration of the Activity Recommendation Device 10. As shown in Figure 2, the Activity Recommendation Device 10 comprises a processing unit 11, a storage device 12, a display device 13, an input device 14, and a communication device 15. Each element of the Activity Recommendation Device 10 is interconnected by one or more buses for communicating information.

[0017] The processing unit 11 is a processor that controls the entire activity recommendation device 10. The processing unit 11 is configured using, for example, one or more chips. The processing unit 11 is configured using, for example, a central processing unit (CPU) that includes interfaces with peripheral devices, an arithmetic unit, and registers. Some or all of the functions of the processing unit 11 may be implemented by hardware such as a DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), PLD (Programmable Logic Device), and FPGA (Field Programmable Gate Array). The processing unit 11 executes various processes in parallel or sequentially.

[0018] The storage device 12 is a recording medium that can be read from and written to by the processing device 11. The storage device 12 includes, for example, non-volatile memory and volatile memory. Non-volatile memory is, for example, ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory), and EEPROM (Electrically Erasable Programmable Read Only Memory). Volatile memory is, for example, RAM (Random Access Memory).

[0019] The storage device 12 stores multiple programs, including the control program PR1, which is executed by the processing device 11. The storage device 12 functions as a work area for the processing device 11.

[0020] Furthermore, the storage device 12 stores the user database UDB, the first activity information database ADB1, the second activity information database ADB2, the third activity information database ADB3, and the learning model LM.

[0021] The user database (UDB) is a database that stores user information (UD) related to the attributes of user U.

[0022] Figure 3 shows an example of the configuration of the user database (UDB). Each user information (UD) stored in the user database (UDB) has the following fields: "User ID," "User Name," "Date of Birth," "Place of Residence," "Occupation," "Personality Type ID," and "Values ​​Type ID."

[0023] The information in the "User ID" field indicates the identifier of the user U corresponding to each user information UD.

[0024] The information in the "Username" field indicates the name of User U, as shown by the "User ID". Note that in Figure 3, for simplicity of explanation, the "Username" information is shown using initials. However, the "Username" information may also be the full name.

[0025] The information in the "Date of Birth" field indicates the date of birth of user U, as indicated by the "User ID".

[0026] The information in the "Place of Residence" field indicates the location where User U, identified by the "User ID," resides.

[0027] The information in the "Occupation" field indicates the occupation of user U, who is identified by the "User ID".

[0028] The information in the "Personality Type ID" field indicates the identifier of the personality type of user U, as shown by the "User ID". For example, the administrator of the activity recommendation device 10 has all users U belonging to community C take a personality diagnostic test in advance, and classifies each user U into one of the personality types based on the results of the personality diagnostic test. The "Personality Type ID" is the identifier of that personality type.

[0029] The information in the "Value Type ID" field indicates the cumulative identifier of the values ​​of user U, as indicated by the "User ID". For example, the administrator of the activity recommendation device 10 conducts a questionnaire in advance with all users U belonging to community C. Based on the results of the questionnaire, the administrator determines that each user U possesses one of the values. The "Value Type ID" is the identifier of the value type.

[0030] In this context, "values" refers, for example, to what user U finds valuable. These "values" serve as the criteria or norms that user U uses to make judgments.

[0031] As an example, the user database UDB shown in Figure 3 stores user information UD indicating that user U, whose "User ID" is "001", has a "Username" of "N.A", a "Date of Birth" of "October 8, 1979", a "Place of Residence" of "Osaka Prefecture", an "Occupation" of "Civil Servant", a "Personality Type ID" of "A028", and a "Values ​​Type ID" of "t09".

[0032] In Figure 2, the first activity information database ADB1 is a database that stores the first activity information AD1 related to the activities of user U. The first activity information AD1 related to the activities of user U is, for example, activity information related to communication between one user U and another user U.

[0033] Figure 4 shows an example of the configuration of the first activity information database ADB1. Each of the first activity information AD1 stored in the first activity information database ADB1 has the following items: "Subject User ID", "Object User ID", "Execution Date and Time", "Activity Details", and "Data Volume".

[0034] The information in the "Subject User ID" field indicates the identifier of User U, who is the subject of the activity.

[0035] The information in the "Object User ID" field indicates the identifier of User U, the object of the activity.

[0036] The information in the "Execution Date and Time" column indicates the date and time the activity was performed.

[0037] The information in the "Activity Details" column indicates the content of the activity. An activity indicated by "Send" indicates that User U, indicated by "Subject User ID," sends a message to User U, indicated by "Object User ID." An activity indicated by "Reply" indicates that User U, indicated by "Subject User ID," replies to a message from User U, indicated by "Object User ID." An activity indicated by "Post" indicates that "Subject User ID" posts a message on SNS. An activity indicated by "Share" indicates that User U, indicated by "Subject User ID," shares a message posted by User U, indicated by "Object User ID," on SNS. An activity indicated by "Give" indicates that User U, indicated by "Subject User ID," sends a message with an attached file to User U, thereby giving that file as a gift. The file may be, for example, an audio file, an image file, or a video file.

[0038] The information in the "Data Volume" column indicates the amount of data for the message handled by User U, indicated by the "Subject User ID," in the activity indicated by the "Activity Details." If the "Activity Details" is "Gift," the information in the "Data Volume" column indicates the total data volume of the message sent from User U, indicated by the "Subject User ID," to User U, indicated by the "Object User ID," and any files attached to that message.

[0039] As an example, the first activity information database ADB1 shown in Figure 4 stores first activity information AD1, which indicates that user U with "Subject User ID" "091" replied to a message from user U with "Object User ID" "198" at "December 1, 2024, 13:45:34", and that the "data size" of the reply message is 2045 kilobytes.

[0040] Furthermore, in the first activity information database ADB1 shown in Figure 4, the messages handled in the activities of "replying," "sending," and "giving" may be messages on social networking services (SNS), emails, or messages sent and received via SMS (Short Message Service).

[0041] Figure 5 is an example of a network diagram NF representing multiple first activity information AD1 stored in the first activity information database ADB1. The network diagram NF has multiple nodes ND1 and multiple edges ED connecting the multiple nodes ND1. The multiple nodes ND1 correspond one-to-one with multiple users U belonging to community C. Each of the multiple edges ED is set between two users U when communication exists between one user U and another user U included in the multiple users U. The larger the amount of data sent and received in the communication between the two users U, and the more frequent the communication, the thicker the edge ED between the two users U becomes.

[0042] In the example shown in Figure 5, multiple users U belonging to community C include users Ua[1] to Ua[4], users Ub[1] to Ub[3], users Uc[1] to Uc[3], and users Ud[1] to Ud[3]. Users Ua[1] to Ua[4] represent users with high activity levels. Specifically, users Ua[1] to Ua[4] represent users whose activity levels related to "writing," "sharing," and "giving" are in the top 30%, and whose activity levels related to "sending" and "replying" are also in the top 30%. Users Ub[1] to Ub[3] represent users with high information provision levels. Specifically, users Ub[1] to Ub[3] represent users whose activity levels related to "writing," "sharing," and "giving" are in the top 30%, and whose activity levels related to "sending" and "replying" are in the bottom 70%. Users Uc[1] to Uc[3] represent users with a high volume of reactions. More specifically, users Uc[1] to Uc[3] represent users whose activity levels related to "writing," "sharing," and "giving" are in the bottom 70%, and their activity levels related to "sending" and "replying" are in the top 30%. Users Ud[1] to Ud[3] represent users with a low level of activity. More specifically, users Ud[1] to Ud[3] represent users whose activity levels related to "writing," "sharing," and "giving" are in the bottom 70%, and their activity levels related to "sending" and "replying" are also in the bottom 70%.

[0043] In Figure 2, the second activity information database ADB2 is a database that stores second activity information AD2 related to the activities of user U. The second activity information AD2 related to user U's activities is, for example, activity information concerning user U's purchase of goods or services. For example, user U's purchase of goods or services is a purchase made at an online shop on the internet. The purchase history of such goods or services is recorded on the terminal 20 used by user U. The second activity information AD2 showing the purchase history of such goods or services is transmitted from the terminal 20 to the activity recommendation device 10.

[0044] FIG. 6 is a diagram showing a configuration example of the second activity information database ADB2. Each of the second activity information AD2 stored in the second activity information database ADB2 has items of "subject user ID", "execution date and time", "purchased item", and "purchase amount".

[0045] The information in the "subject user ID" column indicates the identifier of the user U who is the subject of the activity.

[0046] The information in the "execution date and time" column indicates the date and time when the purchase of goods or services by the user U indicated by the "subject user ID" was executed.

[0047] The information in the "purchased item" column indicates the item of goods or services purchased by the user U indicated by the "subject user ID".

[0048] The information in the "purchase amount" column indicates the amount of goods or services purchased by the user U indicated by the "subject user ID".

[0049] In the second activity information database ADB2 shown in FIG. 6, as an example, the second activity information AD2 indicating that the user U with the "subject user ID" of "098" purchased a "notebook" for "3000 yen" at "9:14:21 on December 1, 2024" is stored.

[0050] In FIG. 2, the third activity information database ADB3 is a database in which the third activity information AD3 regarding the activities of the user U is stored. The third activity information AD3 regarding the activities of the user U is, as an example, activity information regarding a famous person supported by the user U. The activity information regarding the famous person is recorded in the terminal 20 used by the user U and transmitted to the activity recommendation device 10.

[0051] FIG. 7 is a diagram showing a configuration example of the third activity information database ADB3. Each of the third activity information AD3 stored in the third activity information database ADB3 has items of "subject user ID", "execution date and time", "activity content", and "activity volume index".

[0052] The information in the "subject user ID" column indicates the identifier of the user U who is the subject of the activity.

[0053] The information in the "Execution Date and Time" column indicates the date and time the activity was performed.

[0054] The information in the "Activity Details" column describes the nature of the activity.

[0055] The information in the "Activity Level Index" column shows an index corresponding to the activity level of the activity indicated by "Activity Type".

[0056] As an example, the third activity information database ADB3 shown in Figure 7 stores third activity information AD3, which indicates that user U, whose "Subject User ID" is "034", applied to a fan gathering of a celebrity that user U supports at "December 1, 2024, 12:13:53 PM", and that the activity level index indicating the amount of activity for that activity is "500".

[0057] As an example, a specific application is installed on the terminal 20 used by user U, which records the details of user U's activities related to the celebrity they support and the date and time of those activities, as well as calculates an activity level index indicating the amount of activity involved.

[0058] In Figure 2, the learning model LM is a learning model used by the first classification unit 115 (described later) when classifying multiple first users Uf, which are included in multiple users U, into multiple clusters CL using non-hierarchical clustering. The first classification unit 115 inputs the user information UD of the multiple first users Uf into the learning model LM. The learning model LM outputs the classification results of the multiple first users Uf into multiple clusters CL as a result of non-hierarchical clustering.

[0059] The display device 13 is a device that displays images and text information. The display device 13 displays various images under the control of the processing device 11. For example, various display panels such as liquid crystal display panels and organic EL display panels are preferably used as the display device 13.

[0060] The input device 14 is a device that receives operations from the administrator of the activity recommendation device 10. For example, the input device 14 is composed of a keyboard, touchpad, touch panel, or pointing device such as a mouse.

[0061] The communication device 15 is hardware that acts as a transmitting and receiving device for communicating with other devices. The communication device 15 is also called, for example, a network device, a network controller, a network card, or a communication module. The communication device 15 may be equipped with a connector for wired connection and an interface circuit corresponding to the connector. The communication device 15 may also be equipped with a wireless communication interface. Examples of connectors and interface circuits for wired connection include products compliant with wired LAN, IEEE1394, and USB. Examples of wireless communication interfaces include products compliant with wireless LAN and Bluetooth®.

[0062] The processing unit 11 functions as a communication control unit 111, an acquisition unit 112, a calculation unit 113, a group classification unit 114, a first classification unit 115, a second classification unit 116, an extraction unit 117, a recommendation unit 118, and a display control unit 119, for example, by reading and executing the control program PR1 from the storage device 12.

[0063] The communication control unit 111 causes the communication device 15 to send and receive various information between itself and the terminal 20.

[0064] The acquisition unit 112 acquires the first activity information AD1 from the terminal 20 and stores it in the first activity information database ADB1. Alternatively, the acquisition unit 112 may acquire the first activity information AD1 from a server (not shown) that provides a message sending and receiving service between terminals 20, rather than from the terminal 20, and store it in the first activity information database ADB1. This sending and receiving service may be, for example, an SNS. In this case, the "server (not shown)" is a server that provides an SNS service. Alternatively, the above sending and receiving service may be, as another example, a service that uses email. In this case, the "server (not shown)" is an email server.

[0065] Furthermore, the acquisition unit 112 acquires the second activity information AD2 from the terminal 20 and stores it in the second activity information database ADB2. If a payment application for purchasing goods or services is installed on the terminal 20, the acquisition unit 112 acquires the second activity information AD2 from the server that manages the payment application.

[0066] Furthermore, the acquisition unit 112 acquires the third activity information AD3 from the terminal 20 and stores it in the third activity information database ADB3.

[0067] Furthermore, as described above, the activity recommendation device 10 displays recommended activities for user U[k] on the terminal 20[k] used by user U[k]. The acquisition unit 112 may acquire a display request from the terminal 20[k] to display recommended activities for user U[k].

[0068] The calculation unit 113 calculates the activity level of each of the multiple users U. Specifically, the calculation unit 113 extracts first activity information AD1 from the first activity information database ADB1, indicating that the "main user ID" is user U[k]. The calculation unit 113 then converts the "data amount" contained in the extracted first activity information AD1 into an "activity level index" using a pre-set conversion formula. Furthermore, the calculation unit 113 calculates the sum of all "activity level indices" contained in the extracted first activity information AD1. The calculation unit 113 also extracts second activity information AD2 from the second activity information database ADB2, indicating that the "main user ID" is user U[k]. The calculation unit 113 then converts the "purchase amount" contained in the extracted second activity information AD2 into an "activity level index" using a pre-set conversion formula. Furthermore, the calculation unit 113 calculates the sum of all "activity level indices" contained in the extracted second activity information AD2. Furthermore, the calculation unit 113 extracts third activity information AD3 from the third activity information database ADB3, which indicates that the "primary user ID" is user U[k]. The calculation unit 113 also extracts the sum of all "activity level indices" contained in all the extracted third activity information AD3. Finally, the calculation unit 113 calculates the total value of user U[k]'s "activity level indices" by summing the sum of "activity level indices" contained in the first activity information AD1, the sum of "activity level indices" contained in the second activity information AD2, and the sum of "activity level indices" contained in the third activity information AD3.

[0069] The group classification unit 114 classifies the multiple users U into multiple first users Uf and multiple second users Us based on information regarding the activities of each of the multiple users U. Here, "information regarding the activities of each of the multiple users U" means, as an example, at least one of the first activity information AD1 stored in the first activity information database ADB1, the second activity information AD2 stored in the second activity information database ADB2, and the third activity information AD3 stored in the third activity information database ADB3. Each of the first activity information AD1, the second activity information AD2, and the third activity information AD3 is an example of "first information".

[0070] As an example, the group classification unit 114 classifies multiple users U into multiple first users Uf and multiple second users Us based on the activity index of each user U calculated by the calculation unit 113. Specifically, if the activity index of user U[k] is above a pre-set threshold, the group classification unit 114 classifies user U[k] into a first user Uf belonging to the first group GPf. On the other hand, if the activity index of user U[k] is below the threshold, the group classification unit 114 classifies user U[k] into a second user Us belonging to the second group GPs. In other words, the level of activity of each of the multiple second users Us is less than the level of activity of each of the multiple first users Uf.

[0071] The first classification unit 115 classifies multiple first users Uf belonging to the first group GPf into multiple clusters CL based on user information UD corresponding to each of the multiple first users Uf. The user information UD is an example of "first attribute information" relating to the attributes of each of the multiple first users Uf. As described above, the user information UD as "first attribute information" has the items of "personality type ID" and "values ​​type ID". That is, the first attribute information includes information about the values ​​of each of the multiple first users Uf. The first attribute information also includes information about the personality of each of the multiple first users Uf.

[0072] Figure 8 shows an example of the operation of the first classification unit 115. The first classification unit 115 classifies multiple first users Uf belonging to the first group GPf into cluster CL[A], cluster CL[B], and cluster CL[C] based on the user information UD corresponding to each of the multiple first users Uf belonging to the first group GPf. Cluster CL[A] is an example of a "first cluster".

[0073] Figures 9 and 10 are explanatory diagrams of non-hierarchical clustering performed by the first classification unit 115 when classifying multiple first users Uf. In the vector space shown in the XY coordinates of Figure 9, nodes ND2 corresponding to the feature vectors of each of the multiple first users Uf are plotted. These feature vectors are vectors whose components are multiple items included in the user information UD shown in Figure 3. More specifically, these feature vectors are vectors whose components are two or more of the "date of birth," "place of residence," "occupation," "personality type ID," and "values ​​type ID" included in the user information UD. Note that the vector space shown in Figure 9 is a two-dimensional space shown in XY coordinates for the sake of explanation. However, this vector space is a space whose degree is the number of components included in the feature vector.

[0074] The first classification unit 115 inputs the coordinates corresponding to each of the first users Uf shown in Figure 9 into the learning model LM, and performs a non-hierarchical clustering method, such as K-means or DBSCAN, as an example.

[0075] Figure 10 shows an example of the classification results of multiple first users Uf into multiple clusters CL, output from the learning model LM. As shown in Figure 10, the multiple first users Uf are classified into clusters CL[A], cluster CL[B], and cluster CL[C] based on their proximity in the vector space. In the vector space represented by XY coordinates, cluster CL[A] has region RM[A], cluster CL[B] has region RM[B], and cluster CL[C] has region RM[C].

[0076] The second classification unit 116 classifies multiple second users Us into multiple cluster CLs based on the user information UD corresponding to each of the multiple second users Us belonging to the second group GPs and the characteristics of the cluster CL classified by the first classification unit 115. The user information UD is an example of "second attribute information" relating to the attributes of each of the multiple second users Us. As described above, the user information UD as "second attribute information" has the items of "personality type ID" and "values ​​type ID". That is, the second attribute information includes information about the values ​​of each of the multiple second users Us. The second attribute information also includes information about the personality of each of the multiple second users Us.

[0077] Figure 11 shows an example of the operation of the second classification unit 116. The second classification unit 116 classifies multiple second users Us into cluster CL[a], cluster CL[b], and cluster CL[c] based on the user information UD corresponding to each of the multiple second users Us belonging to the second group GPs and the characteristics of cluster CL[A], cluster CL[B], and cluster CL[C] classified by the first classification unit 115. For the sake of explanation, cluster CL[a] to which multiple second users Us are classified is described using a different symbol than cluster CL[A] to which multiple first users Uf are classified, but cluster CL[a] and cluster CL[A] are the same cluster. Similarly, cluster CL[b] and cluster CL[B] are the same cluster. Cluster CL[c] and cluster CL[C] are the same cluster.

[0078] Figures 12 and 13 are explanatory diagrams illustrating how the second classification unit 116 classifies multiple second users Us. In the vector space shown by the XY coordinates in Figure 12, nodes ND2 corresponding to the feature vectors of each of the multiple second users Us are plotted. These feature vectors, like the feature vectors of each of the multiple first users Uf in Figure 9, are vectors whose components are multiple items included in the user information UD shown in Figure 3.

[0079] As shown in Figure 13, the second classification unit 116 divides the node ND2 corresponding to the feature vector of each of the multiple second users Us into the region RM[A] of cluster CL[A], the region RM[B] of cluster CL[B], and the region RM[C] of cluster CL[C]. Furthermore, in the vector space shown in the XY coordinates, the second classification unit 116 classifies the second users Us corresponding to node ND2 included in region RM[A] into cluster CL[a] = cluster CL[A]. Also, the second classification unit 116 classifies the second users Us corresponding to node ND2 included in region RM[B] into cluster CL[b] = cluster CL[B]. Also, the second classification unit 116 classifies the second users Us corresponding to node ND2 included in region RM[C] into cluster CL[c] = cluster CL[C]. Here, region RM[A] is an example of the characteristics of cluster CL[A]. Region RM[B] is an example of the characteristics of cluster CL[B]. Region RM[C] is an example of the characteristics of cluster CL[C].

[0080] In Figure 2, the extraction unit 117 extracts one or more activities from the information on the activities of each of the multiple users U that are included in the activities of one or more first users Uf and not included in the activities of one or more second users Us. Here, the information on the activities of each of the multiple users U is, for example, the "first information" described above. Also, the one or more activities extracted by the extraction unit 117 is an example of the "first activity".

[0081] Furthermore, information regarding the activities of each of the multiple users U is presented in chronological order, showing the content of each of the activities of the respective users U. Specifically, since the first activity information AD1, the second activity information AD2, and the third activity information AD3 corresponding to user U[k] all have an "execution date and time" item, as shown in Figures 4, 6, and 7, the information regarding user U[k]'s activities is presented in chronological order.

[0082] Figure 14 shows an example of the operation of the extraction unit 117. In Figure 14, it is assumed that one or more first users Uf included in cluster CL[A] have so far performed activities P, Q, and R in chronological order. Also, it is assumed that one or more second users Us included in cluster CL[a] have so far performed activity P. The extraction unit 117 extracts activities Q and R as one or more activities that are included in the activities of one or more first users Uf but not in the activities of one or more second users Us.

[0083] In Figure 2, the recommendation unit 118 recommends the activities of one or more first users Uf belonging to the first group GPf and cluster CL[A] to one or more second users Us belonging to the second group GPs and cluster CL[a] = cluster CL[A]. Similarly, the recommendation unit 118 recommends the activities of one or more first users Uf belonging to the first group GPf and cluster CL[B] to one or more second users Us belonging to the second group GPs and cluster CL[b] = cluster CL[B]. Similarly, the recommendation unit 118 recommends the activities of one or more first users Uf belonging to the first group GPf and cluster CL[C] to one or more second users Us belonging to the second group GPs and cluster CL[c] = cluster CL[C].

[0084] In other words, the recommendation unit 118 determines the activities of one or more first users Uf belonging to the first group GPf and cluster CL[A] as activities recommended for one or more second users Us belonging to the second group GPs and cluster CL[a] = cluster CL[A]. Similarly, the recommendation unit 118 determines the activities of one or more first users Uf belonging to the first group GPf and cluster CL[B] as activities recommended for one or more second users Us belonging to the second group GPs and cluster CL[b] = cluster CL[B]. Similarly, the recommendation unit 118 determines the activities of one or more first users Uf belonging to the first group GPf and cluster CL[C] as activities recommended for one or more second users Us belonging to the second group GPs and cluster CL[c] = cluster CL[C].

[0085] As an example, the recommendation unit 118 recommends at least one activity from among the one or more activities of one or more first users Uf belonging to cluster CL[A], which have been extracted by the extraction unit 117, to one or more second users Us belonging to cluster CL[a] = cluster CL[A]. Similarly, the recommendation unit 118 recommends at least one activity from among the one or more activities of one or more first users Uf belonging to cluster CL[B], which have been extracted by the extraction unit 117, to one or more second users Us belonging to cluster CL[b] = cluster CL[B]. Similarly, the recommendation unit 118 recommends at least one activity from among the one or more activities of one or more first users Uf belonging to cluster CL[C], which have been extracted by the extraction unit 117, to one or more second users Us belonging to cluster CL[c] = cluster CL[C].

[0086] If the above-mentioned "at least one activity" refers to two activities, the recommendation unit 118 preferably recommends these two activities to at least one second user Us in the order indicated by the information regarding the activities of the first user Uf.

[0087] In the example shown in Figure 14, the extraction unit 117 extracts activity Q and activity R as one or more activities that are included in the activities of one or more first users Uf classified in cluster CL[A], but are not included in the activities of one or more second users Us classified in cluster CL[a] = cluster CL[A]. The recommendation unit 118 recommends at least one of activity Q and activity R to one or more second users Us belonging to cluster CL[a] = cluster CL[A].

[0088] When the recommendation unit 118 recommends both activity Q and activity R to one or more second users Us belonging to cluster CL[a] = cluster CL[A], it is preferable to recommend activity Q first, followed by activity R, in the order indicated by the information regarding the activities of the first user Uf.

[0089] In Figure 2, the display control unit 119 causes each of the one or more second users Us to display on their respective terminals 20 information, which the recommendation unit 118 has determined to be information that represents the activities of one or more first users Uf that are recommended to the second user Us.

[0090] Figure 15 shows an example of Stage ST to which User U, a fan of a celebrity, belongs in a celebrity fan club using the Activity Recommendation System 1. In Figure 15, the horizontal axis represents the degree of self-centeredness or other-centeredness. More specifically, moving to the left on the horizontal axis towards Figure 15 indicates self-centeredness, and moving to the right indicates other-centeredness. Also, in Figure 15, the vertical axis represents the degree of activity level. More specifically, moving upwards on the vertical axis towards Figure 15 indicates low activity level, and moving downwards indicates high activity level.

[0091] Furthermore, in Figure 15, stages ST1 to ST6 are illustrated as an example. Stages ST1 and ST2 each have lower activity levels than stages ST3 and ST4 each. On the other hand, stages ST5 and ST6 each have higher activity levels than stages ST3 and ST4 each. Also, stage ST1 is more self-centered than stage ST2, and stage ST2 is more other-centered than stage ST1. Stage ST3 is more self-centered than stage ST4, and stage ST4 is more other-centered than stage ST3. Stage ST5 is more self-centered than stage ST6, and stage ST6 is more other-centered than stage ST5.

[0092] Stage ST1, for example, represents the "Exploration" stage. Specifically, Stage ST1 indicates that User U is exploring what actions to take after joining a fan club. Stage ST2, for example, represents the "Admiration" stage. Specifically, Stage ST2 indicates that User U's admiration for the celebrity is growing. Stage ST3, for example, represents the "Sharing" stage. Specifically, Stage ST3 indicates that User U is sharing messages related to the celebrity. Stage ST4, for example, represents the "Support" stage. Specifically, Stage ST4 indicates that User U is supporting the celebrity. Stage ST5, for example, represents the "Solidarity" stage. Specifically, Stage ST5 indicates that User U is showing solidarity with other fans. Stage ST6, for example, represents the "Own" stage. Specifically, Stage ST6 indicates that User U owns merchandise related to the celebrity.

[0093] Here, let's assume that the first user Uf has moved from stage ST1 to stage ST5. Also, let's assume that the second user Us has moved from stage ST1 to stage ST3.

[0094] The activity recommendation device 10 recommends to the second user Us, who is located in stage ST3, the actions of the first user Uf, who is located in stage ST5, in order to move the second user Us, who is located in stage ST3, to stage ST5.

[0095] 2: The operation diagram 16 of the embodiment is a flowchart showing an example of the operation of the activity recommendation device 10.

[0096] In step S1, the processing unit 11 of the activity recommendation device 10 functions as a first classification unit 115. Based on first attribute information relating to the attributes of each of the multiple first users Uf belonging to the first group GPf from among the multiple users U, the processing unit 11 classifies the multiple first users Uf into multiple cluster CLs, including cluster CL [A].

[0097] In step S2, the processing unit 11 functions as a second classification unit 116. The processing unit 11 classifies the multiple second users Us into the multiple cluster CLs based on the second attribute information relating to the attributes of each of the multiple second users Us belonging to the second group GPs, and the characteristics of each cluster CL classified in step S1. The degree of activity of each of the multiple second users Us is less than the degree of activity of each of the multiple first users Uf.

[0098] In step S3, the processing unit 11 functions as a recommendation unit 118. The processing unit 11 recommends the activities of one or more first users Uf belonging to cluster CL[A] to one or more second users Us belonging to cluster CL[A].

[0099] 3: Effects of the Embodiment The activity recommendation device 10 according to this embodiment comprises a first classification unit 115, a second classification unit 116, and a recommendation unit 118. The first classification unit 115 classifies a plurality of first users Uf belonging to a first group GPf from among a plurality of users U into a plurality of cluster CLs, including cluster CL [A] as a first cluster, based on first attribute information relating to the attributes of each of the plurality of first users Uf belonging to a first group GPf from among a plurality of users U. The second classification unit 116 classifies a plurality of second users Us belonging to a second group GPs from among a plurality of users U into the above plurality of cluster CLs based on second attribute information relating to the attributes of each of the plurality of second users Us belonging to a second group GPs from among a plurality of users U, and the characteristics of each cluster CL classified by the first classification unit 115. The recommendation unit 118 recommends the activities of one or more first users Uf belonging to cluster CL [A] to one or more second users Us belonging to cluster CL [A]. The level of activity of each of the multiple second users Us is less than the level of activity of each of the multiple first users Uf.

[0100] The activity recommendation device 10 according to this embodiment, having the above configuration, can recommend appropriate activities to a specific user U, taking into account the degree of activity among users U.

[0101] More specifically, the activity recommendation device 10 classifies first users Uf, who have a high level of activity, into multiple cluster CLs based on first attribute information, and classifies second users Us, who have a low level of activity, into multiple cluster CLs similar to those of first users Uf, based on second attribute information. This allows the activity of first users Uf to be recommended to second users Us, who share attributes with first users Uf. As a result, the activity recommendation device 10 can more effectively determine which activities to recommend to second users Us in order to increase the overall activity level of multiple users U.

[0102] Furthermore, in the activity recommendation device 10 according to this embodiment, the first classification unit 115 performs non-hierarchical clustering on nodes ND2 corresponding to the preceding plurality of first users Uf, which are located in the vector space at positions corresponding to feature vectors indicated by the first attribute information, thereby classifying the plurality of first users Uf into a plurality of cluster CLs, including cluster CL[A] as the first cluster.

[0103] The activity recommendation device 10 according to this embodiment, having the above configuration, can classify multiple first users Uf into multiple cluster CLs, including cluster CL[A] as the first cluster, even if the number of first users Uf becomes enormous.

[0104] Furthermore, the activity recommendation device 10 according to this embodiment further comprises a group classification unit 114. The group classification unit 114 classifies the multiple users U into a plurality of first users Uf and a plurality of second users Us based on first information relating to the activities of each of the plurality of users U.

[0105] The activity recommendation device 10 according to this embodiment, having the above configuration, can, for example, classify multiple users U into multiple first users Uf with high activity levels and multiple second users Us with low activity levels, based on the activity levels of each of the multiple users U.

[0106] Furthermore, the activity recommendation device 10 according to this embodiment further comprises an extraction unit 117. The extraction unit 117 extracts one or more first activities from first information relating to the activities of each of a plurality of users U, which are included in the activities of one or more first users Uf and not included in the activities of one or more second users Us. The recommendation unit 118 recommends at least one of the extracted first activities to at least one second user Us among the one or more second users.

[0107] The activity recommendation device 10 according to this embodiment, having the above configuration, can recommend to the second user Us an activity that the first user Uf is performing but the second user Us has not yet performed.

[0108] Furthermore, in the activity recommendation device 10 according to this embodiment, the first information described above shows the content of each activity of a plurality of users U in chronological order. The at least one first activity described above is two first activities. The recommendation unit 118 recommends the two first activities to at least one second user Us in the order shown by the first information.

[0109] The activity recommendation device 10 according to this embodiment, having the above configuration, allows the second user Us to perform multiple activities in the same order as the first user Uf. As a result, the activity recommendation device 10 can more effectively recommend the activities of the first user Uf to the second user Us in order to increase the overall activity level of the multiple users U.

[0110] Furthermore, in the activity recommendation device 10 according to this embodiment, each activity of a plurality of users U includes communication between one of the plurality of users U and the other users U.

[0111] The activity recommendation device 10 according to this embodiment, by having the above configuration, can increase the amount of communication between multiple users U.

[0112] Furthermore, in the activity recommendation device 10 according to this embodiment, each of the activities of a plurality of users U includes the purchase of goods or services.

[0113] The activity recommendation device 10 according to this embodiment, by having the above configuration, is capable of increasing the purchase amount of each product or service for multiple users U.

[0114] Furthermore, in the activity recommendation device 10 according to this embodiment, the first attribute information includes information about the values ​​of each of the multiple first users Uf. Furthermore, the second attribute information includes information about the values ​​of each of the multiple second users Us.

[0115] The activity recommendation device 10 according to this embodiment, having the above configuration, can classify multiple first users Uf based on the values ​​of the first users Uf.

[0116] Furthermore, in the activity recommendation device 10 according to this embodiment, the first attribute information includes information about the characteristics of each of the multiple first users Uf. Furthermore, the second attribute information includes information about the characteristics of each of the multiple second users Us.

[0117] The activity recommendation device 10 according to this embodiment, having the above configuration, can classify a plurality of first users Uf based on the characteristics of the first users Uf.

[0118] The activity recommendation system 1 according to this embodiment comprises a plurality of terminals 20 corresponding one-to-one with a plurality of users U, and an activity recommendation device 10. The activity recommendation device 10 comprises a first classification unit 115, a second classification unit 116, a recommendation unit 118, and a display control unit 119. The first classification unit 115 classifies a plurality of first users Uf belonging to a first group GPf from among the plurality of users U into a plurality of cluster CLs, including cluster CL [A] as a first cluster, based on first attribute information relating to the attributes of each of the plurality of first users Uf belonging to a first group GPf from among the plurality of users U. The second classification unit 116 classifies a plurality of second users Us belonging to a second group GPs from among the plurality of users U into the plurality of cluster CLs based on second attribute information relating to the attributes of each of the plurality of second users Us belonging to a second group GPs from among the plurality of users U, and the characteristics of each cluster CL classified by the first classification unit 115. The recommendation unit 118 recommends the activities of one or more first users Uf belonging to cluster CL[A] to one or more second users Us belonging to cluster CL[A]. The display control unit 119 causes each of the one or more second users Us to display information representing the activities of the one or more first users Uf on their respective terminals 20. The level of activity of each of the multiple second users Us is less than the level of activity of each of the multiple first users Uf.

[0119] The activity recommendation system 1 according to this embodiment, having the above configuration, can recommend appropriate activities to a specific user U, taking into account the degree of activity among users U.

[0120] More specifically, the activity recommendation system 1 classifies first users Uf, who have a high level of activity, into multiple cluster CLs based on first attribute information, and second users Us, who have a low level of activity, into multiple cluster CLs similar to those of first users Uf, based on second attribute information. This allows the system to recommend the activities of first users Uf to second users Us, who share attributes with first users Uf. As a result, the activity recommendation system 1 can more effectively determine which activities to recommend to second users Us in order to increase the overall activity level of multiple users U.

[0121] 4. Modifications The present disclosure is not limited to the embodiments illustrated above. Specific examples of modifications are given below.

[0122] 4-1: Modification 1 In the above embodiment, the calculation unit 113 extracted first activity information AD1 from the first activity information database ADB1, indicating that the "main user ID" is user U[k]. The calculation unit 113 also converted the "data amount" contained in the extracted first activity information AD1 into an "activity level index" using a pre-set conversion formula. Furthermore, the calculation unit 113 calculated the sum of all "activity level indices" contained in the extracted first activity information AD1. That is, the calculation unit 113 calculated the sum of "activity level indices" indicating the amount of communication activity of user U[k] based on the first activity information database ADB1.

[0123] However, the calculation unit 113 may first generate the network diagram NF shown in Figure 5 using the information stored in the first activity information database ADB1. Subsequently, the calculation unit 113 may calculate the sum of "activity levels indices" that indicate the amount of communication activity of user U[k] based on the number and thickness of edges ED connected to each node ND1 in the network diagram NF.

[0124] Alternatively, if the acquisition unit 112 acquires information showing the network diagram NF shown in Figure 5 from an external device of the activity recommendation device 10, the calculation unit 113 may calculate the sum of "activity levels indices" indicating the amount of communication activity of user U[k] based on the number and thickness of edge EDs connected to each node ND1 in the network diagram NF acquired from the external device.

[0125] The display control unit 119 may also cause the terminal 20 to display the network diagram NF.

[0126] 5. Other (1) In the embodiments described above, ROM and RAM were given as examples for the storage device 12 and storage device 22, but other suitable storage media include flexible disks, magneto-optical disks (e.g., compact disks, digital multipurpose disks, Blu-ray® disks), smart cards, flash memory devices (e.g., cards, sticks, key drives), CD-ROMs (Compact Disc-ROMs), registers, removable disks, hard disks, floppy® disks, magnetic strips, databases, servers, and other appropriate storage media.

[0127] (2) In the embodiments described above, the information, signals, etc. may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0128] (3) In the embodiments described above, the input and output information may be stored in a specific location (e.g., memory) or managed using a management table. The input and output information may be overwritten, updated, or appended to. The output information may be deleted. The input information may be transmitted to other devices.

[0129] (4) In the embodiments described above, the determination may be made by a value represented using one bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, a comparison with a predetermined value).

[0130] (5) The processing procedures, sequences, flowcharts, etc., exemplified in the embodiments described above may be rearranged in order, as long as there is no contradiction. For example, the methods described in this disclosure present various step elements using an exemplary order and are not limited to the specific order presented.

[0131] (6) Each function illustrated in Figures 1 to 16 is realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each function block is not particularly limited. That is, each function block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired or wireless connections). A function block may also be realized by combining the above one device or the above multiple devices with software.

[0132] (7) The programs illustrated in the embodiments described above should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, etc., whether they are called software, firmware, middleware, microcode, hardware description languages ​​or by other names.

[0133] Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technology (such as coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL)) and wireless technology (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0134] (8) In each of the above-mentioned forms, the terms “system” and “network” shall be used interchangeably.

[0135] (9) The information, parameters, etc. described in this disclosure may be expressed using absolute values, relative values ​​from a given value, or other corresponding information.

[0136] (10) In the embodiments described above, the terminal 20 may be a mobile station (MS). A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or several other appropriate terms. In this disclosure, terms such as “mobile station,” “user terminal,” “user equipment (UE),” and “terminal” may be used interchangeably.

[0137] (11) In the embodiments described above, the terms “connected,” “coupled,” or any variation thereof, mean any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” with each other. The coupling or connection between elements may be a physical coupling or connection, a logical coupling or connection, or a combination thereof. For example, “connection” may be reinterpreted as “access.” As used in this disclosure, two elements may be considered to be “connected” or “coupled” with each other using at least one of one or more wires, cables and printed electrical connections, and, in some non-limiting and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain and optical (both visible and invisible) domain.

[0138] (12) In the embodiments described above, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on".

[0139] (13) The terms “determining” and “determining” as used in this disclosure may encompass a wide variety of actions. “Determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, or inquiring (e.g., searching in a table, database, or other data structure), or ascertaining. “Determining” may also include receiving (e.g., receiving information), transmitting (e.g., sending information), inputting, outputting, or accessing (e.g., accessing data in memory). Furthermore, "judgment" and "decision" can include considering something as having been "judged" or "decided" after resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment" and "decision" can include considering something as having been "judged" or "decided" after some action. Also, "judgment (decision)" can be reinterpreted as "assuming," "expecting," or "considering."

[0140] (14) In the embodiments described above, where “include,” “including,” and variations thereof are used, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to be exclusive OR.

[0141] (15) In the present disclosure, if articles are added by translation, such as a, an, and the in English, the present disclosure may include the fact that the noun following these articles is plural.

[0142] (16) In this disclosure, the term “A and B are different” may mean “A and B are different from each other.” The term may also mean “A and B are each different from C.” Terms such as “separate” and “combine” may be interpreted in the same way as “different.”

[0143] (17) Each aspect / embodiment described herein may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of the specified information (e.g., notification that "it is X") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification).

[0144] Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Accordingly, the descriptions in the present disclosure are illustrative and not restrictive in any way.

[0145] 1...Activity recommendation system, 10...Activity recommendation device, 11...Processing device, 12...Storage device, 13...Display device, 14...Input device, 15...Communication device, 20...Terminal, 111...Communication control unit, 112...Acquisition unit, 113...Calculation unit, 114...Group classification unit, 115...First classification unit, 116...Second classification unit, 117...Extraction unit, 118...Recommendation unit, 119...Display control unit, AD1...First activity information, AD2...Second activity information, AD3...Third activity information, ADB1...First activity information database, ADB2...Second activity information database, A DB3...Third Activity Information Database, C...Community, CL...Cluster, ED...Edge, GPf...First Group, GPs...Second Group, LM...Learning Model, ND1...Node, ND2...Node, NET...Network, NF...Network Diagram, P...Activity, PR1...Control Program, Q...Activity, R...Activity, RM...Domain, ST...Stage, U...User, UD...User Information, UDB...User Database, Ua...User, Ub...User, Uc...User, Ud...User, Uf...First User, Us...Second User

Claims

1. An activity recommendation device comprising: a first classification unit that classifies a plurality of first users into a plurality of clusters, including a first cluster, based on first attribute information relating to the attributes of each of a plurality of first users belonging to a first group from among a plurality of users; a second classification unit that classifies a plurality of second users into the plurality of clusters based on second attribute information relating to the attributes of each of a plurality of second users belonging to a second group from among the plurality of users, and the characteristics of each cluster classified by the first classification unit; and a recommendation unit that recommends the activities of one or more first users belonging to the first cluster to one or more second users belonging to the first cluster, wherein the degree of activity of each of the plurality of second users is less than the degree of activity of each of the plurality of first users.

2. The activity recommendation device according to claim 1, wherein the first classification unit performs non-hierarchical clustering on nodes corresponding to the plurality of first users, which are located in a vector space at positions corresponding to feature vectors indicated by the first attribute information, thereby classifying the plurality of first users into the plurality of clusters, including the first cluster.

3. The activity recommendation device according to claim 1, further comprising a group classification unit, wherein the group classification unit classifies the plurality of users into a plurality of first users and a plurality of second users based on first information relating to the activities of each of the plurality of users.

4. The activity recommendation device according to claim 1, further comprising an extraction unit, the extraction unit extracts one or more first activities from first information relating to the activities of each of the plurality of users, which are included in the activities of one or more first users but not included in the activities of one or more second users, and the recommendation unit recommends at least one of the extracted first activities to at least one of the one or more second users.

5. The activity recommendation device according to claim 3, wherein the first information shows the content of each of the plurality of users' activities in chronological order, the at least one first activity is two first activities, and the recommendation unit recommends the two first activities to the at least one second user in the order shown by the first information.

6. The activity recommendation device according to claim 1, wherein each of the activities of the plurality of users includes communication between one of the plurality of users and the other users.

7. The activity recommendation device according to claim 1, wherein each of the activities of the plurality of users includes the purchase of goods or services.

8. The activity recommendation device according to claim 1, wherein the first attribute information includes information relating to the values ​​of each of the plurality of first users, and the second attribute information includes information relating to the values ​​of each of the plurality of second users.

9. The activity recommendation device according to claim 1, wherein the first attribute information includes information relating to the personality of each of the plurality of first users, and the second attribute information includes information relating to the personality of each of the plurality of second users.

10. An activity recommendation system comprising: multiple terminals corresponding to multiple users on a one-to-one basis; and an activity recommendation device, wherein the activity recommendation device comprises: a first classification unit that classifies the multiple first users into multiple clusters, including a first cluster, based on first attribute information relating to the attributes of each of the multiple first users belonging to a first group among the multiple users; a second classification unit that classifies the multiple second users into the multiple clusters based on second attribute information relating to the attributes of each of the multiple second users belonging to a second group among the multiple users, and the characteristics of each cluster classified by the first classification unit; a recommendation unit that recommends the activities of one or more first users belonging to the first cluster to one or more second users belonging to the first cluster; and a display control unit that causes information representing the activities of one or more first users to be displayed on a terminal possessed by each of the one or more second users, wherein the degree of activity of each of the multiple second users is less than the degree of activity of each of the multiple first users.

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