Information processing apparatus, information processing method, and program

Abstract

To provide an information processing apparatus, an information processing method, and a program for realizing targeting in which social connections are considered.SOLUTION: In an information processing apparatus, a target expansion section 104 applies user features of seed users 1201 (target user group) set by a user feature setting section 103 to a relationship graph 1202 generated by a graph generation section 102, adds a user group with social connections to the seed users 1201 to the seed users 1201, and determines the result as expanded user group 1203.SELECTED DRAWING: Figure 12A

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and a program, and in particular to a technique for determining a user group to which an advertisement is to be delivered. [Background technology]

[0002] In recent years, in the distribution of advertisements via the Internet, a group of users to which the advertisements are to be distributed (targeting) is being selected. As a targeting technique, for example, the technique disclosed in Patent Document 1 is known. This document describes that even if there is no purchase history of the product corresponding to the advertisement, a group of consumers who are estimated to have purchased the product corresponding to the advertisement based on the purchase history of other products are determined as the target of the advertisement. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-097717 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the technology of Patent Document 1, advertisement delivery targets are determined based on a database that represents characteristics related to the consumption behavior of each consumer belonging to a plurality of pre-defined consumer groups. However, this technology does not take into account social connections between consumers that may affect the consumption behavior of consumers (users), and there is a problem in that effective targeting is not realized.

[0005] The present invention has been made in consideration of the above-mentioned problems, and aims to provide a technology for realizing targeting that takes social connections into account. [Means for solving the problem]

[0006] In order to solve the above problem, one aspect of an information processing device according to the present invention includes an acquisition means for acquiring factual characteristics of each of a plurality of users as user characteristics; a creation means for creating a relationship graph showing social relationships between the plurality of users based on the user characteristics; a target user setting means for setting one or more users among the plurality of users as a target user group; and a determination means for adding one or more users among the plurality of users who have social relationships with users included in the target user group to the target user group based on the relationship graph and the user characteristics of the target user group, and determining them as an extended user group.

[0007] The system further includes a prediction means for predicting, among the plurality of users, one or more users having user characteristics similar to those of the target user as a similar user group based on the user characteristics, and the determination means may add, among the plurality of users, one or more users who have social relationships with users included in the similar user group to the target user group and the similar user group based on the relationship graph and the user characteristics of the similar user group, and determine them as the extended user group.

[0008] The prediction means may predict the similar user group using a machine learning model.

[0009] In the relationship graph, each user is represented by a user node, and the creation means may connect each node with a link indicating that the nodes have the social relationship based on the factual information.

[0010] The creation means may connect pairs of user nodes having the same factual characteristics with explicit links, and may connect pairs of nodes that are not connected by the explicit links with implicit links based on multiple pairs of user nodes connected by the explicit links.

[0011] The creating means may determine the closeness of the connected pairs based on one or more factual information shared by the pairs.

[0012] The information processing device may further include a distribution means for distributing advertisements to the extended user group.

[0013] In order to solve the above problem, one aspect of the information processing method of the present invention includes an acquisition process for acquiring factual characteristics of each of a plurality of users as user characteristics; a creation process for creating a relationship graph showing social relationships between the plurality of users based on the user characteristics; a target user setting process for setting one or more users among the plurality of users as a target user group; and a determination process for adding one or more users among the plurality of users who have social relationships with users included in the target user group to the target user group based on the relationship graph and the user characteristics of the target user group, and determining them as an extended user group.

[0014] In order to solve the above problem, one aspect of the program of the present invention is an information processing program for causing a computer to execute information processing, the program causing the computer to execute processes including an acquisition process for acquiring factual characteristics of each of a plurality of users as user characteristics, a creation process for creating a relationship graph showing social relationships between the plurality of users based on the user characteristics, a target user setting process for setting one or more users among the plurality of users as a target user group, and a determination process for adding one or more users among the plurality of users who have social relationships with users included in the target user group to the target user group and determining them as an extended user group based on the relationship graph and the user characteristics of the target user group. Effect of the Invention

[0015] According to the present invention, targeting that takes social connections into account becomes possible. The above-mentioned objects, aspects, and advantages of the present invention, as well as objects, aspects, and advantages of the present invention not described above, will be understood by those skilled in the art from the following detailed description of the invention by referring to the accompanying drawings and the claims. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 shows an example of the configuration of an information processing system. [Diagram 2] FIG. 2 shows an example of a functional configuration of the information processing device 10 according to the embodiment. [Diagram 3] FIG. 3 shows a flowchart of the relationship graph creation process. [Figure 4A] FIG. 4A is a diagram for explaining an explicit link. [Figure 4B] FIG. 4B is a diagram for explaining an explicit link. [Figure 4C] FIG. 4C is a diagram for explaining an explicit link. [Figure 4D] FIG. 4D is a diagram for explaining an implicit link. [Figure 5A] FIG. 5A is a diagram for explaining the process of inferring the relationship between links. [Figure 5B] FIG. 5B shows a flow chart of an example process for grouping pairs into clusters. [Figure 6A] FIG. 6A shows a conceptual diagram of a score (closeness score) based on the closeness of the relationship for a user pair. [Figure 6B] FIG. 6B shows a schematic architecture of the score prediction model 112. [Figure 7] FIG. 7 shows a conceptual diagram of a relationship graph. [Figure 8A] FIG. 8A is a diagram for explaining the learning stage of the potential user prediction model 111. As shown in FIG. [Figure 8B] FIG. 8B is a diagram for explaining the prediction stage of the potential user prediction model 111. As shown in FIG. [Figure 9] FIG. 9 shows an example of the hardware configuration of the information processing device 10 and the user device 11. As shown in FIG. [Figure 10] FIG. 10 shows a flowchart of the first target expansion process executed by the information processing device 10. As shown in FIG. [Figure 11] FIG. 11 shows a flowchart of the second target expansion process executed by the information processing device 10. [Figure 12A] FIG. 12A shows a conceptual diagram of the first target expansion process. [Figure 12B] FIG. 12B shows a conceptual diagram of the second target expansion process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, with reference to the attached drawings, an embodiment for carrying out the present invention will be described in detail. Among the components disclosed below, those having the same functions are given the same reference numerals, and their description will be omitted. Note that the embodiment disclosed below is an example of a means for realizing the present invention, and should be appropriately modified or changed depending on the configuration of the device to which the present invention is applied and various conditions, and the present invention is not limited to the following embodiment. Furthermore, not all of the combinations of features described in the present embodiment are necessarily essential to the solution of the present invention.

[0018] [Functional configuration of information processing device] Fig. 1 shows an example of the configuration of an information processing system according to this embodiment. As an example, as shown in Fig. 1, this information processing system includes an information processing device 10 and multiple user devices 11-1 to 11-N (N>1) used by multiple arbitrary users 1 to N. In the following description, unless otherwise specified, user devices 11-1 to 11-N may be collectively referred to as user device 11. In the following description, the terms user device and user may be used synonymously.

[0019] The user device 11 is, for example, a device such as a smartphone or a tablet, and is configured to be able to communicate with the information processing device 10 via a public network such as LTE (Long Term Evolution) or a wireless communication network such as a wireless LAN (Local Area Network). The user device 11 has a display unit (display surface) such as a liquid crystal display, and each user can perform various operations using a GUI (Graphic User Interface) equipped on the liquid crystal display. The operations include various operations on content such as images displayed on the screen, such as tapping, sliding, and scrolling using a finger or a stylus. Note that user device 11 is not limited to the device shown in Fig. 1, and may be a device such as a desktop PC (Personal Computer) or a notebook PC. In this case, each user may perform operations using input devices such as a mouse and a keyboard. User device 11 may also be provided with a separate display screen.

[0020] The user device 11 can log in to a web service (internet-related service) provided from the information processing device 10 or from another device (not shown) via the information processing device 10, and can use the service. The web service can include an online mall, an online supermarket, or services related to communications, finance, real estate, sports, and travel, which are provided via the Internet. By using such a web service, the user device 11 can transmit information about the user of the user device 11 to the information processing device 10.

[0021] For example, the user device 11 can transmit to the information processing device 10 information on characteristics of the user device or the user, such as the IP (Internet Protocol) address of the user device 11, the user's address, and the user's name. In addition, the user device 11 performs positioning calculations based on signals received from GPS (Global Positioning System) satellites (not shown), generates the information obtained by the calculations as location information of the user device 11, and transmits it to the information processing device 10. The information processing device 10 acquires various information from the user device 11, and creates a relationship graph network (hereinafter, relationship graph) showing social relationships between users based on the information. Then, the information processing device 10 uses the created relationship graph to determine an extended user group having a social connection (having a social relationship) with one or more arbitrarily selected users (for example, one or more randomly selected users (seed users)) as targets for advertisement delivery. In other words, the information processing device 10 uses the created relationship graph to perform target expansion (extended targeting) based on the seed users.

[0022] [Functional configuration of information processing device 10] Information processing device 10 according to this embodiment first acquires various information from user devices 11-1 to 11-N, and creates a relationship graph indicating the social relationships between users 1 to N. Then, information processing device 10 performs target expansion using the relationship graph.

[0023] FIG. 2 shows an example of a functional configuration of the information processing device 10 according to the present embodiment. The information processing device 10 shown in Fig. 2 includes a user feature acquisition unit 101, a graph creation unit 102, a user feature setting unit 103, a target extension unit 104, a potential user prediction unit 105, a learning unit 106, an output unit 107, a learning model storage unit 110, and a user feature storage unit 120. The learning model storage unit 110 stores a potential user prediction model 111 and a score prediction model 112, which are machine learning models. The various learning models will be described later. In addition, the user feature storage unit 120 stores a user feature 121.

[0024] The user feature acquisition unit 101 acquires factual features (factual information) (hereinafter, user features) about each of the user devices 11-1 to 11-N. The user features are factual features (information) that are actually or objectively obtained from the user device or user. The user feature acquisition unit 101 can acquire the user features directly from the user device 11, for example. The user feature acquisition unit 101 can also acquire the user features as information registered in a predetermined web service by the user of the user device 11.

[0025] The user characteristics include the IP address of the user device, the user's address and name, the number of the credit card held by the user, and the user's demographic information (demographic user attributes such as gender, age, residential area, occupation, and family structure). The user characteristics may also include a registration number and a registered name when using a specified web service. The user characteristics may also include a call history, a delivery address other than the user's address for the product when using a specified web service, the usage status when using a specified web service, usage history, search history, and information on points that can be accumulated by using a service. In this way, the user characteristics can include any information, including information related to the user device or the user himself / herself, and information on the use of a specified service via communication. The user feature acquiring unit 101 stores the acquired user features in the user feature storage unit 120 as user features 121 .

[0026] The graph creation unit 102 creates a relationship graph by using the various user features acquired by the user feature acquisition unit 101. The relationship graph will be described later.

[0027] The user characteristic setting unit 103 sets an arbitrary target user group selected from the user devices 11-1 to 11-N as a seed user. The seed user may be one person. The seed user may be set by an input operation by an operator using an input unit (input unit 95 in FIG. 9), may be set in advance in the system, or may be set by an arbitrary program stored in a storage unit (ROM 92 or RAM 93 in FIG. 9). Furthermore, the user characteristic setting unit 103 acquires the user characteristics of the set seed user from the user characteristics 121 and sets them in the target extension unit 104 and the prospective user prediction unit 105. Furthermore, the user characteristic setting unit 103 acquires the user characteristics of a group of users other than the seed user from among the user devices 11-1 to 11-N from the user characteristics 121 and sets them in the prospective user prediction unit 105.

[0028] The target extension unit 104 performs target extension (determination of an extended user group) using social connections. In one embodiment, the target extension unit 104 performs target extension based on a seed user set by the user characteristic setting unit 103, using the relationship graph created by the graph creation unit 102. In another embodiment, the target extension unit 104 performs target extension based on a prospective user group (described later) predicted from the set seed user. The prospective user group is predicted by the prospective user prediction unit 105. The target extension process will be described later.

[0029] The prospective user prediction unit 105 predicts a group of users who are predicted to have the same (similar) user characteristics as the seed user set by the user characteristic setting unit 103 as a prospective user group (similar user group). In this embodiment, the prospective users are predicted using a prospective user prediction model 111 that has been learned by the learning unit 106. The prediction process of the prospective users will be described later. Note that the prediction process of the prospective user group is not limited to using the prospective user prediction model 111.

[0030] The learning unit 106 trains the prospective user prediction model 111 and the score prediction model 112, and stores the trained prospective user prediction model 111 and score prediction model 112 in the learning model storage unit 110. The learning process of each learning model will be described later.

[0031] The output unit 107 outputs the result of the target expansion performed by the target expansion unit 104 (that is, the information of the expanded user group). The output can be any output process, and can be output to an external device via the communication I / F (communication I / F 97 in FIG. 9), or can be displayed on the display unit (display unit 96 in FIG. 9).

[0032] [Procedure for creating a relational graph] Next, the procedure for creating the relational graph according to the present embodiment will be described. In the following description, users A to E are users referred to for the purpose of explanation and can be users of the user device 11. The relational graph is composed of the connections of each user node surrounded by circles in FIGS. 4A to 4D, and in the following description, the user node is simply referred to as a user. FIG. 3 shows a flowchart of the relational graph creation process executed by the graph creation unit 102 according to the present embodiment. Each step of the process in FIG. 3 will be described below.

[0033] <S31: Creation of links> In S31, the graph creation unit 102 predicts and creates links (links indicating having a social relationship) among a plurality of users. The link creation process will be described with reference to FIGS. 4A to 4D. FIGS. 4A to 4C are diagrams for explaining explicit links, and FIG. 4D is a diagram for explaining implicit links. An explicit link is a link created based on an explicit common feature between two users (user pair). An implicit link is a link created as an indirect relationship using the explicit links that have already been created, although the existence of an explicit common feature of the user pair is unclear. Thus, the links between users are identified as explicit links and implicit links.

[0034] Fig. 4A shows an example of creating an explicit link using the IP addresses of the users' user devices as a common feature. Fig. 4A shows an example in which there are an online mall 41, a golf course reservation service 42, a travel-related reservation service 43, and a card management system 44 as web services available to users A to C. Figs. 4A to 4C show these four web services, but the number of web services is not limited to a specific number.

[0035] The online mall 41 is a shopping mall available online (using the Internet). The online mall 41 can provide a wide variety of products and services, such as fashion, books, food, concert tickets, and real estate. The golf course reservation service 42 is operated as a website that provides online services related to golf courses, and can provide, for example, golf course search and reservation and lesson information. The travel-related reservation service 43 is operated as a website that provides various travel services available online. For example, the travel-related reservation service 43 can provide hotel and travel tour reservations, airline and rental car reservations, tourist information, and information about hotels and the surrounding areas. The card management system 44 is operated on a website that provides services related to credit cards issued and managed by a specific card management company. The card management system 44 may provide services in association with at least one of an online mall 41, a golf course reservation service 42, and a travel-related reservation service 43.

[0036] 4A, users A to C each use the same IP address (=198.45.66.xx) to access an online mall 41, a golf course reservation service 42, and a travel-related reservation service 43. The IP address information can be acquired by the user characteristic acquisition unit 101. In such a case, the graph creation unit 102 creates explicit links (for example, link L1 between user A and user C) between users A to C with the same IP address characteristic as shown in link state 45.

[0037] Fig. 4B shows an example of creating an explicit link using the features of users' addresses as common features. Like Fig. 4A, Fig. 4B shows an example in which online mall 41, golf course reservation service 42, travel-related reservation service 43, and card management system 44 exist as web services available to users A to C. Here, users A to C each register the same address (delivery address) and use online mall 41, golf course reservation service 42, and travel-related reservation service 43. Address information can be acquired by user feature acquisition unit 101. In such a case, the graph creation unit 102 creates explicit links (for example, link L1 between user A and user C) between users A to C with the same address characteristics, as shown in link state 46.

[0038] 4C shows an example of creating an explicit link using the characteristics of the credit card numbers used by users as a common characteristic. Similar to FIG. 4A, FIG. 4B shows an example in which online mall 41, golf course reservation service 42, travel-related reservation service 43, and card management system 44 exist as web services available to users A to C. Here, users A to C each register the same credit card and use online mall 41, golf course reservation service 42, and travel-related reservation service 43. Information including the credit card numbers can be acquired by user characteristic acquisition unit 101. In such a case, the graph creating unit 102 creates explicit links (for example, link L1 between user A and user C) between users A to C using the same card characteristics, as shown in link state 47.

[0039] FIG. 4D shows an example of creating an implicit link between users. In the example of FIG. 4D, for user A, users C, D, and E are connected by explicit links, and for user B, users C, D, and E are connected by explicit links. Such link features (features indicating the relationship between links) are embedded into a common feature space, and an inferred link in which an implicit relationship is constructed between each user (each node) is created (established) as an implicit link. In the example of FIG. 4D, although user A and user B are not connected by an explicit link, as a result of being inferred to have a relationship in the common feature space, an implicit link L2 is created. Note that the graph creation unit 102 predicts and creates an implicit link between users by performing learning (representation learning, relationship learning, embedding learning, knowledge graph embedding) of a user relationship graph composed of nodes (users) connected by explicit links. At this time, the graph creation unit 102 may perform the learning based on a known embedding model or an extension thereof as appropriate.

[0040] <S32: Inference of Relationship between Links> In S32, the graph creation unit 102 infers the relationship between the links predicted and created in S31. The inference process of the relationship between links will be described with reference to FIGS. 5A and 5B. FIG. 5A is a diagram for explaining the inference process of the relationship between links, and shows an example of inferring the relationship of a link between user A and user B connected by an explicit link.

[0041] The graph creation unit 102 treats pairs of users connected by the links created in S31 as data points, and groups the pairs (data points) into clusters that represent a common type using various information acquired by the user feature acquisition unit 101. The various information may be information such as IP address, address, credit card, age, gender, and friends. Each cluster may be a cluster having a relationship such as spouse, parent and child, neighbor, same household, colleague, friend, same-gender sibling, and different-gender sibling. In the example of FIG. 5A, user pairs are indicated by crosses, and the following clusters into which the pairs may be grouped are indicated: parent-child cluster 51, spouse cluster 52, same-gender sibling cluster 53, friend cluster 54, and colleague cluster. Note that, although five clusters are shown in FIG. 5A, the number of clusters is not limited to a specific number.

[0042] For example, if user A and user B have (share) the following characteristics 50: the same surname, an age difference of less than 10 years, opposite gender, and the same address, the graph creation unit 102 can group the pair of user A and user B into a cluster (spouse cluster 52) representing the relationship between husband and wife (spouse).

[0043] FIG. 5B shows a flowchart of an example of a process performed by the graph creation unit 102 for grouping pairs into clusters. At the start of S51, for pairs to be grouped, assume they have the characteristics of the same address and the same surname. In S52, the graph creation unit 102 determines whether the pair in question has the characteristic of the same gender. If the pair in question has the characteristic of the same gender (Yes in S52), in S53, the graph creation unit 102 determines whether the age difference of the pair in question is less than or equal to a predetermined threshold value (= X value). If the age difference of the pair in question is greater than the X value (No in S53), the graph creation unit 102 groups the pair in question into the parent - child cluster 51. If the age difference is less than or equal to the X value (Yes in S53), the graph creation unit 102 groups the pair in question into the same - gender sibling cluster 53. Also, if the pair in question does not have the characteristic of the same gender (No in S52), in S54, the graph creation unit 102 determines whether the age difference of the pair in question is less than or equal to a predetermined threshold value (= Y value). If the age difference is greater than the Y value (No in S54), the graph creation unit 102 groups the pair in question into the parent - child cluster 51. If the age difference is less than or equal to the Y value (Yes in S54), the graph creation unit 102 groups the pair in question into the spouse cluster 52.

[0044] <S33: Score Assignment Based on Degree of Relationship Intimacy> In S33, the graph creation unit 102 predicts a score based on the degree of relationship intimacy for the pair inferred in S32 and assigns the score to the pair. In this embodiment, the score is a numerical value between 0 and 1, but there is no specific limitation on the numerical values that the score can take. FIG. 6A shows a conceptual diagram of the score based on the degree of relationship intimacy for user pairs (hereinafter referred to as the intimacy score).

[0045] In the example of FIG. 6A, the intimacy of the relationship between the user pair changes depending on the characteristics owned (shared) by user A and user B who are connected by an explicit link. In the upper part of FIG. 6A, if user A and user B have characteristics 60, such as siblings of the same gender, the same address, 1200 call histories, and 50 gift exchanges, the intimacy of the relationship between the user pair (i.e., the intimacy score) is high. On the other hand, in the lower part of FIG. 6A, if user A and user B have characteristics 61, such as siblings of the same gender, different addresses, 30 call histories, and 2 gift exchanges, the intimacy of the relationship between the user pair (i.e., the intimacy score) is low. Thus, as in the example of FIG. 6A, even if user A and user B are siblings of the same gender, the intimacy of the relationship between the pair will differ depending on other characteristics shared by the user pair. It is observed that pairs with high intimacy of the relationship are close to each other in social distance and have high influence. On the other hand, pairs with low relationship intimacy are observed to have a large social distance from each other and are not close to each other.

[0046] In this embodiment, the intimacy score for a user pair is predicted using a score prediction model 112. Fig. 6B shows a schematic architecture of the score prediction model 112. The score prediction model 112 is a learning model that predicts an intimacy score 64 for a feature 63 of a user pair by inputting the feature 63.

[0047] The score prediction model 112 is, for example, a learning model that performs weak supervised learning, for example, a learning model using a convolutional neural network (CNN). In this embodiment, the score prediction model 112 is a learning model that is trained using intimacy scores (0 to 1) assigned to a plurality of features for a user pair as teacher data, as shown in FIG. 6A. For example, in the learning stage, combined data such as an intimacy score close to 1 set for feature 60 in FIG. 6A and an intimacy score close to 0 set for feature 61 are used as teacher data. The learning process is performed by the learning unit 108. Note that the score prediction model 112 may be different for each type of relationship between user pairs, and may be a learning model trained according to one type of relationship.

[0048] In this embodiment, the intimacy score for a user pair is predicted using the score prediction model 112, but the graph creation unit 102 may be configured to predict the score using another method.

[0049] Through the above process, explicit or implicit links are formed between multiple users, and intimacy scores are assigned between each link, and a relationship graph is created. A conceptual diagram of the relationship graph is shown in Figure 7. Each of users 71 to 73 has multiple characteristics, and a predicted intimacy score is assigned to each pair of users as described above.

[0050] [Prediction of potential users] Next, a process for predicting a prospective user according to this embodiment will be described. In this embodiment, the process for predicting a prospective user is performed using the prospective user prediction model 111. The process will be described separately for a learning stage and a prediction stage of the prospective user prediction model 111.

[0051] 8A shows a diagram for explaining the learning stage of prospective user prediction model 111. First, user characteristic setting unit 103 sets active users 81 consisting of a plurality of users. For example, user characteristic setting unit 103 sets a plurality of users who have used a predetermined web service during a certain period of time (e.g., six months) from among users of user devices 11-1 to 11-N shown in FIG. 1 as active users 81. Alternatively, user characteristic setting unit 103 may set a plurality of users, which are set in advance, from among users of user devices 11-1 to 11-N as active users 81.

[0052] After setting the active users 81, the user characteristic setting unit 103 extracts a plurality of positive users 82 and a plurality of negative users 83 from the active users 81. The positive users 82 are users who have purchased and / or used a given product or service through the web service and / or have positively evaluated the product or service through the web service. On the other hand, the negative users 83 are users other than the positive users 82 who are randomly extracted from the active users 81.

[0053] Next, the user feature setting unit 103 acquires the user features 84 of each of the positive user 82 and the negative user 83 from the user features 121 stored in the user feature storage unit 120. For example, the user features 84 include demographic information and purchase history (information on the genre and type of product, etc.) for the positive user 82 and the negative user 83 in the web service. The demographic information and purchase history each include a plurality of subdivided features. Note that the user features 84 are not limited to demographic information and purchase history, and may include other features such as point status (available points, etc.), point features (information on point transactions such as points acquired / used from online or offline shops, etc.). The learning unit 106 trains the prospective user prediction model 111 using the user features 84 and the category (label (correct answer data)) of a positive user or negative user corresponding to the user features.

[0054] The potential user prediction model 111 is, for example, a learning model based on XGBoost. In the learning stage, the learning unit 106 performs validation and tuning (adjustment) of hyperparameters (parameters that control the behavior of the potential user prediction model 111) by grid search and cross validation. Since XGBoost is a tree (decision tree) based model, the potential user prediction model 111 can generate a result (feature evaluation 85) that indicates how input data (user features) affect the model output. This makes it possible to validate, for example, which user features (combinations of subdivided features) positive users are more influenced by.

[0055] The trained potential user prediction model 111 is configured to represent the possibility that each user in an input arbitrary user group has the same user characteristics as a positive user. The possibility is expressed as a numerical value from 0 to 1, with the maximum possibility being 1. Here, for example, if the threshold is set to 0.5, the potential user prediction unit 105 can predict (determine) a user having a possibility larger than 0.5 as a potential user.

[0056] In the prediction (estimation) stage, a group of users who are predicted to have similar user characteristics to the seed users (corresponding to the positive users in the learning stage) are predicted as a group of potential users. Figure 8B shows a diagram for explaining the prediction stage of the group of potential users.

[0057] The prospective user prediction unit 105 inputs user characteristics 87 of the user group 86 other than the seed user set by the user characteristic setting unit 103 among the user devices 11-1 to 11-N into the trained prospective user prediction model 111 to predict the prospective user group 88. As described with reference to FIG. 8A, the user characteristics 87 include demographic information and a purchase history in the web service, but are not limited thereto. As described above, the prospective user prediction model 111 outputs a possibility (for example, a numerical value of 0 to 1) that each user in the user group 86 has the same user characteristics as the seed user. When the threshold value is set to 0.5, the prospective user prediction unit 105 can predict one or more users in the user group 86 who have a possibility higher than 0.5 as the prospective user group 88.

[0058] Next, the target expansion process using the above-mentioned relationship graph showing social connections according to this embodiment will be described. First, as a first target expansion process, a form using only the relationship graph will be described. Next, as a second target expansion process, a form using the potential user prediction model 111 and the relationship graph will be described.

[0059] [First target expansion process] 12A shows a conceptual diagram of the first target expansion process. The target expansion unit 104 applies the user characteristics of the seed user 1201 (target user group) set by the user characteristic setting unit 103 to the relationship graph 1202 created by the graph creation unit 102, and adds a user group having a social connection with the seed user 1201 to the seed user 1201 to determine the user group as the extended user group 1203. The user characteristics of the seed user 1201 may be information that can identify the seed user 1201 in the relationship graph, and may be, for example, the address, name, demographic information, etc. of the seed user 1201. The target expansion unit 104 may add a user who has a closeness score with the seed user 1201 higher than a predetermined threshold in the relationship graph to the seed user 1201 to determine the user group as the extended user group 1203.

[0060] [Second target expansion process] Next, the target expansion process using the aforementioned relationship graph showing social connections according to this embodiment will be described. FIG. 12B shows a conceptual diagram of the second target expansion process. The prospective user prediction unit 105 predicts a prospective user group 1204 from a seed user 1201 (target user group) set by the user characteristic setting unit 103, using the aforementioned prospective user prediction model 111. Specifically, the prospective user prediction unit 105 predicts, as the prospective user group 1204, a user group predicted to have the same user characteristics as the seed user 1201, among other user groups (not shown) other than the seed user.

[0061] Next, the target extension unit 104 applies the user characteristics of the prospective user group 1204 to the relationship graph 1202 created by the graph creation unit 102, and adds a user group having a social connection with the prospective user group 1204 to the seed user 1201 and the prospective user group 1204 to determine the user group as the extended user group 1205. The user characteristics of the prospective user group 1204 may be information that can identify the prospective user group 1204 in the relationship graph, and may be, for example, the address, name, demographic information, etc. of the prospective user group 1204. The target extension unit 104 may add a user who has a closeness score with the prospective user group 1204 that is higher than a predetermined threshold value in the relationship graph to the seed user 1201 and the prospective user group 1204 to determine the user group as the extended user group 1205.

[0062] [Hardware configuration of information processing device 10] FIG. 9 is a block diagram showing an example of a hardware configuration of the information processing device 10 according to the present embodiment. The information processing apparatus 10 according to the present embodiment can be implemented on any single or multiple computers, mobile devices, or any other processing platform. 9, the information processing device 10 is illustrated as being implemented in a single computer, but the information processing device 10 according to the present embodiment may be implemented in a computer system including multiple computers. The multiple computers may be connected to each other via a wired or wireless network so as to be able to communicate with each other.

[0063] 9, the information processing device 10 may include a CPU 91, a ROM 92, a RAM 93, a HDD 94, an input unit 95, a display unit 96, a communication I / F 97, and a system bus 98. The information processing device 10 may also include an external memory. A CPU (Central Processing Unit) 91 generally controls the operations of the information processing device 10, and controls each of the components (92 to 97) via a system bus 98, which is a data transmission path.

[0064] The ROM (Read Only Memory) 92 is a non-volatile memory that stores control programs and the like necessary for the CPU 91 to execute processing. Note that the programs may be stored in a non-volatile memory such as an HDD (Hard Disk Drive) 94 or an SSD (Solid State Drive) or in an external memory such as a removable storage medium (not shown). The RAM (Random Access Memory) 93 is a volatile memory and functions as a main memory, a work area, etc. of the CPU 91. That is, the CPU 91 loads necessary programs, etc. from the ROM 92 to the RAM 93 when executing processing, and realizes various functional operations by executing the programs, etc. The learning model storage unit 110 and the user feature storage unit 120 shown in FIG. 2 can be configured by the RAM 93.

[0065] The HDD 94 stores, for example, various data and various information required when the CPU 91 performs processing using a program. The HDD 94 also stores, for example, various data and various information obtained when the CPU 91 performs processing using a program. The input unit 95 is composed of a keyboard and a pointing device such as a mouse. The display unit 96 is configured with a monitor such as a liquid crystal display (LCD), etc. The display unit 96 may be configured in combination with the input unit 95 to function as a GUI (Graphical User Interface).

[0066] The communication I / F 97 is an interface that controls communication between the information processing device 10 and an external device. The communication I / F 97 provides an interface with a network and executes communication with an external device via the network. Various data, various parameters, etc. are transmitted and received between the external device and the communication I / F 97. In this embodiment, the communication I / F 97 may execute communication via a wired LAN (Local Area Network) or a dedicated line conforming to a communication standard such as Ethernet (registered trademark). However, the network available in this embodiment is not limited to this, and may be configured as a wireless network. This wireless network includes wireless PANs (Personal Area Networks) such as Bluetooth (registered trademark), ZigBee (registered trademark), and UWB (Ultra Wide Band). It also includes wireless LANs (Local Area Networks) such as Wi-Fi (Wireless Fidelity) (registered trademark), and wireless MANs (Metropolitan Area Networks) such as WiMAX (registered trademark). It also includes wireless WANs (Wide Area Networks) such as LTE / 3G, 4G, and 5G. Note that the network only needs to connect each device so that they can communicate with each other, and the communication standard, scale, and configuration are not limited to those described above.

[0067] At least some of the functions of each element of the information processing device 10 shown in Fig. 2 can be realized by the CPU 91 executing a program. However, at least some of the functions of each element of the information processing device 10 shown in Fig. 2 may be operated as dedicated hardware. In this case, the dedicated hardware operates under the control of the CPU 91.

[0068] [Hardware configuration of user device 11] The hardware configuration of user device 11 shown in Fig. 1 may be the same as that of Fig. 9. That is, user device 11 may include a CPU 91, a ROM 92, a RAM 93, a HDD 94, an input unit 95, a display unit 96, a communication I / F 97, and a system bus 98. User device 11 can display various information provided by information processing device 10 on the display unit 96, and perform processing corresponding to an input operation received from a user via a GUI (configured by the input unit 95 and the display unit 96).

[0069] [Processing flow] Fig. 10 shows a flowchart of the above-mentioned first target expansion process executed by the information processing device 10 according to this embodiment. The process shown in Fig. 10 can be realized by the CPU 91 of the information processing device 10 loading a program stored in the ROM 92 or the like into the RAM 93 and executing it. For the explanation of Fig. 10, the information processing system shown in Fig. 1 will be referred to. It is assumed that the score prediction model 112 learned by the learning unit 106 is stored in the learning model storage unit 110.

[0070] In S101, user characteristic acquisition unit 101 acquires user characteristics of each user from user devices 11-1 to 11-N, and stores the user characteristics in user characteristic storage unit 120 as user characteristics 121. The process of S101 may be a process of acquiring (collecting) user characteristics for a certain period of time in the past.

[0071] In S102, the graph creation unit 102 creates a relationship graph for users 1 to N, using the various user characteristics acquired by the user characteristic acquisition unit 101. The procedure for creating the relationship graph is as described above.

[0072] In S103, the user characteristic setting unit 103 sets an arbitrary group of target users from among users 1 to N as seed users. As described above, the seed users may be set by an operator performing an input operation using the input unit 95, may be set in advance in the system, or may be set by an arbitrary program stored in the ROM 92 or the RAM 93. Furthermore, in S103, the user characteristic setting unit 103 acquires the user characteristics of the seed users from the user characteristics 121 and sets them in the target extension unit 104.

[0073] In S104, the target extension unit 104 applies the user characteristics of the seed user set in S103 to the relationship graph created in S102, and adds a group of users having social connections with the seed user to the seed user to determine an extended user group for the seed user. The process of S104 corresponds to the process described with reference to FIG. 12A.

[0074] In S105, the output unit 107 outputs information on the extended user group determined in S104. The output unit 107 may generate various information related to the extended user group and output it to an external device (not shown). For example, the output unit 107 may deliver an advertisement created for the seed user to the extended user group.

[0075] 11 shows a flowchart of the above-mentioned second target expansion process executed by the information processing device 10 according to this embodiment. The process shown in FIG. 11 can be realized by the CPU 91 of the information processing device 10 loading a program stored in the ROM 92 or the like into the RAM 93 and executing it. As with FIG. 10, the information processing system shown in FIG. 1 will be referred to for the explanation of FIG. 11. It is assumed that the score prediction model 112 learned by the learning unit 106 is stored in the learning model storage unit 110.

[0076] The processes in S111 and S112 are similar to those in S101 and S102 in FIG. 10, and therefore will not be described. In S113, the user characteristic setting unit 103 sets an arbitrary target user group from among users 1 to N as seed users. As described above, the seed users may be set by an operator through an input operation using the input unit 95, may be set in advance in the system, or may be set by an arbitrary program stored in the ROM 92 or the RAM 93. Furthermore, in S113, the learning unit 106 sets the seed users as positive users and trains the prospective user prediction model 111. The learning process corresponds to the process described with reference to FIG. 8A.

[0077] In S114, the prospective user prediction unit 105 predicts a prospective user group from the seed user. Specifically, the prospective user prediction unit 105 inputs user features of a user group 86 other than the seed user into the prospective user prediction model 111 trained in S113 to predict the prospective user group. The prospective user prediction unit 105 may determine a user group obtained by adding the seed user to the predicted prospective user group as a final prospective user group. In S115 , the user characteristic setting unit 103 acquires the user characteristics of the prospective user group from the user characteristics 121 and sets them in the target extension unit 104 .

[0078] In S116, the target extension unit 104 applies the user characteristics of the potential user to the relationship graph created in S112, and adds a user group having social connections with the potential user group to the seed user and the potential user group, and determines the user group as an extended user group for the potential user group. The process of S116 corresponds to the process described with reference to FIG. 12B.

[0079] In S117, output unit 107 outputs information on the extended user group determined in S116. Output unit 107 may generate various information related to the extended user group and output it to an external device (not shown). For example, output unit 107 may deliver an advertisement created for the seed user to the extended user group.

[0080] In this way, the information processing device 10 is configured to determine a group of users who have social connections with an arbitrarily set seed user (one or more target users) as an extended user group. Since the extended user group has social connections with the seed user, such as parent-child connections, it is highly likely that advertising information delivered to the seed user will be shared among the extended user group. Therefore, the same advertising effect as that of an advertisement delivered to the seed user can be expected for the extended user group.

[0081] The information processing device 10 is also configured to estimate a prospective user group from the seed user, and determine a user group having social connections with the prospective user group as an extended user group. That is, users who are expected to behave similarly to the seed user in a web service (such as purchasing a product) are predicted as a prospective user group, and an extended user group having social connections with the prospective user group is determined. This makes it possible to specify a wider range of extended user groups as ad delivery destinations, and further improves the advertising effect.

[0082] According to the embodiment described above, it is possible to perform expanded targeting based on social connections from a small number of seed users, resulting in efficient and effective targeting.

[0083] Although specific embodiments have been described above, the embodiments are merely illustrative and are not intended to limit the scope of the present invention. The apparatus and method described in this specification can be embodied in forms other than those described above. Furthermore, the above-described embodiments can be appropriately omitted, substituted, and modified without departing from the scope of the present invention. Such omitted, substituted, and modified forms are included in the scope of the claims and their equivalents, and belong to the technical scope of the present invention. [Explanation of symbols]

[0084] 1 to N: users, 10: information processing device, 11-1 to 11-N: user devices, 101: user feature acquisition unit, 102: graph creation unit, 103: user feature setting unit, 104: target extension unit, 105: potential user prediction unit, 106: learning unit, 107: output unit, 110: learning model storage unit, 111: potential user prediction model, 112: score prediction model, 120: user feature storage unit, 121: user feature

Claims

1. An acquisition means for acquiring factual features of each of a plurality of users as user features; A generating means for generating a relationship graph showing social relationships between the plurality of users based on the user characteristics; A target user setting means for setting one or more users among the plurality of users as a target user group; a determination means for determining, based on the relationship graph and user characteristics of the target user group, one or more users among the plurality of users who have a social relationship with a user included in the target user group, as an extended user group by adding the user to the target user group; 13. An information processing device comprising:

2. The method further includes a prediction means for predicting, among the plurality of users, one or more users having user characteristics similar to those of users included in the target user group as a similar user group based on the user characteristics, The information processing device according to claim 1, characterized in that the determination means adds one or more users among the plurality of users who have social relationships with users of the similar user group to the target user group and the similar user group based on the relationship graph and the user characteristics of the similar user group, and determines them as the extended user group.

3. The information processing device according to claim 2 , wherein the prediction means predicts the similar user group using a machine learning model.

4. The information processing device according to any one of claims 1 to 3, characterized in that in the relationship graph, each user is represented by a user node, and the creation means connects each node with a link indicating that the node has the social relationship based on the factual information.

5. The information processing device according to claim 4, characterized in that the creation means connects pairs of user nodes having the same fact characteristics with explicit links, and connects pairs of nodes that are not connected by the explicit links with implicit links based on the pairs of user nodes connected by the explicit links.

6. 6. The information processing apparatus according to claim 4, wherein the creating means determines the intimacy of the connected pair based on one or more pieces of factual information shared by the pair.

7. 7. The information processing apparatus according to claim 1, further comprising a distribution unit that distributes advertisements to the extended user group.

8. An acquisition step of acquiring factual features of each of a plurality of users as user features; creating a relationship graph that indicates social relationships between the plurality of users based on the user characteristics; a target user setting step of setting one or more users among the plurality of users as a target user group; a determination step of adding one or more users among the plurality of users who have a social relationship with a user included in the target user group to the target user group based on the relationship graph and user characteristics of the target user group, and determining the user group as an extended user group; 13. An information processing method comprising:

9. An information processing program for causing a computer to execute information processing, the program comprising: An acquisition process of acquiring factual features of each of a plurality of users as user features; A process of creating a relationship graph showing social relationships between the plurality of users based on the user characteristics; A target user setting process for setting one or more users among the plurality of users as a target user group; and a determination process of adding one or more users among the plurality of users who have a social relationship with a user included in the target user group to the target user group based on the relationship graph and the user characteristics of the target user group, and determining the user as an extended user group. Information processing program.

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