Information processing device, information processing method and program

Abstract

To provide an information processing device, information processing method and program for improving effects of advertisements on community sites.SOLUTION: An information processing device 1 includes an acquisition unit 131 for acquiring behavioral information representing behavior of a first user, a specification unit 132 for specifying a second user representing behavior similar to the behavior of the first user, an evaluation unit 133 that inputs the behavioral information of the second user into a behavior learning model and causes the behavior learning model to output an estimated value representing a degree to which the user's behavior represented by the behavior information is estimated to be behavior of users belonging to a community, an extraction unit 134 that extracts the behavioral information of the second user to be displayed on an information terminal of the first user on the basis of the estimated value output by the evaluation unit, and a display control unit 135 that controls the information terminal of the first user to display the behavioral information extracted by the extraction unit and further controls the information terminal of the first user to display information for browsing information relating to a prescribed product in association with the behavioral information extracted by the extraction unit.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] Advertisements are displayed in social networking services (SNS). For example, an advertising system is known that encourages users to post on SNS that include advertisements (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-195026 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in conventional technology, the reward is given only when a user posts content specified by the advertiser, which creates the problem that such posts have less appeal to viewers.

[0005] The present invention has been made in consideration of these points, and aims to improve the effectiveness of advertisements on community sites. [Means for solving the problem]

[0006] An information processing device of a first aspect of the present invention has a memory unit that stores a behavioral learning model that has learned the behavioral information of each of a plurality of users participating in a specified community, the behavioral learning model being trained to input behavioral information indicating user behavior and output an estimated value indicating the degree to which the user behavior indicated by the input behavioral information is estimated to be the behavior of a user belonging to the community; an acquisition unit that acquires behavioral information indicating the behavior of a first user participating in the community; an evaluation unit that, triggered by the acquisition unit acquiring the behavioral information of the first user, inputs behavioral information of a second user different from the first user into the behavioral learning model and outputs an estimated value; an extraction unit that extracts behavioral information of the second user based on the estimated value output by the evaluation unit to determine information to be displayed on the information terminal of the first user; a determination unit that determines information to be displayed on the information terminal of the first user based on the content of the behavioral information extracted by the extraction unit; and a display control unit that controls the information determined by the determination unit to be displayed on the information terminal of the first user.

[0007] The memory unit may further store a general-purpose learning model, which is a pre-trained general-purpose language model, configured to input behavioral information and output estimated values, the behavioral learning model being a trained model obtained by fine-tuning the general-purpose learning model based on behavioral information of multiple users participating in the specified community, the evaluation unit may input the behavioral information into the general-purpose learning model and further output estimated values, and the extraction unit may extract behavioral information to be displayed on the information terminal of the first user based on the estimated values ​​that the evaluation unit has caused the behavioral learning model to output and the estimated values ​​that the evaluation unit has caused the general-purpose learning model to output.

[0008] The extraction unit may extract behavioral information to be displayed on the information terminal of the first user, where the magnitude relationship between the estimated value that the evaluation unit has output to the behavioral learning model and the estimated value that the evaluation unit has output to the general-purpose learning model satisfies a predetermined condition.

[0009] The extraction unit may convert each of the first user's behavioral information and the second user's behavioral information into a vector, calculate the distance between the vector corresponding to the second user's behavioral information and the vector corresponding to the first user's behavioral information, and extract behavioral information for which the calculated distance is greater than or equal to a predetermined threshold as behavioral information to be displayed on the first user's information terminal.

[0010] The display control unit may control the behavioral information extracted by the extraction unit to be displayed on the information terminal of the first user, and may also control the information determined by the determination unit to be associated with the behavioral information extracted by the extraction unit and displayed on the information terminal of the first user.

[0011] The behavioral information is text data indicating user comments in a community, and the display control unit may further display the behavioral information extracted by the extraction unit on the information terminal of the first user, and if the behavioral information extracted by the extraction unit includes a string indicating a specified product, may control information for viewing information about the specified product to be associated with the behavioral information extracted by the extraction unit and displayed on the information terminal of the first user.

[0012] The display control unit may further display the behavioral information extracted by the extraction unit on the information terminal of the first user, and if the behavioral information extracted by the extraction unit indicates behavior related to a specified product, may display information for viewing information about the specified product on the information terminal of the first user in association with the behavioral information extracted by the extraction unit, and the information processing device may further have a benefit granting unit that grants a specified benefit to the second user when the first user purchases the specified product via the information for viewing information about the specified product that the display control unit has displayed on the information terminal.

[0013] An information processing method of a second aspect of the present invention includes an acquisition step executed by a computer to acquire behavioral information indicating the behavior of a first user participating in a specified community; an evaluation step in which, triggered by the acquisition of the behavioral information of the first user in the acquisition step, a behavioral learning model that has learned the behavioral information of each of a plurality of users participating in the specified community and stored in a memory unit, the behavioral learning model being trained to take the behavioral information as input and output an estimated value indicating the degree to which the behavior of the user indicated by the input behavioral information is estimated to be the behavior of a user belonging to the community, inputs behavioral information of a second user different from the first user and outputs the estimated value; an extraction step in which, based on the estimated value output in the evaluation step, extracts behavioral information of the second user to determine information to be displayed on the information terminal of the first user; a determination step in which, based on the content of the behavioral information extracted in the extraction step, the information to be displayed on the information terminal of the first user; and a display control step in which the information determined in the determination step is further displayed on the information terminal of the first user.

[0014] A third aspect of the program of the present invention includes the following steps in a computer: an acquisition step of acquiring behavioral information indicating the behavior of a first user participating in a predetermined community; an evaluation step of inputting behavioral information of a second user different from the first user into a behavior learning model that has learned the behavioral information of each of a plurality of users participating in the predetermined community and that is stored in a memory unit when the behavioral information of the first user is acquired, the behavior learning model being trained to take the behavioral information as input and output an estimated value indicating the degree to which the behavior of the user indicated by the input behavioral information is estimated to be the behavior of a user belonging to the community; an extraction step of extracting behavioral information of the second user based on the estimated value output in the evaluation step to determine information to be displayed on the information terminal of the first user; and a determination step of determining information to be displayed on the information terminal of the first user based on the content of the behavioral information extracted in the extraction step. and a display control step of causing the information determined in the determination step to be further displayed on the information terminal of the first user.

[0015]

[0016] [Effects of the Invention]

[0017] According to the present invention, it is possible to improve the effectiveness of advertisements on community sites. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a diagram for explaining an overview of an information processing system S according to an embodiment. [Figure 2] 1 is a block diagram showing a configuration of an information processing device 1. FIG. [Figure 3] 3 is a diagram showing an example of a behavior information table stored in the storage unit 12. FIG. [Figure 4] FIG. 10 is a diagram illustrating an example of processing by an evaluation unit 133. [Figure 5] 10 is a diagram showing an example of a screen displayed by a display control unit 135. FIG. [Figure 6] 3 is a flowchart showing the flow of processing in the information processing device 1. DETAILED DESCRIPTION OF THE INVENTION

[0019] [Outline of Information Processing System S] FIG. 1 is a diagram illustrating an overview of an information processing system S according to an embodiment. The information processing system S is a system for managing a fan community (hereinafter, sometimes referred to as a "predetermined community"). A fan community is a community for users who are fans of a particular product, service, brand, hobby, person, organization, group, etc. to interact with each other. More specifically, a fan community is a social networking service (SNS) in which participating users post images, text, etc., and view, rate, and comment on other users' posts, thereby interacting with each other. As an example, the information processing system S includes an information processing device 1 and an information terminal 2.

[0020] The information processing device 1 is a device that manages fan communities. The information processing device 1 may manage each of a plurality of communities set up for each theme. The information processing device 1 accepts posts from users' information terminals 2 and displays information posted to the communities on the information terminals 2.

[0021] The information terminal 2 is a terminal used by a user. The information terminal 2 is a smartphone, a tablet, or a personal computer. The information terminal 2 transmits, for example, posts made by the user to a community to the information processing device 1, and displays information obtained from the information processing device 1.

[0022] The processing in the information processing system S will be described. The information processing device 1 acquires behavioral information of a first user. The behavioral information is information that indicates the user's behavior. Examples of the behavioral information include text posted by the user on an SNS, a product purchase history, or a log of a device used by the user. The first user is a user who is to determine the information to be displayed.

[0023] The information processing device 1 identifies a user who exhibits behavior similar to the behavior of the first user as a second user based on the acquired behavior information of the first user ((1) in FIG. 1). As an example, the information processing device 1 calculates the similarity between the behavior information of the first user and the behavior information of other users who belong to the community, and identifies the second user based on the calculated similarity.

[0024] The information processing device 1 extracts behavioral information to be displayed on the information terminal 2 of the first user from the behavioral information of the second user ((2) in Figure 1). The information processing device 1 inputs the behavioral information of the second user into the behavior learning model, causes it to output an estimated value, and extracts the behavioral information based on the estimated value output by the behavior learning model. The behavior learning model is a trained model that has learned the behavioral information of each of multiple users participating in a specified community. The behavior learning model is trained to input behavioral information and output an estimated value corresponding to the input behavioral information. The estimated value is a value that indicates the degree to which the behavior of the user indicated by the behavior information is estimated to be the behavior of a user belonging to the community. As an example, the information processing device 1 extracts behavioral information whose estimated value is equal to or greater than a specified threshold.

[0025] The information processing device 1 displays the extracted behavioral information on the information terminal 2 of the first user ((3) in FIG. 1). When the extracted behavioral information indicates behavior related to a predetermined product, the information processing device 1 further displays information for viewing information related to the predetermined product on the information terminal 2 of the first user. The predetermined product is, for example, a product or service related to a service, brand, hobby, person, organization, group, etc. that is targeted by a predetermined community. The information processing device 1 may display, instead of the information related to the product, information for viewing information related to a service, brand, store, character, person, organization, group, etc. that is related to the community. As an example, the information processing device 1 displays, on the information terminal 2 of the first user, a screen including a link for accessing information related to the predetermined product.

[0026] By configuring the information processing device 1 in this way, it is possible to improve the effectiveness of advertisements on the community site.

[0027] [Configuration of information processing device 1] 2 is a block diagram showing the configuration of the information processing device 1. The information processing device 1 has a communication unit 11, a storage unit 12, and a control unit 13. The control unit 13 has an acquisition unit 131, an identification unit 132, an evaluation unit 133, an extraction unit 134, a display control unit 135, a calculation unit 136, a determination unit 137, and a benefit granting unit 138.

[0028] The communication unit 11 is a communication interface for transmitting and receiving data to and from other devices via a network. The storage unit 12 is a storage medium including a ROM (Read Only Memory), a RAM (Random Access Memory), an SSD (Solid State Drive), a hard disk drive, etc. The storage unit 12 stores in advance a program to be executed by the control unit 13.

[0029] The memory unit 12 stores behavioral information indicating user behavior, for each of a plurality of users participating in a predetermined community. FIG. 3 is a diagram showing an example of the data structure of a behavioral information table stored in the memory unit 12. In the behavioral information table shown in FIG. 3, an "behavior ID," a "target community," a "posting user," and "behavior information" are associated with each other. The "behavior ID" is an ID (Identification) for identifying the behavioral information. The "target community" is an ID indicating the community to which the behavioral information was posted. The "posting user" indicates the user ID of the user who posted the behavioral information. The "behavioral information" is information such as text, images, and videos posted by users to the community. In the behavioral information table, an estimated value corresponding to the behavioral information may also be associated.

[0030] The storage unit 12 stores the behavioral learning model. The storage unit 12 may store user information that associates a user ID for identifying a user with information indicating a community to which the user belongs.

[0031] The control unit 13 is a processor such as a CPU (Central Processing Unit), etc. The control unit 13 executes the programs stored in the storage unit 12, thereby functioning as an acquisition unit 131, an identification unit 132, an evaluation unit 133, an extraction unit 134, a display control unit 135, a calculation unit 136, a determination unit 137, and a benefit granting unit 138.

[0032] The acquisition unit 131 acquires behavioral information indicating the behavior of a first user participating in a community. The behavioral information includes, for example, the user ID of the user who posts, a community ID indicating the community to which the post is to be made, and the content of the post. For example, the behavioral information is text data indicating the user's comments in the community. The acquisition unit 131 acquires the behavioral information of the user to be determined from the information terminal 2.

[0033] The identification unit 132 calculates the similarity between the behavioral information of the first user and the behavioral information of each of the multiple users stored in the storage unit 12, and identifies a second user who exhibits behavior similar to the behavior of the first user based on the calculated similarities. As an example, the identification unit 132 converts the behavioral information of the first user and the behavioral information of the multiple users into vectors. The identification unit 132 calculates the similarity between a vector based on the behavioral information of the first user and a vector based on the behavioral information of the second user. An example of the similarity is cosine similarity, but is not limited to this. As an example, the identification unit 132 identifies the user corresponding to the behavioral information with the largest calculated similarity as the second user.

[0034] A user whose behavioral information has a similarity equal to or greater than a predetermined threshold may be identified as the second user. That is, the identification unit 132 calculates the similarity between the behavioral information of the first user and the behavioral information of multiple users stored in the storage unit 12, and identifies a user whose behavioral information has a similarity equal to or greater than a predetermined threshold with respect to the behavioral information of the first user as the second user.

[0035] The evaluation unit 133 inputs the behavioral information of the second user into the behavioral learning model and outputs an estimated value. The evaluation unit 133 refers to the storage unit 12, acquires the behavioral information of the second user identified by the identification unit 132, inputs the acquired behavioral information into the behavioral learning model, and outputs an estimated value. As an example, the evaluation unit 133 refers to the storage unit 12, inputs one or more pieces of behavioral information associated with the second user into the behavioral learning model, and outputs an estimated value for each of the one or more pieces of behavioral information.

[0036] An example of the processing of the evaluation unit 133 will be described with reference to FIG. 4. As an example, the evaluation unit 133 decomposes the behavioral information A1 of the second user into tokens (T1 to T6). A token is the smallest unit processed as natural language, and is a character, a word, or a vector corresponding to the content indicated by the purchase history of a specific product or a device log. The evaluation unit 133 inputs the tokens generated by decomposition from the behavioral information into a behavioral learning model in an autoregressive manner, and outputs an estimated value. In this case, the estimated value is the conditional probability that the input token will be consecutive given the previously input token. As an example, the evaluation unit 133 outputs the sum of the conditional probabilities output by the behavioral learning model for each token as the estimated value.

[0037] The extraction unit 134 extracts behavioral information of the second user to be displayed on the information terminal of the first user based on the estimated value output by the evaluation unit 133. As an example, the extraction unit 134 extracts behavioral information having an estimated value equal to or greater than a threshold from among the behavioral information associated with the second user identified by the identification unit 132. The extraction unit 134 may extract behavioral information having the largest estimated value from among the behavioral information associated with the second user identified by the identification unit 132.

[0038] The display control unit 135 causes the behavioral information extracted by the extraction unit 134 to be displayed on the information terminal of the first user. When the behavioral information extracted by the extraction unit 134 indicates behavior related to a predetermined product, the display control unit 135 controls so that information for viewing information about the predetermined product is further displayed on the information terminal of the first user in association with the behavioral information extracted by the extraction unit 134. More specifically, when the behavioral information is text mentioning the predetermined product, the display control unit 135 displays information about the predetermined product. That is, the display control unit 135 causes the behavioral information extracted by the extraction unit 134 to be displayed on the information terminal of the first user, and when the behavioral information extracted by the extraction unit 134 includes a character string indicating the predetermined product, the display control unit 135 controls so that information for viewing information about the predetermined product is displayed on the information terminal of the first user in association with the behavioral information extracted by the extraction unit 134.

[0039] Fig. 5 is a diagram showing an example of a screen displayed by the display control unit 135. In the screen shown in Fig. 5, an object OB for viewing information about a product related to the behavioral information is displayed together with the behavioral information extracted by the extraction unit 134. A link for viewing information about the product is embedded in the object OB. When the user operates the information terminal 2 and presses the object OB displayed on the screen, a page showing information about the product is displayed on the information terminal 2.

[0040] As an example, the storage unit 12 stores product information that associates the name of a product with a link for viewing information about the product. The product information may further be associated with an ID that identifies the product. When the behavioral information includes a character string indicating a product, the display control unit 135 causes the information terminal 2 to display a screen that includes a link in the product information that corresponds to the character string indicating the product included in the behavioral information.

[0041] By configuring the information processing device 1 in this way, it is possible to improve the effectiveness of advertisements on the community site.

[0042] The information processing device 1 may be configured to determine the behavioral information to be extracted by comparing it with an estimated value output by a general-purpose learning model. In this case, the behavioral learning model is a trained model obtained by fine-tuning the general-purpose learning model based on behavioral information of multiple users participating in a predetermined community. The general-purpose learning model is a pre-trained general-purpose language model. The general-purpose learning model is trained to be able to perform natural language processing based on a large amount of data set. The general-purpose learning model is configured to input behavioral information and output an estimated value. The memory unit 12 stores the general-purpose learning model.

[0043] Compared to general-purpose learning models, behavioral learning models are trained using behavioral information of users belonging to specific communities as training data, and therefore differ from general-purpose learning models in that when behavioral information of a user belonging to a specific community is input, it is expected to output degree information indicating a higher likelihood that the user belongs to the specific community.

[0044] The evaluation unit 133 inputs the behavioral information into the general-purpose learning model and outputs an estimated value. The evaluation unit 133 inputs the behavioral information to be input into the behavioral learning model into the general-purpose learning model. The evaluation unit 133 inputs the behavioral information of the second user identified by the identification unit 132 into the general-purpose learning model and outputs an estimated value. The evaluation unit 133 refers to the memory unit 12, inputs one or more pieces of behavioral information associated with the second user into the general-purpose learning model, and outputs an estimated value for each of the one or more pieces of behavioral information.

[0045] The extraction unit 134 extracts behavioral information to be displayed on the information terminal of the first user based on the estimated value that the evaluation unit 133 has caused the behavioral learning model to output and the estimated value that the evaluation unit 133 has caused the general-purpose learning model to output. As an example, the extraction unit 134 extracts behavioral information in which the difference between the estimated value that the evaluation unit 133 has caused the behavioral learning model to output and the estimated value that the evaluation unit 133 has caused the general-purpose learning model to output is equal to or greater than a threshold.

[0046] The extraction unit 134 extracts behavioral information for display on the information terminal of the first user, where the magnitude relationship between the estimated value output by the evaluation unit 133 to the behavioral learning model and the estimated value output by the evaluation unit 133 to the general-purpose learning model satisfies a predetermined condition. As an example, the behavioral information may be extracted based on the ratio between the estimated value output by the general-purpose learning model and the estimated value output by the behavioral learning model. For example, the estimated value output by the general-purpose learning model is P0, the estimated value output by the behavioral learning model is P1, and the estimated value output by the behavioral learning model is P2. L In this case, the determination unit 133 determines that P L It is also possible to extract behavioral information in which / P0 is equal to or greater than a predetermined threshold value.

[0047] By configuring the information processing device 1 in this way, if the user's behavior has a high affinity with the behavior of a user who belongs to the community, the user can be notified of information about the community.

[0048] Behavioral information that is far from the first user's behavioral information can be expected to be information that the first user does not know, and such information may be useful to the first user.

[0049] The extraction unit 134 converts each of the behavioral information of the first user and the behavioral information of the second user into a vector, calculates the distance between the vector corresponding to the behavioral information of the second user and the vector corresponding to the behavioral information of the first user, and extracts behavioral information for which the calculated distance is equal to or greater than a predetermined threshold as behavioral information to be displayed on the information terminal of the first user.The extraction unit 134 extracts behavioral information for which the distance from the behavioral information of the first user is equal to or greater than a predetermined threshold from the behavioral information of the second user for which the estimated value is equal to or greater than the predetermined threshold as behavioral information to be displayed on the information terminal of the first user.

[0050] The identification unit 132 may be configured to identify a second user based on an activity amount in the community. The calculation unit 136 calculates the activity amount of each of multiple users in the community based on behavioral information of multiple users participating in a specific community stored in the storage unit 12. Examples of the activity amount include the number of comments posted to the community and the amount of behavioral information posted by the user. The calculation unit refers to the storage unit 12, tallies the behavioral information of each user belonging to the community, and calculates the activity amount of the user.

[0051] The identification unit 132 identifies a second user from among users whose activity amount is equal to or greater than a predetermined threshold calculated by the calculation unit 136. As an example, the identification unit 132 identifies, as the second user, a user whose activity amount is equal to or greater than a predetermined threshold among users corresponding to behavioral information whose similarity to the behavioral information of the first user is equal to or greater than a predetermined threshold.

[0052] The information to be displayed on the user's information terminal may be determined based on the content of the behavior indicated by the behavior information.

[0053] The determination unit 137 determines information to be displayed on the information terminal of the first user based on the content of the behavior indicated by the behavior information extracted by the extraction unit 134. As an example, the storage unit 12 stores a prediction model, which is a trained model that receives behavior information as input and predicts the probability that the behavior is a behavior regarding a predetermined product. As an example, the prediction model is trained to output a product related to the behavior information when the behavior information is input, using the behavior information and the product targeted by the behavior information as training data.

[0054] The determination unit 137 inputs the behavioral information extracted by the extraction unit 134 into the prediction model and outputs the product targeted by the behavioral information. The determination unit 137 identifies a link in the product information that corresponds to the product output by the prediction model and determines it as information to be displayed on the information terminal 2. The display control unit 135 controls the behavioral information extracted by the extraction unit 134 to be displayed on the information terminal of the first user, and also controls the information determined by the determination unit 137 to be displayed on the information terminal of the first user in association with the behavioral information extracted by the extraction unit 134.

[0055] The information processing device 1 may be configured to grant a benefit to a second user corresponding to the behavioral information displayed on the information terminal 2 of the first user when the first user purchases a product or the like based on the displayed information.

[0056] The information terminal 2 further includes a benefit granting unit 138 that grants a predetermined benefit to the second user identified by the identification unit 132 when the first user purchases the predetermined product via information for viewing information about the predetermined product that is displayed on the information terminal 2 by the display control unit 135. When displaying a link in the product information that corresponds to a character string indicating the product included in the behavioral information, the display control unit 135 causes the information terminal 2 to display a link in which a user ID indicating the second user is embedded.

[0057] When the first user purchases a product on a page showing information about the predetermined product, the acquisition unit 131 acquires information indicating that the first user has purchased the predetermined product. The information indicating that the first user has purchased the predetermined product includes the user ID of the second user embedded in the link. The information indicating that the first user has purchased the predetermined product may also include the price of the product purchased by the first user.

[0058] When the benefit granting unit 138 acquires information indicating that the first user has purchased a predetermined product, the benefit granting unit 138 performs processing to grant a predetermined benefit to the second user. As an example, the benefit granting unit 138 transmits an instruction to grant points to the second user to a server that manages points in a predetermined point service. The amount of points granted to the second user may be a predetermined amount, or may be an amount calculated by multiplying the product purchased by the first user by a predetermined coefficient.

[0059] By configuring the information processing device 1 in this way, it is possible to encourage users to actively post to the community.

[0060] [Processing flow in information processing device 1] Fig. 6 is a flowchart showing the flow of processing in the information processing device 1. The flowchart shown in Fig. 6 starts from the point at which behavioral information is acquired.

[0061] The acquiring unit 131 acquires behavioral information of a first user (S01). The identifying unit 132 identifies a second user who exhibits behavior similar to the behavioral information of the first user (S02).

[0062] The evaluation unit 133 inputs the behavioral information of the second user into the behavior learning model and outputs an estimated value (S03). The extraction unit 134 extracts behavioral information to be displayed on the information terminal of the first user based on the estimated value (S04).

[0063] The display control unit 135 determines whether the extracted behavioral information is a behavior related to a predetermined product (S05). As an example, the display control unit 135 determines whether the extracted behavioral information includes a character string indicating the predetermined product.

[0064] If the extracted behavioral information is behavior related to a predetermined product (YES in S05), the display control unit 135 identifies a link corresponding to the product included in the behavioral information, and causes the information terminal 2 to display a screen including the identified link and the extracted behavioral information (S06).Then, the information processing device 1 ends the process.

[0065] If the extracted behavioral information is not a behavior related to a predetermined product (NO in S05), the display control unit 135 causes the information terminal 2 to display a screen including the extracted behavioral information (S07). Then, the information processing device 1 ends the process.

[0066] [Effects of information processing device 1] By configuring the information processing device 1 in this way, it is possible to improve the effectiveness of advertisements on the community site.

[0067] Furthermore, this invention will make it possible to contribute to Goal 9 of the United Nations' Sustainable Development Goals (SDGs), which is "Build resilient infrastructure, promote inclusive and sustainable industrialization, and promote innovation and resilience."

[0068] The present invention has been described above using embodiments, but the technical scope of the present invention is not limited to the scope described in the above embodiments, and various modifications and changes are possible within the scope of the gist of the present invention. For example, all or part of the device can be configured by functionally or physically distributing or integrating any unit. Furthermore, new embodiments resulting from any combination of multiple embodiments are also included in the embodiments of the present invention. The effects of the new embodiments resulting from the combination also have the effects of the original embodiments. [Explanation of symbols]

[0069] 1. Information processing equipment 2. Information terminal 11 Communications Department 12 Storage section 13 Control Unit 131 Acquisition Department 132 Specific part 133 Evaluation Department 134 Extraction part 135 Display control unit 136 Calculation Unit 137 Decision Section 138 Benefit Granting Department

Claims

1. a memory unit that stores a behavioral learning model that has learned behavioral information of each of a plurality of users participating in a predetermined community, the behavioral learning model being trained to input behavioral information indicating user behavior and output an estimated value indicating the degree to which the user behavior indicated by the input behavioral information is estimated to be the behavior of a user belonging to the community; an acquisition unit that acquires behavior information indicating behavior of a first user who participates in the community; an evaluation unit that, when the acquisition unit acquires the behavioral information of the first user, inputs behavioral information of a second user different from the first user into the behavior learning model and outputs an estimated value; an extraction unit that extracts behavioral information of the second user based on the estimated value output by the evaluation unit to determine information to be displayed on the information terminal of the first user; a determination unit that determines information to be displayed on the information terminal of the first user based on the content of the behavioral information extracted by the extraction unit; a display control unit that controls the information determined by the determination unit to be displayed on the information terminal of the first user; An information processing device having the above.

2. the storage unit further stores a general-purpose learning model that is a pre-trained general-purpose language model, the general-purpose learning model being configured to receive behavioral information as input and output an estimated value; the behavioral learning model is a trained model obtained by fine-tuning the general-purpose learning model based on behavioral information of a plurality of users participating in the predetermined community, the evaluation unit inputs the behavioral information into the general-purpose learning model and causes it to output an estimated value; the extraction unit extracts behavioral information to be displayed on the information terminal of the first user based on the estimated value that the evaluation unit has caused the behavioral learning model to output and the estimated value that the evaluation unit has caused the general-purpose learning model to output. The information processing device according to claim 1 .

3. The extraction unit extracts behavioral information, the behavioral information being displayed on the information terminal of the first user, in which a magnitude relationship between the estimated value that the evaluation unit has caused the behavioral learning model to output and the estimated value that the evaluation unit has caused the general-purpose learning model to output satisfies a predetermined condition. The information processing device according to claim 2 .

4. the extraction unit converts each of the first user's behavior information and the second user's behavior information into a vector, calculates a distance between the vector corresponding to the second user's behavior information and the vector corresponding to the first user's behavior information, and extracts behavior information for which the calculated distance is equal to or greater than a predetermined threshold as behavior information to be displayed on the information terminal of the first user. The information processing device according to claim 1 .

5. the display control unit controls the behavioral information extracted by the extraction unit to be displayed on the information terminal of the first user, and the information determined by the determination unit to be associated with the behavioral information extracted by the extraction unit and displayed on the information terminal of the first user. The information processing device according to claim 1 .

6. The behavioral information is text data indicating user comments in the community. the display control unit controls the information terminal of the first user to further display the behavioral information extracted by the extraction unit, and, when the behavioral information extracted by the extraction unit includes a character string indicating a predetermined product, to display information for viewing information about the predetermined product on the information terminal of the first user in association with the behavioral information extracted by the extraction unit; The information processing device according to claim 5 .

7. the display control unit further displays the behavioral information extracted by the extraction unit on the information terminal of the first user, and, when the behavioral information extracted by the extraction unit indicates behavior related to a predetermined product, displays information for viewing information related to the predetermined product on the information terminal of the first user in association with the behavioral information extracted by the extraction unit; The information processing device further includes a benefit granting unit that grants a predetermined benefit to the second user when the first user purchases the predetermined product via information for viewing information about the predetermined product that is displayed on the information terminal by the display control unit. The information processing device according to claim 1 .

8. The computer executes an acquiring step of acquiring behavior information indicating behavior of a first user who participates in a predetermined community; an evaluation step in which, triggered by the acquisition of the behavioral information of the first user in the acquisition step, a behavioral learning model is inputted to the memory unit, the behavioral learning model having learned the behavioral information of each of a plurality of users participating in the predetermined community, the behavioral learning model being trained to take the behavioral information as input and output an estimated value indicating the degree to which the behavior of the user indicated by the input behavioral information is estimated to be the behavior of a user belonging to the community, the evaluation step being performed to input the behavioral information of a second user different from the first user and output an estimated value; an extraction step of extracting behavioral information of the second user based on the estimated value output in the evaluation step, for determining information to be displayed on the information terminal of the first user; a determination step of determining information to be displayed on the information terminal of the first user based on the content of the behavioral information extracted in the extraction step; a display control step of further displaying the information determined in the determination step on the information terminal of the first user; An information processing method comprising:

9. On the computer, an acquiring step of acquiring behavior information indicating behavior of a first user who participates in a predetermined community; an evaluation step in which, triggered by the acquisition of the behavioral information of the first user in the acquisition step, a behavioral learning model is inputted to the memory unit, the behavioral learning model having learned the behavioral information of each of a plurality of users participating in the predetermined community, the behavioral learning model being trained to take the behavioral information as input and output an estimated value indicating the degree to which the behavior of the user indicated by the input behavioral information is estimated to be the behavior of a user belonging to the community, the evaluation step being performed to input the behavioral information of a second user different from the first user and output an estimated value; an extraction step of extracting behavioral information of the second user based on the estimated value output in the evaluation step, for determining information to be displayed on the information terminal of the first user; a determination step of determining information to be displayed on the information terminal of the first user based on the content of the behavioral information extracted in the extraction step; a display control step of further displaying the information determined in the determination step on the information terminal of the first user; A program to execute.

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