Information processing apparatus, information processing method, and information processing program

Abstract

To efficiently guide a browsing user to a distribution content.SOLUTION: An information processing device according to the present application includes an estimation unit and a selection unit, in which the estimation unit estimates a comment user and a reaction user having a predetermined sympathy degree based on a posting history and a reaction history of a user with respect to distribution content, and the selection unit selects distribution content having a predetermined affinity degree with respect to the estimated comment user and reaction user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Conventionally, there are known techniques for distributing distribution content such as news articles. For example, Patent Document 1 discloses a technique for providing provided content including distribution content and each piece of posted content for the distribution content. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-197422 Summary of the Invention [Problem to be solved by the invention]

[0004] However, conventional techniques have room for improvement in guiding browsing users to distributed content.

[0005] The present invention has been made in view of the above, and has an object to provide an information processing device, an information processing method, and an information processing program that can efficiently guide viewing users to distribution content. [Means for solving the problem]

[0006] The information processing device of the present application is characterized by having an estimation unit that estimates comment users and reaction users who have a predetermined degree of empathy based on users' posting history and reaction history for distributed content, and a selection unit that selects distributed content that has a predetermined degree of compatibility with the estimated comment users and reaction users. [Effects of the Invention]

[0007] According to the present invention, it is possible to efficiently guide viewing users to distribution content. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is an explanatory diagram illustrating an example of information processing according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of the configuration of a terminal device according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of the configuration of the information processing device according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of the user information database. [Figure 6] FIG. 6 is a diagram illustrating an example of the history information database. [Figure 7] FIG. 7 is a diagram illustrating an example of the content information database. [Figure 8] FIG. 8 is a flowchart showing a processing procedure according to the embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of a hardware configuration. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an information processing device, an information processing method, and an information processing program according to the present application (hereinafter referred to as "embodiments") will be described in detail with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program according to the present application are not limited to these embodiments. Furthermore, the same components in the following embodiments will be denoted by the same reference numerals, and duplicated descriptions will be omitted.

[0010] [1. An example of information processing] First, an example of information processing according to an embodiment will be described with reference to FIG. 1. FIG. 1 is a diagram illustrating an example of information processing according to an embodiment, which is executed by an information processing device 100. Note that FIG. 1 illustrates an example in which matching is performed between a comment user and a reaction user based on the degree of empathy, and distribution content that is compatible with both users is assigned to both users. Note that the information processing device 100 processes only comments and articles for which the user's permission has been obtained. For example, a contract regarding the use of an article may be concluded in advance with the right holder of the article (or their representative). Alternatively, when generating content using another article, the poster of the article may be contacted and only if permission to use the article is granted may the content using the article be processed. Furthermore, permission may be sought from the user who posted the comment to be processed when the user posts a comment or when processing the comment.

[0011] The information processing device 100 shown in FIG. 1 is an information processing device that is connected to terminal devices 10a and 10b of multiple users Ua and Ub via a network N (see FIG. 2) via wired or wireless connections to enable mutual communication and provides various information to users Ua and Ub. This information processing device is implemented, for example, by one or more servers or cloud systems. Hereinafter, users Ua, Ub, etc. may be referred to as user U, and terminal devices 10a, 10b, etc. may be referred to as terminal devices 10. Furthermore, users in general may be referred to as user U. Furthermore, distributed content that allows comments and reactions is distributed content that, for example, has a comment posting function and a reaction input function such as a "like" function within an article, allowing the reactions of viewing users to be understood through posted comments and reaction information. In the embodiment, posted comments and positive reactions such as a like button are described as examples, but these are merely examples and are not limiting. Other types of comments and reactions may also be used.

[0012] The terminal device 10 is a smart device such as a smartphone or tablet terminal used by a user U, and is a portable terminal device capable of communicating with any server device via a wireless communication network such as 4G (4th Generation) or LTE (Long Term Evolution). The terminal device 10 has a screen such as a liquid crystal display with a touch panel function, and accepts various operations on displayed data such as content, such as tapping, sliding, and scrolling, performed by the user U with a finger or a stylus. An operation performed on an area of ​​the screen where content is displayed may be considered an operation on the content. The terminal device 10 may be not only a smart device, but also an information processing device such as a desktop PC (Personal Computer) or a notebook PC.

[0013] The information processing device 100 is an information processing device that works in conjunction with the terminal device 10 of each user U and provides API (Application Programming Interface) services for various applications (hereinafter referred to as apps) and various data to the terminal device 10 of each user U, and is realized by a computer, a cloud system, etc.

[0014] Furthermore, the information processing device 100 may be an information processing device that provides some kind of online web service to the terminal device 10 of each user U. For example, the information processing device 100 may provide, as web services, services such as internet connection, search services, SNS (Social Networking Service), electronic commerce (EC), electronic payment, online games, online banking, online trading, hotel and ticket reservations, video and music distribution, news, maps, route searches, route guidance, line information, operation information, and weather forecasts. In practice, the information processing device 100 may cooperate with various servers that provide the above-mentioned web services and act as an intermediary for the web services or may be responsible for processing the web services.

[0015] The information processing device 100 can acquire user information about the user U. For example, the information processing device 100 acquires information about the attributes of the user U, such as the gender, age, and residential area of ​​the user U. The information processing device 100 then stores and manages the information about the attributes of the user U together with identification information (such as a user ID) indicating the user U.

[0016] The information processing device 100 also acquires various types of history information (log data) indicating the behavior of the user U from the terminal device 10 of the user U or from various servers and the like based on the user ID and the like. For example, the information processing device 100 acquires a location history, which is a history of the user U's location and date and time, from the terminal device 10. The information processing device 100 also acquires a search history, which is a history of search queries entered by the user U, from a search server (search engine). The information processing device 100 also acquires a browsing history, which is a history of content viewed by the user U, from a content server. The information processing device 100 also acquires a purchase history (payment history), which is a history of the user U's product purchases and payment processes, from an e-commerce server or a payment processing server. The information processing device 100 may also acquire a listing history and a sales history, which are a history of the user U's listings on the marketplace, from the e-commerce server or the payment processing server. The information processing device 100 also acquires the user U's posting history and reaction history from a distribution content server and the like. The posting history includes, for example, comments posted to an article of the distributed content and comments to comments posted to an article of the distributed content. The reaction history includes, for example, reactions entered to an article of the distributed content and reactions entered to comments posted to an article of the distributed content. The information processing device 100 may acquire the posting history and reaction history of the user U from a posting server or an SNS server that provides a word-of-mouth posting service. Note that the various servers and the like described above may be the information processing device 100 itself. That is, the information processing device 100 may function as the various servers and the like described above.

[0017] Some news sites publish a summary of user U's comments and reactions to news articles. For example, such posted comments and reaction information can be considered part of the content. Therefore, if the comments section of a news article or the summary of reactions becomes popular, an increase in the number of page views (PV) for the corresponding news article is expected.

[0018] Therefore, the information processing device 100 of the embodiment assigns predetermined distribution content to a heavy comment user U who frequently posts comments on articles of distribution content and a reaction user U who frequently reacts to posted comments, and who are compatible with each other, and provides the distribution content assigned to both users in order to stimulate communication within the article.

[0019] [1-1. Deliver content that allows comments and reactions] 1, first, the information processing device 100 distributes distribution content that allows comments and reactions to the terminal device 10a (step S1). Similarly, the information processing device 100 distributes distribution content that allows comments and reactions to the terminal device 10b (step S2).

[0020] The distributed content is, for example, the content of a news article, but may also be the content of a blog article, content posted on a social networking site, or other content. The distributed content includes, for example, news articles and comments or reaction information posted regarding each news article. The posted comments are, for example, comments posted by comment users on articles of the distributed content, or comments posted by comment users on comments on articles of the distributed content. The posted comments are the opinions and impressions of comment users, and include text, stamps, emoticons, etc. Note that the posted comments may be, instead of or in addition to comments, URLs (Uniform Resource Locators) of other content, etc. The reaction information is, for example, an aggregate value of reactions to articles of the distributed content or reactions to posted comments.

[0021] [1-2. Acquiring posted comments / Acquiring reactions to posted comments] Next, the information processing device 100 acquires posted comments linked to the distribution content (step S3). For example, the user Ua operates the terminal device 10a to post a comment that the user Ua has created for the distribution content from the terminal device 10a as a posted comment. For example, the terminal device 10a selects a desired distribution content from a list of distribution contents by the user Ua operating the terminal device 10a, and links the comment that the user Ua created for the distribution content to the distribution content as a posted comment and transmits the comment to the information processing device 100.

[0022] The information processing device 100 also acquires reactions to the posted comments linked to the distribution content (step S4). For example, the user Ub operates the terminal device 10b to input a reaction to the posted comment linked to the distribution content from the terminal device 10b. For example, the user Ub operates the terminal device 10a to select a desired distribution content from the list of distribution contents, and the terminal device 10b associates the reaction input to the posted comment included in the distribution content with the posted comment and transmits it to the information processing device 100.

[0023] The information processing device 100 can accept posted comments linked to distributed content from each of a plurality of comment users including user Ua and sent by the user Ua, and reactions linked to posted comments in the distributed content from each of a plurality of reaction users including user Ub and sent by the user Ub, and can store the accepted posted comments and reactions in a database. For example, the information processing device 100 has a database in which a plurality of posted comments and reactions are linked to each distributed content, and can acquire a plurality of posted comments and reactions linked to the distributed content from the database or external storage, etc.

[0024] [1-3. Obtaining user information for comment users / reaction users] Next, the information processing device 100 acquires user information (attribute information, history information, etc.) of the comment user Ua who posted the comment on the distribution content (step S5). The information processing device 100 identifies the comment user Ua using, for example, an ID that identifies the content or an identifier of the terminal device 10a, and acquires the user information of the comment user Ua.

[0025] Similarly, the information processing device 100 acquires user information (attribute information, history information, etc.) of the reaction user Ub who has input a reaction to a comment on the distributed content (step S6). The information processing device 100 identifies the reaction user Ub using, for example, an ID that identifies the content or an identifier of the terminal device 10b, and acquires the user information of the reaction user Ub.

[0026] [1-4. Matching heavy comment users with reaction users] Next, the information processing device 100 matches heavy comment users with reaction users (step S7). First, if the frequency of comment posting in the posting history of user U is equal to or greater than a predetermined threshold, the information processing device 100 estimates that user U is a heavy comment user U. Furthermore, if the frequency of reaction inputs to posted comments in the reaction history of user U is equal to or greater than a predetermined threshold, the information processing device 100 estimates that user U is a reaction user U.

[0027] For example, the information processing device 100 infers that a reaction user U who directly inputs a favorable reaction such as "like" to a comment posted by a heavy comment user Ua is a reaction user Ub who has a predetermined degree of empathy. Furthermore, the information processing device 100 infers that a reaction user U who inputs a favorable reaction to a comment posted on an article of distribution content similar to the article of distribution content posted by the heavy comment user Ua is a reaction user Ub who has a predetermined degree of empathy. Furthermore, the information processing device 100 infers that a reaction user U who inputs a favorable reaction to a comment posted on an article of distribution content similar to the article of distribution content posted by the heavy comment user Ua is a reaction user Ub who has a predetermined degree of empathy.

[0028] Here, a distributed content article similar to a distributed content article refers to, for example, an article in which the compared distributed content was distributed within a predetermined period of time and which has commonality or similarity in the genre or category of the distributed content article, commonality or similarity in the events included in the distributed content article, or commonality or similarity in the user segments or user personas to which the people appearing in the distributed content article belong, etc. Furthermore, a posted comment similar to a posted comment refers to, for example, a posted comment in which the compared posted comment was posted within a predetermined period of time and has commonality or similarity in the genre or category of the topics of the posted comment, commonality or similarity in the events described in the posted comment, or commonality or similarity in the user segments or user personas to which the people described in the posted comment belong, etc.

[0029] In addition, the information processing device 100 may estimate that a reaction user U who has a history similar to the browsing history or other behavioral history of the heavy comment user Ua, in addition to the posting history of the heavy comment user Ua and the favorable reaction history of the reaction user U, is a reaction user Ub who has a certain degree of sympathy.

[0030] For example, the information processing device 100 may estimate the user attributes of the reaction user U who has a certain degree of empathy based on the posting history of the heavy comment user Ua and the reaction history of the reaction user U, and match the reaction user Ub with the estimated user attributes. The user attributes include a user segment, a user persona, etc.

[0031] At this time, the information processing device 100 may use machine learning to estimate the user attributes of the reaction user U from the posting history of the heavy comment user Ua and the reaction history of the reaction user U. For example, the information processing device 100 uses the posting history of the heavy comment user Ua and the reaction history of the reaction user U as input data to generate an estimation model that outputs the user attributes of the reaction user U. Note that the number of user attributes to be output may be multiple.

[0032] Alternatively, in order to estimate the user attributes of the reaction user U from the posting history of the heavy comment user Ua and the reaction history of the reaction user U, the information processing device 100 may use machine learning to generate an estimation model that uses as input data a set of the posting history of the heavy comment user Ua, the reaction history of the reaction user U, and the user attributes of the reaction user U, and outputs an empathy score (or an empathy level). The empathy score is, for example, a score that indicates the degree of empathy regarding the commonality or similarity of the distributed content in the posting history of the heavy comment user Ua and the reaction history of the reaction user U.

[0033] The information processing device 100 may, for example, use image recognition and natural language processing (NLP) to vectorize the posting history of heavy comment user U, the reaction history of reaction user U, and the user attributes of reaction user U, and calculate the empathy score using the cosine similarity of each vector.

[0034] The information processing device 100 may use machine learning to generate an estimation model that uses a set of the posting history of heavy comment user Ua, the reaction history of reaction user U, and an empathy score as input data, and outputs the user attributes of reaction user U or a reaction user Ub with those user attributes. The information processing device 100 inputs the posting history of heavy comment user Ua, the reaction history of reaction user U, and a predetermined empathy score into the estimation model, and matches the user attributes of reaction user U or a reaction user Ub with those user attributes who are connected through, for example, the posting history of heavy comment user Ua and the reaction history of reaction user U. There may be multiple reaction users Ub.

[0035] More specifically, for example, the information processing device 100 calculates an empathy score related to the commonality or similarity of the distributed content in the posting history of the heavy comment user Ua and the reaction history of the reaction user U, based on the distribution of the distributed content within a predetermined period, the commonality or similarity of the genres or categories of the articles of the distributed content, the commonality or similarity of the events included in the articles of the distributed content, or the commonality or similarity of the user segments or user personas to which the people appearing in the articles of the distributed content belong. Next, the information processing device 100 estimates the user attributes (e.g., user segment, user persona, etc.) of the reaction user U who has the input empathy score (e.g., the input empathy score or higher). Next, the information processing device 100 searches for a reaction user Ub who belongs to the user attribute from, for example, a history information database or a content information database, and matches the reaction user Ub. The information processing device 100 may estimate the user attributes (e.g., user segment, user persona, etc.) of the reaction user U by, for example, adding the heavy comment user Ua's browsing history and other behavioral history to the browsing history and other behavioral history of the reaction user U, in addition to the posting history of the heavy comment user Ua and the favorable reaction history of the reaction user U, and using an empathy score that takes into account the commonality or similarity of the browsing history and other behavioral history.

[0036] The machine learning method may be deep learning, a recurrent neural network (RNN), a long short-term memory (LSTM), etc. Note that these are merely examples and are not intended to be limiting.

[0037] [1-5. Selecting content to be distributed] Next, the information processing device 100 selects distribution content to be provided to the matched heavy comment user Ua and reaction user Ub based on feedback information for the article, such as an "article reaction button" for the distribution content (step S8). An "article reaction button" is, for example, a button such as "educational," "easy to understand," or "new perspective" that is provided to the viewing user along with the article in the distribution content, and is used to obtain feedback on the article from the viewing user. For example, the information processing device 100 may select articles in the distribution content that have a predetermined number of characters, have a predetermined number of reaction inputs related to "educational" or more, and have a predetermined number of posted comments or less.

[0038] The information processing device 100 may select an article genre of the content to be distributed that belongs to a predetermined selection target based on, for example, the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub, and select the content to be distributed from the selected article genre. The article genre may be, for example, politics, economy, international affairs, society, science, culture, entertainment, sports, etc.

[0039] The information processing device 100 may use machine learning to select an article genre of distribution content that belongs to a predetermined selection target from the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub. For example, the information processing device 100 uses the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub as input data and generates a selection model that outputs an article genre of distribution content that belongs to the predetermined selection target. Note that there may be multiple article genres of distribution content that belong to the predetermined selection target to be output.

[0040] Alternatively, in order to select an article genre of distribution content that belongs to a predetermined selection target from the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub, the information processing device 100 may use machine learning to generate a selection model that uses as input data pairs of the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub and the article genre of distribution content that belongs to the predetermined selection target, and outputs a compatibility score (or a compatibility level). The compatibility score is, for example, a score that indicates the degree of compatibility related to the commonality or similarity between the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub and the article genre.

[0041] The information processing device 100 may, for example, use image recognition and natural language processing to vectorize the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub, as well as the article genre of the distributed content that belongs to a specified selection target, and calculate a compatibility score using the cosine similarity of each vector.

[0042] The information processing device 100 may use machine learning to generate a selection model that uses a combination of the posting history of the heavy comment user Ua, the reaction history of the reaction user Ub, and a compatibility score as input data, and outputs an article genre of the distributed content that belongs to a predetermined selection target, or articles of that article genre. The information processing device 100 inputs a predetermined compatibility score into the selection model, and selects, for example, an article genre of the distributed content that belongs to the predetermined selection target, or articles of that article genre, that are connected by the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub, etc. The number of selected articles may be multiple.

[0043] [1-6. Assign selected distribution content] Next, the information processing device 100 assigns the selected distribution content to the heavy comment user Ua (step S9). Specifically, the information processing device 100 transmits the selected distribution content to the terminal device 10a of the heavy comment user Ua. When the heavy comment user Ua creates a comment on the assigned distribution content and posts it linked to the distribution content, the information processing device 100 acquires the posted comment linked to the distribution content from the terminal device 10a.

[0044] Next, the information processing device 100 provides the selected distribution content, on which the comment has been posted by the heavy comment user Ua, to the reaction user Ub (step S10). Specifically, the information processing device 100 transmits the selected distribution content, on which the comment has been posted by the heavy comment user Ua, to the terminal device 10b of the reaction user Ub. When the reaction user Ub inputs a reaction to the comment on the distribution content, the information processing device 100 acquires the reaction linked to the distribution content from the terminal device 10b.

[0045] [1-7. Variation] When providing the selected distribution content on which a comment has been posted by the heavy comment user Ua to the reaction user Ub, the information processing device 100 may provide a summary of the distribution content including the comment posted by the heavy comment user Ua. The information processing device 100 may generate the summary using a generative model such as a GPT (Generative Pre-Trained Transformer). Furthermore, the information processing device 100 may not only simply encourage the reaction user Ub to react, but may also extract questions such as "What do other people think?" from the comments posted by the heavy comment user Ua and suggest to the reaction user Ub to input a reaction or post a comment in response to the question.

[0046] Furthermore, the information processing device 100 may perform the above-mentioned processing only on comments posted by heavy comment users Ua during a predetermined period.

[0047] In this way, the information processing device 100 can stimulate communication within an article by assigning a heavy comment user U and a reaction user U who are matched at a predetermined level of empathy to a good article that has posted few comments, and when the assigned heavy comment user actually posts a comment, the comment is displayed at the top of the results, thereby spurring the stimulation of communication.

[0048] [2. Example of information processing system configuration] Next, the configuration of an information processing system 1 including an information processing device 100 according to an embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the configuration of the information processing system 1 according to an embodiment. As shown in Fig. 2, the information processing system 1 according to an embodiment includes a terminal device 10 and an information processing device 100. These various devices are connected to each other via a network N so as to be able to communicate with each other via a wired or wireless connection. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

[0049] Furthermore, the number of devices included in the information processing system 1 shown in Fig. 2 is not limited to that shown in the figure. For example, in Fig. 2, for the sake of simplicity, only two terminal devices 10 are shown, but this is merely an example and is not limiting, and three or more devices may be included.

[0050] The terminal device 10 is an information processing device used by a user U. For example, the terminal device 10 may be a smart device such as a smartphone or a tablet terminal, a feature phone, a PC (Personal Computer), a PDA (Personal Digital Assistant), a game console or AV device with a communication function, a car navigation system, a wearable device such as a smart watch or a head-mounted display, smart glasses, etc. The terminal device 10 may also be a house / building, a car, a home appliance, an electronic device, etc. that is compatible with the Internet of Things (IOT).

[0051] In addition, the terminal device 10 can connect to a network N via a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation: 5th generation mobile communication system), or via short-range wireless communication such as Bluetooth (registered trademark) or wireless LAN (Local Area Network), and communicate with the information processing device 100.

[0052] The information processing device 100 is, for example, a computer such as a PC or a blade server, or a mainframe or a workstation, etc. The information processing device 100 may be realized by cloud computing.

[0053] [3. Example of terminal device configuration] Next, the configuration of the terminal device 10 will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the configuration of the terminal device 10. As shown in Fig. 3, the terminal device 10 includes a communication unit 11, a display unit 12, an input unit 13, a positioning unit 14, a sensor unit 20, a control unit 30 (controller), and a storage unit 40.

[0054] (Communications Department 11) The communication unit 11 is connected to a network N (see FIG. 2) by wire or wirelessly, and transmits and receives information to and from the information processing device 100 via the network N. For example, the communication unit 11 is realized by a NIC (Network Interface Card), an antenna, etc.

[0055] (Display section 12) Display unit 12 is a display device that displays various information such as position information. For example, display unit 12 is a liquid crystal display (LCD) or an organic electro-luminescent display (OLED). Display unit 12 is also a touch panel display, but is not limited to this.

[0056] (Input section 13) The input unit 13 is an input device that accepts various operations from the user U. For example, the input unit 13 has buttons for inputting characters, numbers, and the like. The input unit 13 may be an input / output port (I / O port), a USB (Universal Serial Bus) port, or the like. If the display unit 12 is a touch panel display, a part of the display unit 12 functions as the input unit 13. The input unit 13 may be a microphone that accepts voice input from the user U. The microphone may be wireless.

[0057] (Positioning unit 14) The positioning unit 14 receives signals (radio waves) transmitted from satellites of a GPS (Global Positioning System), and acquires position information (e.g., latitude and longitude) indicating the current position of the terminal device 10, which is the device itself, based on the received signals. That is, the positioning unit 14 positions the position of the terminal device 10. Note that GPS is merely an example of a GNSS (Global Navigation Satellite System).

[0058] The positioning unit 14 can also measure the position using various methods other than GPS. For example, the positioning unit 14 may measure the position by using various communication functions of the terminal device 10 as an auxiliary positioning means for position correction, etc., as described below.

[0059] (Wi-Fi positioning) For example, the positioning unit 14 uses a Wi-Fi (registered trademark) communication function of the terminal device 10 or a communication network provided by each communication company to measure the position of the terminal device 10. Specifically, the positioning unit 14 performs Wi-Fi communication or the like and measures the distance to a nearby base station or access point, thereby measuring the position of the terminal device 10.

[0060] (Beacon positioning) The positioning unit 14 may also measure the position by using a Bluetooth (registered trademark) function of the terminal device 10. For example, the positioning unit 14 measures the position of the terminal device 10 by connecting to a beacon transmitter connected by the Bluetooth (registered trademark) function.

[0061] (geomagnetic positioning) The positioning unit 14 also measures the position of the terminal device 10 based on a geomagnetic pattern of a structure that has been measured in advance and a geomagnetic sensor that the terminal device 10 has.

[0062] (RFID positioning) Furthermore, for example, if the terminal device 10 has a function of an RFID (Radio Frequency Identification) tag equivalent to a contactless IC card used at station ticket gates, in stores, etc., or has a function of reading an RFID tag, the location where the terminal device 10 was used is recorded together with information on the payment or the like made by the terminal device 10. The positioning unit 14 may obtain such information to determine the location of the terminal device 10. Alternatively, the location may be determined by an optical sensor, an infrared sensor, or the like provided in the terminal device 10.

[0063] The positioning unit 14 may measure the position of the terminal device 10 using one or a combination of the above-mentioned positioning means, as needed.

[0064] (Sensor unit 20) The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. The connection may be wired or wireless. For example, the sensors may be detection devices other than the terminal device 10, such as wearable devices or wireless devices. In the example shown in FIG. 3 , the sensor unit 20 includes an acceleration sensor 21, a gyro sensor 22, a barometric pressure sensor 23, a temperature sensor 24, a sound sensor 25, a light sensor 26, a magnetic sensor 27, and an image sensor (camera) 28.

[0065] The above-described sensors 21 to 28 are merely examples and are not intended to be limiting. That is, the sensor unit 20 may be configured to include some of the sensors 21 to 28, or may include other sensors such as a humidity sensor in addition to or instead of the sensors 21 to 28.

[0066] The acceleration sensor 21 is, for example, a three-axis acceleration sensor, and detects physical movements of the terminal device 10, such as the direction of movement, speed, and acceleration of the terminal device 10. The gyro sensor 22 detects physical movements of the terminal device 10, such as tilt in three axial directions, based on the angular velocity of the terminal device 10. The air pressure sensor 23 detects, for example, the air pressure around the terminal device 10.

[0067] Since the terminal device 10 includes the acceleration sensor 21, the gyro sensor 22, the atmospheric pressure sensor 23, etc., it is possible to measure the position of the terminal device 10 using a technique such as Pedestrian Dead-Reckoning (PDR) that uses these sensors 21 to 23. This makes it possible to obtain indoor position information that is difficult to obtain using a positioning system such as GPS.

[0068] For example, the number of steps, walking speed, and distance walked can be calculated using a pedometer that uses the acceleration sensor 21. In addition, the direction of travel, line of sight, and body tilt of the user U can be determined using the gyro sensor 22. In addition, the altitude and floor on which the user U's terminal device 10 is located can be determined from the air pressure detected by the air pressure sensor 23.

[0069] The temperature sensor 24 detects, for example, the temperature around the terminal device 10. The sound sensor 25 detects, for example, the sound around the terminal device 10. The light sensor 26 detects the illuminance around the terminal device 10. The magnetic sensor 27 detects, for example, the geomagnetism around the terminal device 10. The image sensor 28 captures an image around the terminal device 10.

[0070] The above-mentioned air pressure sensor 23, temperature sensor 24, sound sensor 25, light sensor 26, and image sensor 28 can detect the air pressure, temperature, sound, and illuminance, respectively, and capture images of the surroundings, thereby detecting the environment and situation around the terminal device 10. Furthermore, the accuracy of the location information of the terminal device 10 can be improved based on the environment and situation around the terminal device 10.

[0071] (control unit 30) The control unit 30 includes, for example, a microcomputer having a CPU (Central Processing Unit), ROM (Read Only Memory), RAM, input / output ports, etc., and various other circuits. The control unit 30 may also be configured with hardware such as an integrated circuit, for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 30 includes a transmitting unit 31, a receiving unit 32, and a processing unit 33.

[0072] (Transmitter 31) The transmission unit 31 can transmit, for example, various information input by the user U using the input unit 13, various information detected by each sensor 21 to 28 mounted on or connected to the terminal device 10, and location information of the terminal device 10 measured by the positioning unit 14 to the information processing device 100 via the communication unit 11.

[0073] (Receiving unit 32) The receiving unit 32 can receive various information provided by the information processing device 100 and requests for various information from the information processing device 100 via the communication unit 11.

[0074] (Processing unit 33) The processing unit 33 controls the entire terminal device 10, including the display unit 12. For example, the processing unit 33 can output various information transmitted by the transmission unit 31 and various information received from the information processing device 100 by the reception unit 32 to the display unit 12 for display.

[0075] (Storage unit 40) The storage unit 40 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), an optical disk, etc. The storage unit 40 stores various programs, various data, etc.

[0076] [4. Configuration example of information processing device] Next, the configuration of the information processing device 100 according to the embodiment will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of the configuration of the information processing device 100 according to the embodiment. As shown in Fig. 4, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0077] (Communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC), etc. The communication unit 110 is also connected to a network N (see FIG. 2) by wire or wirelessly.

[0078] (Storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as an HDD, an SSD, an optical disk, etc. As shown in Fig. 4, the storage unit 120 has a user information database 121, a history information database 122, and a content information database 123.

[0079] (User Information Database 121) The user information database 121 stores user information about the user U. For example, the user information database 121 stores various information such as the attributes of the user U. FIG. 5 is a diagram showing an example of the user information database 121. In the example shown in FIG. 5, the user information database 121 has items such as "User ID (Identifier)," "Age," "Gender," "Home," "Workplace," and "Interests."

[0080] The "user ID" indicates identification information for identifying the user U. The "user ID" may be the contact information of the user U (telephone number, email address, etc.), or may be identification information for identifying the terminal device 10 of the user U.

[0081] Furthermore, "age" indicates the age of user U identified by the user ID. Note that "age" may be information indicating the specific age of user U (e.g., 35 years old, etc.), or may be information indicating the generation of user U (e.g., 30s, etc.). Alternatively, "age" may be information indicating the date of birth of user U, or may be information indicating the generation of user U (e.g., born in the 1980s, etc.). Furthermore, "gender" indicates the gender of user U identified by the user ID.

[0082] Furthermore, "home" indicates the location information of the home of user U identified by the user ID. In the example shown in FIG. 5, "home" is illustrated as an abstract code such as "LC11," but it may also be latitude and longitude information, etc. Furthermore, for example, "home" may also be the name of an area or an address.

[0083] Furthermore, "workplace" indicates location information of the workplace (school in the case of a student) of user U identified by the user ID. In the example shown in FIG. 5, "workplace" is illustrated as an abstract code such as "LC12," but it may also be latitude and longitude information, etc. Furthermore, for example, "workplace" may also be the name of a region or an address.

[0084] Furthermore, "interests" indicate the interests of a user U identified by a user ID. In other words, "interests" indicate subjects in which a user U identified by a user ID is highly interested. For example, "interests" may be search queries (keywords) entered by a user U into a search engine. Note that, in the example shown in FIG. 5, one "interest" is illustrated for each user U, but there may be multiple "interests."

[0085] For example, in the example shown in FIG. 5, the age of user U identified by user ID "U1" is "20s" and the gender is "male." Furthermore, for example, the home address of user U identified by user ID "U1" is "LC11." Furthermore, for example, the workplace of user U identified by user ID "U1" is "LC12." Furthermore, for example, the user U identified by user ID "U1" is interested in "sports."

[0086] 5, abstract values ​​such as "U1", "LC11", and "LC12" are used for illustration, but "U1", "LC11", and "LC12" are assumed to store information such as specific character strings and numerical values. Below, abstract values ​​may also be illustrated in diagrams relating to other information.

[0087] The user information database 121 is not limited to the above, and may store various types of information depending on the purpose. For example, the user information database 121 may store various types of information related to the terminal device 10 of the user U. The user information database 121 may also store information related to the user U's attributes, such as demographic attributes, psychographic attributes, geographic attributes, and behavioral attributes. For example, the user information database 121 may store information such as name, family structure, hometown (hometown), occupation, job title, income, qualifications, type of residence (detached house, apartment, etc.), whether or not the user has a car, commuting time, commuting route, commuter pass section (station, line, etc.), frequently used stations (other than the nearest station to home or workplace), extracurricular activities (location, time zone, etc.), hobbies, interests, lifestyle, etc.

[0088] (History Information Database 122) The history information database 122 stores various information related to history information (log data) that indicates the behavior of the user U. Fig. 6 is a diagram showing an example of the history information database 122. In the example shown in Fig. 6, the history information database 122 has items such as "user ID," "location history," "purchase history," "search history," "browsing history," "posting history," and "reaction history."

[0089] "User ID" indicates identification information for identifying user U. "Location history" indicates location history, which is a history of user U's locations and movements. "Purchase history" indicates purchase history, which is a history of purchases made by user U. "Search history" indicates search history, which is a history of search queries entered by user U. "Browsing history" indicates browsing history, which is a history of content viewed by user U. "Post history" indicates posting history, which is a history of posts made by user U. "Reaction history" indicates reaction history, which is a history of reactions made by user U.

[0090] For example, in the example shown in Figure 6, user U, identified by user ID "U1," moved as shown in "Location History #1," purchased specific products at specific stores as shown in "Purchase History #1," searched as shown in "Search History #1," viewed content as shown in "Viewing History #1," posted as shown in "Posting History #1," and reacted as shown in "Reaction History #1."

[0091] Here, in the example shown in Figure 6, abstract values ​​such as "U1", "Location History #1", "Purchase History #1", "Search History #1", "Viewing History #1", "Posting History #1", and "Reaction History #1" are used for the illustration, but "U1", "Location History #1", "Purchase History #1", "Search History #1", "Viewing History #1", "Posting History #1", and "Reaction History #1" are assumed to store specific information such as character strings and numbers.

[0092] The history information database 122 is not limited to the above and may store various types of information depending on the purpose. For example, the history information database 122 may store the user U's usage history of a predetermined service. The history information database 122 may also store the user U's store visit history or facility visit history. The history information database 122 may also store the user U's payment history (electronic payment) using the terminal device 10.

[0093] (Content Information Database 123) The content information database 123 stores various information related to distributed content and posting and reaction patterns. Fig. 7 is a diagram showing an example of the content information database 123. In the example shown in Fig. 7, the content information database 123 has items such as "distributed content," "content details," "user ID," "posting information," and "reaction information."

[0094] "Distributed content" indicates identification information for identifying the distributed content. In practice, this may be the title or genre of the distributed content (or the source, theme, or category). "Content content" indicates an article of the distributed content or identification information for identifying an article of the distributed content. "User ID" indicates identification information for identifying user U. "Posted information" indicates posted comments on the distributed content or posted comments on comments included in the distributed content. "Reaction information" indicates reactions to the distributed content or reactions to comments included in the distributed content.

[0095] For example, in the example shown in Figure 7, for the distribution content "Distribution #1," the content is identified by "Content #1," and user U, identified by "User ID," posted a comment identified by "Posted information #1" and entered a reaction identified by "Reaction information #1."

[0096] Here, in the example shown in Figure 7, abstract values ​​such as "Distribution #1," "Content #1," "Posted information #1," and "Reaction information #1" are used for the illustration, but "Distribution #1," "Content #1," "Posted information #1," and "Reaction information #1" are assumed to store specific information such as character strings and numbers.

[0097] The content information database 123 is not limited to the above and may store various types of information depending on the purpose. For example, the content information database 123 may store identification information for identifying the provider (distributor) of the distributed content. The content information database 123 may also store information related to content and comment trees.

[0098] (control unit 130) 4, the explanation will be continued. The control unit 130 is a controller, and is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like, executing various programs (corresponding to examples of information processing programs) stored in a storage device inside the information processing device 100 using a storage area such as a RAM as a working area. In the example shown in FIG. 4, the control unit 130 has a distribution unit 131, an acquisition unit 132, an estimation unit 133, a selection unit 134, and a provision unit 135.

[0099] The distribution unit 131 distributes distribution content that allows comments and reactions to the terminal device 10. The distribution content includes, for example, news articles, comments posted to each news article, and reactions to the comments.

[0100] Specifically, the distribution unit 131 distributes information about the top page of the news site to the terminal device 10, and then distributes distribution content corresponding to the news article selected by the user U.

[0101] In addition, the distribution unit 131 accepts comments posted and reactions entered by the user U through the distribution content, and registers the comments posted and reactions entered by the user U in the content information database 123, and distributes them to the user U as distribution content from the next time onwards.

[0102] (Acquisition part 132) The acquisition unit 132 acquires posted comments linked to the distribution content and reactions to the posted comments. For example, when a user U posts a comment or inputs a reaction regarding the distribution content from the terminal device 10, the acquisition unit 132 acquires the posted comments or reactions linked to the distribution content via the communication unit 110. The acquisition unit 132 may store the comments posted by each of multiple comment users in relation to the distribution content and the reactions input by each of multiple reaction users in relation to the distribution content in a database. Furthermore, the acquisition unit 132 may acquire the posted comments and reactions linked to the distribution content from a database in which the posted comments and reactions are linked to each distribution content or from an external storage or the like.

[0103] Furthermore, the acquisition unit 132 acquires user information about the user U via the communication unit 110. For example, the acquisition unit 132 acquires identification information (such as a user ID) indicating the user U, location information of the user U, attribute information of the user U, and the like from the terminal device 10 of the user U. Furthermore, the acquisition unit 132 may acquire the identification information indicating the user U, the attribute information of the user U, and the like when registering the user U. Then, the acquisition unit 132 registers the user information in the user information database 121 of the storage unit 120.

[0104] Furthermore, the acquisition unit 132 acquires various types of history information (log data) indicating the behavior of the user U via the communication unit 110. For example, the acquisition unit 132 acquires various types of history information indicating the behavior of the user U from the terminal device 10 of the user U or from various servers based on the user ID or the like. Then, the acquisition unit 132 registers the various types of history information in the history information database 122 of the storage unit 120.

[0105] Furthermore, the acquisition unit 132 acquires a search query input by the user U. For example, when the user U inputs a search query into a search engine or the like to perform a keyword search, the acquisition unit 132 acquires the search query via the communication unit 110. That is, the acquisition unit 132 acquires, via the communication unit 110, the keywords input by the user U into the search box of a search engine, website, or app.

[0106] (Estimation part 133) If the comment posting frequency in the posting history of the user U is equal to or greater than a predetermined threshold, the estimation unit 133 estimates the user U as a heavy comment user U. Furthermore, if the reaction input frequency to posted comments in the reaction history of the user U is equal to or greater than a predetermined threshold, the estimation unit 133 estimates the user U as a reaction user U.

[0107] For example, the estimation unit 133 estimates that a reaction user U who directly inputs a favorable reaction such as "Like" to a comment posted by a heavy comment user Ua is a reaction user Ub who has a predetermined degree of empathy. Furthermore, the estimation unit 133 estimates that a reaction user U who inputs a favorable reaction to a comment posted on an article of distribution content similar to the article content of the distribution content posted by the heavy comment user Ua is a reaction user Ub who has a predetermined degree of empathy. Furthermore, the estimation unit 133 estimates that a reaction user U who inputs a favorable reaction to a comment posted on an article of distribution content similar to the article content of the distribution content posted by the heavy comment user Ua is a reaction user Ub who has a predetermined degree of empathy.

[0108] In addition, the estimation unit 133 may estimate that a reaction user U who has a history similar to the browsing history or other behavioral history of the heavy comment user Ua, in addition to the posting history of the heavy comment user Ua and the favorable reaction history of the reaction user U, is a reaction user Ub who has a certain degree of sympathy.

[0109] The estimation unit 133 may estimate the user attributes of the reaction user U who has a predetermined sympathy based on, for example, the posting history of the heavy comment user Ua and the reaction history of the reaction user U, and match the reaction user Ub with the estimated user attributes. The user attributes include a user segment, a user persona, etc.

[0110] At this time, the estimation unit 133 may use machine learning to estimate the user attributes of the reaction user U from the posting history of the heavy comment user Ua and the reaction history of the reaction user U. For example, the estimation unit 133 uses the posting history of the heavy comment user Ua and the reaction history of the reaction user U as input data, and generates an estimation model that outputs the user attributes of the reaction user U. Note that there may be multiple user attributes that are output.

[0111] Alternatively, in order to estimate the user attributes of the reaction user U from the posting history of the heavy comment user Ua and the reaction history of the reaction user U, the estimation unit 133 may use machine learning to generate an estimation model that uses as input data a set of the posting history of the heavy comment user Ua, the reaction history of the reaction user U, and the user attributes of the reaction user U, and outputs an empathy score (or an empathy level). The empathy score is, for example, a score that indicates the degree of empathy regarding the commonality or similarity of the distributed content in the posting history of the heavy comment user Ua and the reaction history of the reaction user U.

[0112] The estimation unit 133 may use machine learning to generate an estimation model that uses a set of the posting history of heavy comment user Ua, the reaction history of reaction user U, and an empathy score as input data, and outputs the user attributes of reaction user U or a reaction user Ub with those user attributes. The estimation unit 133 inputs the posting history of heavy comment user Ua, the reaction history of reaction user U, and a predetermined empathy score into the estimation model, and matches the user attributes of reaction user U or a reaction user Ub with those user attributes who are connected through, for example, the posting history of heavy comment user Ua and the reaction history of reaction user U. There may be multiple reaction users Ub.

[0113] More specifically, the estimation unit 133 calculates an empathy score related to the commonality or similarity of the distributed content in the posting history of the heavy comment user Ua and the reaction history of the reaction user U, based on, for example, the distribution content in the posting history of the heavy comment user Ua and the favorable reaction history of the reaction user U, which were distributed within a predetermined period, and based on commonality or similarity, such as the genre or category of the article of the distributed content, commonality or similarity, such as the event included in the article of the distributed content, or commonality or similarity, such as the user segment or user persona to which the person who appears in the article of the distributed content belongs. Next, the estimation unit 133 estimates the user attributes (e.g., user segment, user persona, etc.) of the reaction user U who has the input empathy score (e.g., the input empathy score or higher). Next, the estimation unit 133 searches for a reaction user Ub who belongs to the user attribute from, for example, the history information database 122 or the content information database 123, and matches the reaction user Ub. The estimation unit 133 may estimate the user attributes (e.g., user segment, user persona, etc.) of the reaction user U by, for example, adding the posting history of the heavy comment user Ua and the favorable reaction history of the reaction user U to the browsing history and other behavioral history of the heavy comment user Ua and the browsing history and other behavioral history of the reaction user U, and using an empathy score that takes into account the commonality or similarity of the browsing history and other behavioral history.

[0114] (Selection unit 134) The selection unit 134 selects distribution content to be provided to the matched heavy comment user Ua and reaction user Ub based on feedback information for the article, such as an "article reaction button," for the distribution content. The "article reaction button" is, for example, a button such as "educational," "easy to understand," or "new perspective" that is provided to the viewing user along with the article in the distribution content, and is used to obtain feedback on the article from the viewing user. For example, the selection unit 134 may select articles in the distribution content that have a predetermined number of characters, have a predetermined number of reaction inputs related to "educational" or more, and have a predetermined number of posted comments or less.

[0115] The selection unit 134 may select an article genre of the content to be distributed that belongs to a predetermined selection target based on, for example, the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub, and select the content to be distributed from the selected article genre. The article genre may be, for example, politics, economy, international affairs, society, science, culture, entertainment, sports, etc.

[0116] The selection unit 134 may use machine learning to select an article genre of distribution content that belongs to a predetermined selection target from the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub. For example, the selection unit 134 uses the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub as input data and generates a selection model that outputs an article genre of distribution content that belongs to the predetermined selection target. Note that there may be multiple article genres of distribution content that belong to the predetermined selection target that are output.

[0117] Alternatively, the selection unit 134 may use machine learning to generate a selection model that uses as input data pairs of the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub, and the article genre of the distribution content that belongs to the predetermined selection target, and outputs a compatibility score (or a compatibility level), in order to select an article genre of the distribution content that belongs to a predetermined selection target from the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub. The compatibility score is, for example, a score that indicates the degree of compatibility related to the commonality or similarity between the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub and the article genre.

[0118] The selection unit 134 may, for example, use image recognition and natural language processing to vectorize the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub, as well as the article genre of the distributed content that belongs to a specified selection target, and calculate a compatibility score using the cosine similarity of each vector.

[0119] The selection unit 134 may use machine learning to generate a selection model that uses a combination of the posting history of the heavy comment user Ua, the reaction history of the reaction user Ub, and a compatibility score as input data, and outputs an article genre of the distributed content that belongs to a predetermined selection target, or articles of that article genre. The information processing device 100 inputs a predetermined compatibility score into the selection model, and selects, for example, an article genre of the distributed content that belongs to a predetermined selection target, or articles of that article genre, that are connected by the posting history of the heavy comment user Ua and the reaction history of the reaction user Ub, etc. The number of selected articles may be multiple.

[0120] (Provider 135) The providing unit 135 provides the selected distribution content to the heavy comment user Ua. Specifically, the providing unit 135 provides the selected distribution content to the terminal device 10a of the heavy comment user Ua via the communication unit 110.

[0121] Next, the providing unit 135 provides the selected distribution content, on which the comment has been posted by the heavy comment user Ua, to the reaction user Ub. Specifically, the providing unit 135 provides the selected distribution content, on which the comment has been posted by the heavy comment user Ua, to the terminal device 10b of the reaction user Ub via the communication unit 110.

[0122] [5. Processing Procedure] Next, a processing procedure by the information processing device 100 according to the embodiment will be described with reference to Fig. 8. Fig. 8 is a flowchart showing the processing procedure according to the embodiment. Note that the processing procedure shown below is repeatedly executed by the control unit 130 of the information processing device 100.

[0123] As shown in FIG. 8, the distribution unit 131 of the information processing device 100 distributes distribution content that allows comments and reactions via the communication unit 110 (step S101).

[0124] Next, the acquisition unit 132 of the information processing device 100 acquires posted comments linked to the distribution content via the communication unit 110 (step S102).

[0125] Next, the acquisition unit 132 of the information processing device 100 acquires, via the communication unit 110, reactions to the posted comments linked to the distribution content (step S103).

[0126] Next, the acquisition unit 132 of the information processing device 100 acquires the user information of the comment user U and the reaction user U (step S104).

[0127] Next, the estimation unit 133 of the information processing device 100 estimates whether or not the heavy comment user U and the reaction user U match with a predetermined degree of empathy (step S105).

[0128] If the heavy comment user and the reaction user match at a predetermined degree of empathy (step S105: Yes), the selection unit 134 of the information processing device 100 selects distribution content that has a predetermined degree of compatibility with the heavy comment user U and the reaction user U (step S106).

[0129] Next, the providing unit 135 of the information processing device 100 assigns the selected distribution content to the heavy comment user U via the communication unit 110 (step S107).

[0130] Next, the providing unit 135 of the information processing device 100 provides the distribution content including the comments posted by the heavy comment user to the reaction user U via the communication unit 110 (step S108).

[0131] If the heavy comment user and the reaction user do not match at a predetermined empathy level (step S105: No), the information processing device 100 appropriately repeats the processes from step S102 onwards.

[0132] [6. Modifications] The terminal device 10 and the information processing device 100 described above may be implemented in various different forms other than the above-described embodiment. Therefore, modifications of the embodiment will be described below.

[0133] In the above embodiment, some or all of the processing performed by the information processing device 100 may actually be performed by the terminal device 10. For example, the processing may be completed in a stand-alone manner (by the terminal device 10 alone). In this case, the terminal device 10 is assumed to have the functions of the information processing device 100 in the above embodiment. Furthermore, in the above embodiment, the terminal device 10 cooperates with the information processing device 100, and therefore, from the perspective of the user U, it appears that the processing of the information processing device 100 is also being performed by the terminal device 10. In other words, from another perspective, it can be said that the terminal device 10 is equipped with the information processing device 100.

[0134] For example, the terminal device 10 may execute the processing of the information processing device 100 in the above embodiment as processing on an installed app. That is, the processing of the information processing device 100 in the above embodiment may be processing on an app installed on the terminal device 10. In this case, the terminal device 10 may generate an estimation model through on-device learning. Alternatively, the terminal device 10 may cooperate with the information processing device 100, provide data to the information processing device 100, and acquire an estimation model generated by the information processing device 100 through machine learning. Note that the information processing device 100 may generate an estimation model for each user attribute (segment, persona). Furthermore, the terminal device 10 may cooperate with the information processing device 100 and generate an estimation model through federated learning.

[0135] Furthermore, in the above embodiment, news and the like are merely an example of distributed content. In reality, the distributed content may be a website where comments (or any other writings) can be posted, such as a bulletin board, as well as SNS, SMS (Short Message Service), a messaging app, and the like. Furthermore, the type (content) of the comment may be a product or service review, a questionnaire response, or the like.

[0136] [7. Effects] As described above, the information processing device (terminal device 10 and information processing device 100) according to the present application includes an estimation unit 133 that estimates comment users and reaction users who have a predetermined degree of empathy based on the user's posting history and reaction history for the distributed content, and a selection unit 134 that selects distributed content that has a predetermined degree of compatibility with the estimated comment users and reaction users.

[0137] The information processing device according to the present application further includes an acquisition unit 132 that acquires predetermined comments and reactions linked to the distribution content.

[0138] The acquisition unit 132 acquires the user information of the user who posted the predetermined comment or input the predetermined reaction.

[0139] The estimation unit 133 estimates the user attributes of reaction users who have a predetermined degree of empathy with the comment user based on the posting history and reaction history in the user information, and estimates reaction users who belong to the user attributes.

[0140] The selection unit 134 selects an article genre of distribution content that has a predetermined compatibility level based on the posting history and reaction history in the user information, and selects distribution content that belongs to the article genre.

[0141] The information processing device according to the present application further includes a providing unit 135 that provides the distribution content having the predetermined compatibility degree to the estimated comment user.

[0142] The providing unit 135 provides the distribution content having the predetermined compatibility and to which the comment by the estimated comment user has been posted to the estimated reaction user.

[0143] By performing any one or a combination of the above-described processes, the information processing device according to the present application can efficiently guide the viewing user to the distribution content.

[0144] [8. Hardware Configuration] The terminal device 10 and the information processing device 100 according to the above-described embodiments are realized by, for example, a computer 1000 configured as shown in Fig. 9. The following description will be given taking the information processing device 100 as an example. Fig. 9 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which a calculation device 1030, a primary storage device 1040, a secondary storage device 1050, an output I / F (Interface) 1060, an input I / F 1070, and a network I / F 1080 are connected via a bus 1090.

[0145] The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The arithmetic device 1030 is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like.

[0146] The primary storage device 1040 is a memory device such as a RAM (Random Access Memory) that temporarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device in which data used by the arithmetic device 1030 for various calculations and various databases are registered, and is realized by a ROM (Read Only Memory), an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like. The secondary storage device 1050 may be an internal storage device or an external storage device. The secondary storage device 1050 may also be a removable storage medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) memory card. The secondary storage device 1050 may also be cloud storage (online storage), a NAS (Network Attached Storage), a file server, or the like.

[0147] The output I / F 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information, such as a display, a projector, a printer, etc., and is realized by a connector conforming to a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (High Definition Multimedia Interface), etc. The input I / F 1070 is an interface for receiving information from various input devices 1020, such as a mouse, a keyboard, a keypad, a button, a scanner, etc., and is realized by a USB, etc.

[0148] Furthermore, the output I / F 1060 and the input I / F 1070 may be wirelessly connected to the output device 1010 and the input device 1020, respectively. That is, the output device 1010 and the input device 1020 may be wireless devices.

[0149] The output device 1010 and the input device 1020 may be integrated into one device, such as a touch panel. In this case, the output I / F 1060 and the input I / F 1070 may also be integrated into one device as an input / output I / F.

[0150] The input device 1020 may be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0151] The network I / F 1080 receives data from other devices via the network N and sends it to the arithmetic device 1030, and also transmits data generated by the arithmetic device 1030 to other devices via the network N.

[0152] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output I / F 1060 and the input I / F 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040 and executes the loaded program.

[0153] For example, when the computer 1000 functions as the information processing device 100, the arithmetic unit 1030 of the computer 1000 executes a program loaded onto the primary storage device 1040 to realize the functions of the control unit 130. The arithmetic unit 1030 of the computer 1000 may also load a program acquired from another device via the network I / F 1080 onto the primary storage device 1040 and execute the loaded program. The arithmetic unit 1030 of the computer 1000 may also cooperate with the other device via the network I / F 1080 to call and use the functions and data of a program from another program of the other device.

[0154] [9. Other] Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. Furthermore, the above-described components include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that are within the scope of so-called equivalents. Furthermore, the above-described components can be combined as appropriate. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the spirit of the above-described embodiments.

[0155] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using a known method. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0156] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0157] For example, the information processing device 100 described above may be realized by multiple server computers, and depending on the function, the configuration can be flexibly changed, such as by calling an external platform using an API (Application Programming Interface) or network computing.

[0158] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0159] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit," etc. For example, an acquisition unit can be read as an acquisition means or an acquisition circuit. [Explanation of symbols]

[0160] 1. Information Processing Systems 10 Terminal Equipment 100 Information processing device 110 Communications Department 120 Storage section 121 User Information Database 122 Historical Information Database 123 Content Information Database 130 Control Unit 131 Distribution Department 132 Acquisition Department 133 Estimation Department 134 Selection Section 135 Provision Department

Claims

1. an estimation unit that estimates comment users and reaction users who have a predetermined degree of empathy based on user posting histories and reaction histories for the distributed content; a selection unit that selects distribution content having a predetermined compatibility with the estimated comment user and reaction user; An information processing device comprising:

2. an acquisition unit that acquires predetermined comments and predetermined reactions associated with the distribution content; The information processing apparatus according to claim 1 , further comprising:

3. The acquisition unit acquires user information of a user who posted the predetermined comment or input the predetermined reaction.

3. The information processing apparatus according to claim 2, wherein:

4. The estimation unit estimates user attributes of reaction users who have the predetermined degree of empathy for the comment user based on the posting history and reaction history in the user information, and estimates reaction users who belong to the user attributes.

4. The information processing apparatus according to claim 3,

5. The selection unit selects an article genre of distribution content having the predetermined compatibility based on a posting history and a reaction history in the user information, and selects distribution content belonging to the article genre.

5. The information processing apparatus according to claim 4,

6. a providing unit that provides distribution content having the predetermined compatibility to the estimated comment user; 6. The information processing apparatus according to claim 5, further comprising:

7. The providing unit provides the distribution content having the predetermined compatibility, to which the comment by the estimated comment user has posted, to the estimated reaction user.

7. The information processing apparatus according to claim 6,

8. An information processing method executed by an information processing device, an estimation step of estimating comment users and reaction users who have a predetermined degree of empathy based on comment histories and reaction histories for the distribution content; a selection step of selecting distribution content having a predetermined compatibility with the estimated comment user and reaction user; An information processing method comprising:

9. an estimation procedure for estimating comment users and reaction users who have a predetermined degree of empathy based on comment histories and reaction histories for the distributed content; a selection step of selecting distribution content having a predetermined compatibility with the estimated comment user and reaction user; An information processing program characterized by causing a computer to execute the above.

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