Information processing device, information processing method, and information processing program

Abstract

To provide a submission of a comment in response to a distribution content such as news to an appropriate user.SOLUTION: An information processing device according to a present invention, comprises: an estimation part that estimates an attribute of a user who proposes a submission of a comment in response to a distribution content on the basis of information related to the distribution content; and a distribution part that distributes a proposed content for proposing the submission of the comment in response to the distribution content on the basis of the distribution content and an existing comment to the user of an estimated attribute.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

[0002] Techniques for easily realizing a review request have been disclosed.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the above prior art requests other users belonging to the same group as the user to review the content shared in the group. Therefore, it cannot be said that it proposes to post comments on distribution content such as news to an appropriate user among an unspecified number of users.

[0005] The present application has been made in view of the above, and an object thereof is to propose to an appropriate user to post a comment on distribution content such as news.

Means for Solving the Problems

[0006] The information processing apparatus according to the present application is A first learning unit generates a target audience estimation model that learns the relationship between the title or content of the distributed content and the attributes of the user through machine learning, and using the target audience estimation model, distribution content From the title or content and an estimation unit that estimates the attributes of the user with respect to the distribution content and are related and proposes to the user with the estimated attributes to post a comment on the distribution content A second learning unit generates a target type estimation model that learns the relationship between the title or content of the distributed content, the attributes of the user, and the type of comment that the user wants to post, using machine learning; a determination unit that uses the target type estimation model to determine the type of comment that the user wants to post from the title or content of the distributed content and the estimated user attributes; and a generation unit that generates suggestion content that proposes posting the determined type of comment to the distributed content to the user with the estimated attributes. together with the existing comments and the distribution content on the distribution content The type determined above a comment posting The aforementionedIt is characterized by comprising a distribution unit that distributes proposed content. [Effects of the Invention]

[0007] According to one embodiment of the system, it is possible to suggest to appropriate users that they post comments on distributed content such as news. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is an explanatory diagram showing an overview of the information processing method according to the embodiment. [Figure 2] Figure 2 shows an example of the configuration of an information processing system according to the embodiment. [Figure 3] Figure 3 shows an example of the configuration of a terminal device according to this embodiment. [Figure 4] Figure 4 shows an example of the configuration of a server device according to this embodiment. [Figure 5] Figure 5 shows an example of a user information database. [Figure 6] Figure 6 shows an example of a historical information database. [Figure 7] Figure 7 shows an example of a proposal information database. [Figure 8] Figure 8 is a flowchart showing the processing procedure according to the embodiment. [Figure 9] Figure 9 shows an example of a hardware configuration. [Modes for carrying out the invention]

[0009] The following describes in detail, with reference to the drawings, embodiments for implementing the information processing device, information processing method, and information processing program according to the present application (hereinafter referred to as "embodiments"). Note that these embodiments do not limit the information processing device, information processing method, and information processing program according to the present application. Furthermore, the same parts are denoted by the same reference numerals in the following embodiments, and redundant descriptions are omitted.

[0010] [1. Overview of Information Processing Methods] First, with reference to Figure 1, an overview of the information processing method performed by the information processing device according to the embodiment will be described. Figure 1 is an explanatory diagram showing an overview of the information processing method according to the embodiment. In Figure 1, the example of suggesting that appropriate users post comments on distributed content such as news will be used for the explanation.

[0011] As shown in Figure 1, the information processing system 1 includes a terminal device 10 and a server device 100. The terminal device 10 and the server device 100 are connected to each other via a network N (see Figure 2) by wire or wireless means so that they can communicate with each other. In this embodiment, the terminal device 10 cooperates with the server device 100.

[0012] Terminal device 10 is a smart device such as a smartphone or tablet used by user U, and is a mobile terminal device capable of communicating with any server device via a wireless communication network such as 4G (Generation) or LTE (Long Term Evolution). Terminal device 10 also has a screen such as an LCD display with touch panel functionality, and accepts various operations on displayed data such as content from user U using a finger or stylus, such as tapping, sliding, and scrolling. Operations performed on the area of ​​the screen where content is displayed may also be considered operations on the content. Furthermore, 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 server device 100 is an information processing device that works in conjunction with each user U's terminal device 10 and provides each user U's terminal device 10 with API (Application Programming Interface) services for various applications (hereinafter referred to as "apps") and various data, and is implemented by a computer or cloud system.

[0014] Further, the server device 100 may be an information processing device that provides some kind of web service online to the terminal device 10 of each user U. For example, as a web service, the server device 100 may provide services such as Internet connection, search service, SNS (Social Networking Service), e-commerce (EC: Electronic Commerce), electronic payment, online game, online banking, online trading, accommodation and ticket reservation, video and music distribution, news, map, route search, route guidance, route information, operation information, weather forecast, etc. In reality, the server device 100 may cooperate with various servers that provide the above web services and mediate the web service, or be responsible for the processing of the web service.

[0015] In addition, the server device 100 can acquire user information regarding the user U. For example, the server device 100 acquires information regarding the attributes of the user U, such as the gender, age, and residential area of the user U. Then, the server device 100 stores and manages the information regarding the attributes of the user U together with the identification information (such as user ID) indicating the user U.

[0016] Further, the server device 100 acquires various types of history information (log data) indicating the actions of the user U from the terminal device 10 of the user U or from various servers etc. based on the user ID etc. For example, the server device 100 acquires a location history, which is a history of the location and time of the user U, from the terminal device 10. Also, the server device 100 acquires a search history, which is a history of search queries input by the user U, from a search server (search engine). Also, the server device 100 acquires a browsing history, which is a history of the content browsed by the user U, from a content server. Also, the server device 100 acquires a purchase history (settlement history), which is a history of the user U's product purchases and settlement processes, from an e-commerce server or a settlement processing server. Also, the server device 100 may acquire a listing history and a sales history, which are histories of the user U's listings on the marketplace, from an e-commerce server or a settlement processing server. Also, the server device 100 acquires a posting history, which is a history of the user U's posts, from a posting server or an SNS server that provides a word-of-mouth posting service. Note that the above various servers etc. may be the server device 100 itself. That is, the server device 100 may function as the above various servers etc.

[0017] [1-1. Comment Posting Proposal] In the present embodiment, the server device 100 estimates the attributes (which may also be segments or personas) of the user U who proposes to post a comment on the distributed content based on the information regarding the distributed content. Then, the server device 100 distributes proposal content for proposing to post a comment to the user U with the estimated attributes together with the distributed content.

[0018] For example, as shown in FIG. 1, the server device 100 acquires user information (attribute information, history information, etc.) regarding the user U from the terminal device 10 of the user U directly or indirectly (via other server devices etc.) (step S1).

[0019] Next, the server device 100 estimates highly relevant user attributes (user segments, user personas, etc.) from the titles and content of the distributed content such as news (also including the source, theme, and category) (step S2).

[0020] In this case, the server device 100 may use machine learning to estimate highly relevant user attributes from the titles and content of the distributed content, such as news. For example, the server device 100 takes the titles and content of the distributed content, such as news, as input data and generates an estimation model that outputs highly relevant user attributes. Note that there may be multiple user attributes output.

[0021] Alternatively, the server device 100 may use machine learning to generate an estimation model that takes pairs of titles and content of distributed content and user attributes as input data, and outputs a score (or level) related to the degree of relevance, in order to estimate user attributes that are highly relevant to the titles and content of distributed content such as news.

[0022] Alternatively, the server device 100 may use machine learning to generate an estimation model that takes a pair of the title and content of the distributed content and a score related to its relevance as input data, and outputs user attributes (or users with those user attributes).

[0023] Regarding machine learning methods, deep learning, RNN (Recurrent Neural Network), or LSTM (Long Short-Term Memory) may be used. These are merely examples, and the method is not limited to these.

[0024] Next, the server device 100 determines the type (content) of comments it wants users with highly relevant user attributes to post about the distributed content (step S3).

[0025] In this case, the server device 100 may use machine learning to estimate the type of comment that should be posted based on the title and content of the distributed content, such as news, and user attributes. For example, the server device 100 takes pairs of the title and content of the distributed content, such as news, and highly relevant user attributes as input data, and generates an estimation model that outputs the type of comment that should be posted. Note that there may be multiple types of comments output.

[0026] Next, the server device 100 generates suggestion content that proposes comment posting to users with highly relevant user attributes, based on the highly relevant user attributes and the type of comment that it wants users with those user attributes to post (step S4).

[0027] Next, the server device 100 delivers the content to users with highly relevant user attributes, along with comments on the content (existing comments posted by other users, etc.) and suggestion content that proposes posting comments (step S5).

[0028] In this case, the server device 100 may distribute information on each comment on the distributed content, such as the user attributes of the user who posted the comment (and the type of the comment), to be displayed on the screen of the user's terminal device 10.

[0029] Next, the server device 100 receives comments (comments corresponding to the type of comment that should be posted) from users with highly relevant user attributes who have received the proposed content (step S6).

[0030] For example, if the title and content of news or other distributed content are related to "job hunting," users with highly relevant user attributes include "job seekers" (currently job hunting), "those with job hunting experience" (after receiving a job offer), and "recruiters" (interviewers). In this case, users can also be classified by company, industry, and job type. Furthermore, they can be classified by categories such as "new graduate recruitment," "second-career recruitment," and "mid-career recruitment." They can also be classified by era, such as "bubble economy" or "employment ice age." Depending on each user's position and attributes, the content of comments on news or other content on the same theme is likely to differ.

[0031] [1-2. Variations] Server device 100 uses a table of "keywords x attributes x what to write". For example, server device 100 changes the content of what to write based on the combination of articles and attributes, such as "job hunting x 3 years after employment x personal experience" or "job hunting x currently job hunting x interview information".

[0032] Furthermore, the server device 100 may determine the target user attributes and the types of comments it wants to receive based on the types (content) and keywords of comments already posted to the distributed content (existing comments). For example, if the server device 100 wants comments on "3 years after employment x experiences" and "currently job hunting x interview information," but the latter is more prevalent in existing comments, it may distribute suggestion content to target users who have been employed for 3 years, proposing the posting of "experience stories" in order to increase the number of "3 years after employment x experiences" posts.

[0033] Furthermore, the server device 100 may extract requests such as "I want to hear the experiences of people who are XX" from existing comments, determine the type of comment that should be posted based on the extracted requests, and generate suggestion content to encourage users with the target user attributes to post comments that meet those requests. In this case, the server device 100 may estimate highly relevant user attributes (user segments or user personas may also be used) based on the extracted requests, or determine the type (content) of comments that should be posted.

[0034] Additionally, the server device 100 may subtly prompt users with the target user attributes to post by asking questions such as, "How does this compare to your job hunting experience?"

[0035] Furthermore, the server device 100 may distribute the suggested content only to users with the corresponding attributes. In this case, the server device 100 may not distribute the suggested content to users with user attributes other than those with highly relevant attributes (such as users with other user attributes), but instead distribute only the distributed content and comments on the distributed content. By excluding users with other attributes and distributing the suggested content only to users with the corresponding attributes, the accuracy of the content of comments posted according to user attributes can be improved.

[0036] Furthermore, the server device 100 may distribute the content by separating the comment trees according to user attributes. For example, when a user views a news article, the server device 100 may display separate comment trees for women and men. The server device 100 can also control the display, for example, by not showing the comment tree for men when a woman accesses the site.

[0037] [2. Example of an information processing system configuration] Next, the configuration of the information processing system 1, which includes the server device 100 according to the embodiment, will be described using Figure 2. Figure 2 is a diagram showing an example of the configuration of the information processing system 1 according to the embodiment. As shown in Figure 2, the information processing system 1 according to the embodiment includes a terminal device 10 and a server device 100. These various devices are connected to each other via a network N, either by wire or wireless communication. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

[0038] Furthermore, the number of devices included in the information processing system 1 shown in Figure 2 is not limited to those illustrated. For example, in Figure 2, only one terminal device 10 is shown for the sake of illustration, but this is merely an example and not limiting; there may be two or more.

[0039] Terminal device 10 is an information processing device used by user U. For example, terminal device 10 may be a smart device such as a smartphone or tablet, a feature phone, a PC (Personal Computer), a PDA (Personal Digital Assistant), a game console or AV equipment with communication functions, a car navigation system, a wearable device such as a smartwatch or head-mounted display, or smart glasses. Alternatively, terminal device 10 may be a house or building compatible with the Internet of Things (IoT), a car, home appliances, electronic devices, etc.

[0040] Furthermore, the terminal device 10 can connect to the network N via wireless communication networks such as LTE (Long Term Evolution), 4G (4th Generation), and 5G (5th Generation), or via short-range wireless communication such as Bluetooth (registered trademark) and Wi-Fi (Local Area Network), and communicate with the server device 100.

[0041] The server device 100 is, for example, a computer such as a PC or blade server, or a mainframe or workstation. The server device 100 may also be implemented through cloud computing.

[0042] [3. Example of terminal device configuration] Next, the configuration of the terminal device 10 will be explained using Figure 3. Figure 3 is a diagram showing an example of the configuration of the terminal device 10. As shown in Figure 3, the terminal device 10 comprises 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.

[0043] (Communications Section 11) The communication unit 11 is connected to the network N (see Figure 2) by wire or wireless connection and transmits and receives information to and from the server device 100 via the network N. For example, the communication unit 11 can be implemented using a NIC (Network Interface Card) or an antenna.

[0044] (Display section 12) The display unit 12 is a display device that displays various information such as location information. For example, the display unit 12 may be a liquid crystal display (LCD) or an organic electro-luminescent display (OLED). The display unit 12 may also be a touch panel display, but is not limited to this.

[0045] (Input section 13) The input unit 13 is an input device that receives various operations from the user U. For example, the input unit 13 has buttons for inputting characters, numbers, etc. The input unit 13 may also be an input / output port (I / O port) or a USB (Universal Serial Bus) port. 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 also be a microphone that receives voice input from the user U. The microphone may be wireless.

[0046] (Positioning unit 14) The positioning unit 14 receives signals (radio waves) transmitted from GPS (Global Positioning System) satellites and, based on the received signals, acquires position information (e.g., latitude and longitude) indicating the current position of the terminal device 10. In other words, the positioning unit 14 determines the position of the terminal device 10. Note that GPS is just one example of a GNSS (Global Navigation Satellite System).

[0047] Furthermore, the positioning unit 14 can determine its position using various methods other than GPS. For example, the positioning unit 14 may use various communication functions of the terminal device 10 to determine its position as an auxiliary positioning means for position correction, etc., as described below.

[0048] (Wi-Fi positioning) For example, the positioning unit 14 determines the location of the terminal device 10 by utilizing the Wi-Fi® communication function of the terminal device 10 and the communication network provided by each telecommunications company. Specifically, the positioning unit 14 determines the location of the terminal device 10 by performing Wi-Fi communication, etc., and determining the distance to nearby base stations and access points.

[0049] (Beacon positioning) Furthermore, the positioning unit 14 may determine the location using the Bluetooth® function of the terminal device 10. For example, the positioning unit 14 determines the location of the terminal device 10 by connecting to a beacon transmitter connected via the Bluetooth® function.

[0050] (Geomagnetic positioning) Furthermore, the positioning unit 14 determines the position of the terminal device 10 based on the geomagnetic pattern of the structure, which has been measured in advance, and the geomagnetic sensor provided by the terminal device 10.

[0051] (RFID positioning) Furthermore, if, for example, the terminal device 10 is equipped with an RFID (Radio Frequency Identification) tag function equivalent to that of a contactless IC card used at a train station ticket gate or in a store, or if it is equipped with a function to read RFID tags, the location where it was used will be recorded along with the information on the payment or other transactions made by the terminal device 10. The positioning unit 14 may determine the location of the terminal device 10 by acquiring such information. Alternatively, the location may be determined by an optical sensor or infrared sensor equipped in the terminal device 10.

[0052] The positioning unit 14 may, if necessary, determine the position of the terminal device 10 using one or a combination of the positioning means described above.

[0053] (Sensor unit 20) The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. The connection can 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 Figure 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.

[0054] The sensors 21-28 described above are merely examples and not limiting. In other words, the sensor unit 20 may be configured to include some of the sensors 21-28, or it may include other sensors such as humidity sensors in addition to or instead of the sensors 21-28.

[0055] The acceleration sensor 21 is, for example, a 3-axis acceleration sensor and detects the physical movement of the terminal device 10, such as its direction of movement, velocity, and acceleration. The gyro sensor 22 detects the physical movement of the terminal device 10, such as its tilt in the three axes, based on its angular velocity. The barometric pressure sensor 23 detects the atmospheric pressure around the terminal device 10, for example.

[0056] Since the terminal device 10 is equipped with the acceleration sensor 21, gyroscope 22, barometric pressure sensor 23, etc., it becomes possible to determine the position of the terminal device 10 using technologies such as pedestrian dead-reckoning (PDR) that utilize these sensors 21 to 23. This makes it possible to obtain indoor location information that is difficult to obtain with positioning systems such as GPS.

[0057] For example, a pedometer using an accelerometer 21 can calculate the number of steps, walking speed, and distance walked. Additionally, a gyroscope 22 can be used to determine the user U's direction of movement, gaze direction, and body tilt. Furthermore, the barometric pressure detected by the barometric pressure sensor 23 can be used to determine the altitude and floor number of the user U's terminal device 10.

[0058] The temperature sensor 24 detects, for example, the ambient temperature around the terminal device 10. The sound sensor 25 detects, for example, the ambient sound around the terminal device 10. The light sensor 26 detects the ambient illumination around the terminal device 10. The magnetic sensor 27 detects, for example, the Earth's magnetic field around the terminal device 10. The image sensor 28 captures an image of the area around the terminal device 10.

[0059] The aforementioned pressure sensor 23, temperature sensor 24, sound sensor 25, light sensor 26, and image sensor 28 can detect the surrounding environment and conditions of the terminal device 10 by detecting atmospheric pressure, temperature, sound, and illuminance, respectively, and by capturing images of the surroundings. Furthermore, it becomes possible to improve the accuracy of the location information of the terminal device 10 based on the surrounding environment and conditions.

[0060] (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, and various circuits. Alternatively, the control unit 30 may be composed of hardware such as an integrated circuit (ASIC) or FPGA (Field Programmable Gate Array). The control unit 30 includes a transmission unit 31, a reception unit 32, and a processing unit 33.

[0061] (Transmitter 31) The transmission unit 31 can transmit various information, such as information input by the user U using the input unit 13, various information detected by sensors 21-28 mounted on or connected to the terminal device 10, and location information of the terminal device 10 determined by the positioning unit 14, to the server device 100 via the communication unit 11.

[0062] (Receiver 32) The receiving unit 32 can receive various information provided by the server device 100, as well as requests for various information from the server device 100, via the communication unit 11.

[0063] (Processing 33) The processing unit 33 controls the entire terminal device 10, including the display unit 12. For example, the processing unit 33 can output and display various information transmitted by the transmission unit 31 and various information received from the server device 100 by the reception unit 32 to the display unit 12.

[0064] (Storage unit 40) The storage unit 40 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or by storage devices such as HDD (Hard Disk Drive), SSD (Solid State Drive), and optical discs. Various programs and various data are stored in this storage unit 40.

[0065] [4. Example of Server Device Configuration] Next, the configuration of the server device 100 according to the embodiment will be described using Figure 4. Figure 4 is a diagram showing an example of the configuration of the server device 100 according to the embodiment. As shown in Figure 4, the server device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0066] (Communications Department 110) The communication unit 110 is implemented, for example, by a NIC (Network Interface Card). The communication unit 110 is connected to the network N (see Figure 2) by wire or wireless connection.

[0067] (Storage unit 120) The storage unit 120 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or by storage devices such as HDDs, SSDs, and optical discs. As shown in Figure 4, the storage unit 120 has a user information database 121, a history information database 122, and a suggestion information database 123.

[0068] (User Information Database 121) The user information database 121 stores user information about user U. For example, the user information database 121 stores various information such as user U's attributes. Figure 5 shows an example of the user information database 121. In the example shown in Figure 5, the user information database 121 has items such as "User ID (Identifier)", "Age", "Gender", "Home", "Workplace", and "Interests".

[0069] "User ID" refers to identification information used to identify user U. Note that "User ID" may be user U's contact information (telephone number, email address, etc.) or identification information used to identify user U's terminal device 10.

[0070] Furthermore, "Age" indicates the age of user U, identified by the user ID. Note that "Age" may be information indicating user U's specific age (e.g., 35 years old), or information indicating user U's age group (e.g., 30s), or "Age" may be information indicating user U's date of birth, or information indicating user U's generation (e.g., born in the 1980s). Furthermore, "Gender" indicates the gender of user U, identified by the user ID.

[0071] Furthermore, "Home" indicates the location information of user U's home, which is identified by the user ID. In the example shown in Figure 5, "Home" is represented by an abstract code such as "LC11," but it could also be latitude and longitude information, etc. Also, for example, "Home" could be a regional name or address.

[0072] Furthermore, "Workplace" indicates the location information of the workplace (or school in the case of a student) of user U, identified by the user ID. In the example shown in Figure 5, "Workplace" is illustrated with an abstract code such as "LC12," but it may also be latitude and longitude information, etc. Also, for example, "Workplace" may be a regional name or address.

[0073] Furthermore, "Interests" indicate the interests of user U, who is identified by their user ID. In other words, "Interests" indicate the subjects of high interest to user U, who is identified by their user ID. For example, "Interests" may be search queries (keywords) that user U enters into a search engine. In the example shown in Figure 5, one "Interest" is shown for each user U, but there may be multiple interests.

[0074] For example, in the example shown in Figure 5, user U, identified by user ID "U1", is in their 20s and is male. Also, for example, user U, identified by user ID "U1", has their home address at "LC11". Furthermore, for example, user U, identified by user ID "U1", has their workplace at "LC12". Finally, for example, user U, identified by user ID "U1", is interested in "sports".

[0075] In the example shown in Figure 5, abstract values ​​such as "U1," "LC11," and "LC12" are used to illustrate the information, but it is assumed that "U1," "LC11," and "LC12" actually store specific strings, numbers, or other information. In the following diagrams relating to other information, abstract values ​​may also be used to illustrate the information.

[0076] 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 about user U's terminal device 10. In addition, the user information database 121 may store information about user U's demographic, psychographic, geographic, and behavioral attributes. For example, the user information database 121 may store information such as name, family structure, place of origin (hometown), occupation, job title, income, qualifications, type of residence (detached house, apartment, etc.), whether or not a car is owned, commuting time, commuting route, commuter pass section (station, line, etc.), frequently used stations (other than the nearest station to home / workplace), lessons / classes (location, time, etc.), hobbies, interests, and lifestyle.

[0077] (History Information Database 122) The history information database 122 stores various information related to the history information (log data) that shows the user U's actions. Figure 6 shows an example of the history information database 122. In the example shown in Figure 6, the history information database 122 has items such as "User ID", "Location History", "Search History", "Browsing History", "Purchase History", and "Posting History".

[0078] "User ID" indicates identification information used to identify user U. "Location History" indicates the location history, which is the history of user U's location and movements. "Search History" indicates the search history, which is the history of search queries entered by user U. "Browsing History" indicates the browsing history, which is the history of content viewed by user U. "Purchase History" indicates the purchase history, which is the history of purchases made by user U. "Posting History" indicates the posting history, which is the history of posts made by user U. Note that "Posting History" may include questions about user U's possessions.

[0079] For example, in the example shown in Figure 6, user U, identified by user ID "U1", moves as described in "Location History #1", searches as described in "Search History #1", views content as described in "Browsing History #1", purchases specified goods at specified stores as described in "Purchase History #1", and posts as described in "Posting History #1".

[0080] In the example shown in Figure 6, abstract values ​​such as "U1", "Location History #1", "Search History #1", "Browsing History #1", "Purchase History #1", and "Posting History #1" are used for illustration. However, it is assumed that "U1", "Location History #1", "Search History #1", "Browsing History #1", "Purchase History #1", and "Posting History #1" will actually store specific strings, numbers, and other information.

[0081] 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 usage history of user U for a specified service. The history information database 122 may also store the visit history of user U to a physical store or a facility. The history information database 122 may also store the payment history of user U using the terminal device 10 for payments (electronic payments).

[0082] (Proposal Information Database 123) The proposal information database 123 stores various information about the distributed content and the proposed content. Figure 7 shows an example of the proposal information database 123. In the example shown in Figure 7, the proposal information database 123 has items such as "distributed content," "attributes," "relevance," "type of comment," and "proposed content."

[0083] "Distributed Content" refers to identifying information used to identify the distributed content. In practice, this may include the title and content of the distributed content (distributor, theme, or category are also acceptable). "Attributes" refers to user attributes (user segments or user personas) that are highly relevant to the distributed content. Multiple user attributes are allowed. "Relevance" indicates the relationship between the distributed content and the user attributes. Relevance may be expressed as a score (level) indicating the degree of relevance. "Comment Type" indicates the type (content) of comments that users with highly relevant user attributes should post about the distributed content. "Suggested Content" refers to suggested content that proposes comments to users with highly relevant user attributes.

[0084] For example, in the example shown in Figure 7, the relationship between the delivered content "Delivery #1" and the user attribute "Attribute #1" is "Relationship #1," and this indicates that a suggestion content "Suggestion #1" is generated and delivered to users with this user attribute, suggesting the posting of a comment of type "Type #1."

[0085] In the example shown in Figure 7, abstract values ​​such as "Delivery #1," "Attribute #1," "Relevance #1," "Type #1," and "Proposal #1" are used for illustration. However, it is assumed that "Delivery #1," "Attribute #1," "Relevance #1," "Type #1," and "Proposal #1" will actually store specific strings, numbers, or other numerical information.

[0086] The proposal information database 123 may store various types of information depending on the purpose, not limited to those mentioned above. For example, the proposal information database 123 may store identification information to identify users with highly relevant user attributes. The proposal information database 123 may also store information about existing comments on the distributed content. Furthermore, the proposal information database 123 may store identification information to identify the provider (distributor) of the distributed content. In addition, the proposal information database 123 may store information about the content tree.

[0087] (Control unit 130) Returning to Figure 4, let's continue the explanation. The control unit 130 is a controller, and is realized by various programs (corresponding to an example of an information processing program) stored in the internal memory of the server device 100, such as a CPU (Central Processing Unit), MPU (Micro Processing Unit), ASIC (Application Specific Integrated Circuit), or FPGA (Field Programmable Gate Array), executing them using a memory area such as RAM as the working area. In the example shown in Figure 4, the control unit 130 has an acquisition unit 131, an estimation unit 132, a determination unit 133, a generation unit 134, a distribution unit 135, and a reception unit 136.

[0088] (Acquisition part 131) The acquisition unit 131 acquires the search query entered by user U. For example, when user U enters a search query into a search engine or the like and performs a keyword search, the acquisition unit 131 acquires the search query via the communication unit 110. In other words, the acquisition unit 131 acquires the keyword entered by user U into the search box of a search engine, website, or app via the communication unit 110.

[0089] Furthermore, the acquisition unit 131 acquires user U information about user U via the communication unit 110. For example, the acquisition unit 131 acquires identification information (such as user UID), location information, and attribute information of user U from user U's terminal device 10. The acquisition unit 131 may also acquire identification information and attribute information of user U when user U is registered. The acquisition unit 131 then registers the user U information in the user U information database 121 of the storage unit 120.

[0090] Furthermore, the acquisition unit 131 acquires various historical information (log data) indicating the user U's actions via the communication unit 110. For example, the acquisition unit 131 acquires various historical information indicating the user U's actions from the user U's terminal device 10, or from various servers based on the user UID, etc. The acquisition unit 131 then registers the various historical information in the history information database 122 of the storage unit 120.

[0091] (Estimation part 132) The estimation unit 132 estimates the attributes of user U who proposes posting comments on the distributed content, based on information about the distributed content (such as the title and content of the distributed content). At this time, the estimation unit 132 estimates attributes that are highly relevant to the information about the distributed content.

[0092] (Decision Section 133) The decision unit 133 determines the type of comment that users U with highly relevant attributes should post about the distributed content.

[0093] (Generation unit 134) The generation unit 134 generates suggestion content that proposes comment posting to user U with highly relevant attributes, based on the attributes with high relevance and the type of comment that user U with those attributes should post.

[0094] Furthermore, the generation unit 134 extracts requests for specific content from existing comments and generates suggestion content to encourage users U with the target attributes to post comments that meet those requests.

[0095] (Distribution Section 135) The distribution unit 135 distributes to user U, whose attributes are estimated, the distribution content and existing comments, along with suggestion content that proposes posting comments on the distribution content.

[0096] Furthermore, the distribution unit 135 does not distribute suggested content to users U with attributes other than those estimated, and only distributes the distributed content and existing comments.

[0097] Furthermore, the distribution unit 135 distributes the content by separating the comment trees according to the attributes of user U.

[0098] (Reception desk 136) The reception unit 136 receives comments from users U who have received the proposed content, regarding the content they have received in response to the distribution of the proposed content.

[0099] [5. Processing Procedure] Next, the processing procedure by the server device 100 according to the embodiment will be described using Figure 8. Figure 8 is a flowchart of the processing procedure according to the embodiment. Note that the processing procedure shown below is repeatedly executed by the control unit 130 of the server device 100.

[0100] As shown in Figure 8, the acquisition unit 131 of the server device 100 acquires user information (attribute information, history information, etc.) about user U via the communication unit 110 (step S101).

[0101] Next, the estimation unit 132 of the server device 100 estimates highly relevant user attributes (user segments, user personas, etc.) from the title and content of the distributed content (which can also be the source, theme, or category) (step S102).

[0102] Next, the determination unit 133 of the server device 100 determines the type (content) of comments that it wants users with highly relevant user attributes to post about the distributed content (step S103).

[0103] Next, the generation unit 134 of the server device 100 generates suggestion content that proposes comment posting to users with highly relevant user attributes, based on the highly relevant user attributes and the type of comment that users with those user attributes should post (step S104).

[0104] Next, the distribution unit 135 of the server device 100 distributes, via the communication unit 110, the distribution content, along with comments on the distribution content (existing comments posted by other users, etc.), and suggestion content that proposes posting comments to users with highly relevant user attributes (step S105).

[0105] Next, the reception unit 136 of the server device 100 receives comments (comments corresponding to the type of comments that should be posted) from users with highly relevant user attributes who have received the proposed content via the communication unit 110 (step S106).

[0106] [6. Variant Example] The terminal device 10 and server device 100 described above may be implemented in various other forms besides those of the embodiment described above. Therefore, the following describes modifications of the embodiment.

[0107] In the above embodiment, some or all of the processing performed by the server device 100 may actually be performed by the terminal device 10. For example, the processing may be completed in a standalone manner (by the terminal device 10 alone). In this case, the terminal device 10 is assumed to have the functions of the server device 100 in the above embodiment. Furthermore, in the above embodiment, since the terminal device 10 is in cooperation with the server device 100, from the perspective of the user U, it appears as if the processing of the server 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 server device 100.

[0108] For example, the terminal device 10 may execute the processing of the server device 100 in the above embodiment as processing on an installed application. That is, the processing of the server device 100 in the above embodiment may be processing on an application installed on the terminal device 10. In this case, the terminal device 10 may generate an estimation model by on-device learning. Alternatively, the terminal device 10 may cooperate with the server device 100, provide data to the server device 100, and obtain an estimation model generated by the server device 100 through machine learning. The server device 100 may generate an estimation model for each user attribute (segment, persona). Furthermore, the terminal device 10 may cooperate with the server device 100 to generate an estimation model by federated learning.

[0109] Furthermore, in the above embodiment, news and the like are merely examples of distributed content. In reality, the distributed content may include not only websites where comments (or any kind of posting) can be made, such as bulletin boards, but also social networking services (SNS), SMS (Short Message Service), messaging apps, etc. Also, the type (content) of the comments may include product or service reviews, or survey responses, etc.

[0110] [7. Effects] As described above, the information processing device (terminal device 10 and server device 100) according to the present invention includes an estimation unit 132 that estimates the attributes of a user U who proposes posting a comment on the distributed content based on information about the distributed content, and a distribution unit 135 that distributes the distributed content and existing comments, along with proposed content that proposes posting a comment on the distributed content, to the user U with the estimated attributes.

[0111] The estimation unit 132 estimates attributes that are highly relevant to the information about the distributed content.

[0112] The information processing device according to the present invention further includes a determination unit 133 that determines the type of comment that users U with highly relevant attributes should post about the distributed content.

[0113] The information processing device according to the present invention further comprises a generation unit 134 that generates suggestion content that proposes the posting of comments to user U with highly relevant attributes, based on the attributes with high relevance and the type of comments that users U with those attributes should post.

[0114] The generation unit 134 extracts requests for specific content from existing comments and generates suggestion content to encourage users U with the target attributes to post comments that meet those requests.

[0115] The information processing device according to the present invention further includes a reception unit 136 that receives comments posted on the distributed content by a user U who has received the proposed content in response to the distribution of the proposed content.

[0116] The distribution unit 135 does not distribute suggested content to users U with attributes other than those estimated, and only distributes the distributed content and existing comments.

[0117] The distribution unit 135 distributes the content by separating the comment trees according to the attributes of user U.

[0118] Through any or a combination of the above-described processes, the information processing device according to the present invention can suggest to appropriate users the posting of comments on distributed content such as news.

[0119] [8. Hardware Configuration] Furthermore, the terminal device 10 and server device 100 according to the above-described embodiment are realized by a computer 1000 having a configuration such as that shown in Figure 9. The following explanation will use the server device 100 as an example. Figure 9 is a diagram showing an example of the hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which an arithmetic unit 1030, a primary storage device 1040, a secondary storage device 1050, an output interface 1060, an input interface 1070, and a network interface 1080 are connected by a bus 1090.

[0120] The arithmetic unit 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, as well as programs read from the input device 1020, and executes various processes. The arithmetic unit 1030 can be implemented using, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), or an FPGA (Field Programmable Gate Array).

[0121] The primary storage device 1040 is a memory device, such as RAM (Random Access Memory), that temporarily stores data used by the arithmetic unit 1030 for various calculations. The secondary storage device 1050 is a storage device where data used by the arithmetic unit 1030 for various calculations and various databases are registered, and can be implemented using ROM (Read Only Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), flash memory, etc. The secondary storage device 1050 may be internal storage or external storage. The secondary storage device 1050 may also be a removable storage medium such as USB (Universal Serial Bus) memory or SD (Secure Digital) memory card. The secondary storage device 1050 may also be cloud storage (online storage), NAS (Network Attached Storage), file server, etc.

[0122] The output I / F 1060 is an interface for transmitting information to be output to output devices 1010, such as displays, projectors, and printers, and is implemented using connectors of standards such as USB (Universal Serial Bus), DVI (Digital Visual Interface), and HDMI (High Definition Multimedia Interface). The input I / F 1070 is an interface for receiving information from various input devices 1020, such as mice, keyboards, keypads, buttons, and scanners, and is implemented using, for example, USB.

[0123] Furthermore, the output interface 1060 and input interface 1070 may be wirelessly connected to the output device 1010 and input device 1020, respectively. In other words, the output device 1010 and input device 1020 may be wireless devices.

[0124] Furthermore, the output device 1010 and the input device 1020 may be integrated as a touch panel. In this case, the output I / F 1060 and the input I / F 1070 may also be integrated as an input / output I / F.

[0125] The input device 1020 may also be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), DVD (Digital Versatile Disc), or 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.

[0126] The network interface 1080 receives data from other devices via network N and sends it to the computing unit 1030, and also transmits data generated by the computing unit 1030 to other devices via network N.

[0127] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output interface 1060 and the input interface 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.

[0128] For example, when computer 1000 functions as a server device 100, the arithmetic unit 1030 of computer 1000 realizes the functions of the control unit 130 by executing a program loaded onto the primary storage device 1040. Alternatively, the arithmetic unit 1030 of computer 1000 may load a program obtained from another device via the network interface 1080 onto the primary storage device 1040 and execute the loaded program. Furthermore, the arithmetic unit 1030 of computer 1000 may cooperate with other devices via the network interface 1080 and call and use program functions, data, etc., from other programs on other devices.

[0129] [9. Other] Although embodiments of the present invention have been described above, the present invention is not limited by the content of these embodiments. Furthermore, the aforementioned components include those that can be easily conceived by those skilled in the art, those that are substantially the same, and those that fall within the so-called equivalent range. Moreover, the aforementioned components can be combined as appropriate. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the gist of the embodiments described above.

[0130] 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 by known methods. In addition, the processing procedures, specific names, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown.

[0131] Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions.

[0132] For example, the server device 100 described above may be implemented using multiple server computers, and the configuration can be flexibly changed, such as by calling external platforms via APIs (Application Programming Interfaces) or network computing depending on the function.

[0133] Furthermore, the embodiments and modifications described above can be combined as appropriate, provided that the processing content is not inconsistent.

[0134] Furthermore, the terms "section, module, unit" mentioned above can be replaced with "means" or "circuit," etc. For example, the acquisition unit can be replaced with acquisition means or acquisition circuit. [Explanation of Symbols]

[0135] 1. Information Processing System 10 Terminal devices 100 Server Devices 110 Communications Department 120 Storage section 121 User Information Database 122 History Information Database 123 Proposal Information Database 130 Control Unit 131 Acquisition Department 132 Estimation Department 133 Decision Section 134 Generation part 135 Distribution Department 136 Reception Department

Claims

1. A first learning unit that generates a target estimation model that learns the relationship between the title or content of the distributed content and the attributes of the user by machine learning, An estimation unit that uses the aforementioned target audience estimation model to estimate the attributes of users who are relevant to the distributed content based on the title or content of the distributed content, A second learning unit generates a target type estimation model that uses machine learning to learn the relationship between the title or content of the distributed content, user attributes, and the type of comment that the user wants to post. A determination unit that uses the aforementioned target type estimation model to determine the type of comment that should be posted based on the title or content of the distributed content and the estimated user attributes, A generation unit that generates suggestion content that proposes posting comments of the determined type to the distributed content to users with the estimated attributes, A distribution unit that distributes the suggested content to users with the estimated attributes, along with the distributed content and existing comments on the distributed content, and suggests posting comments of the determined type on the distributed content. An information processing device characterized by comprising:

2. The first learning unit generates a score estimation model that, when inputting a pair of the title or content of the distributed content and the user's attributes, outputs a score relating to the degree of relevance between the distributed content and the user, The estimation unit uses the score estimation model to estimate the attributes of users whose relevance score is above a threshold as the attributes of users who are relevant to the distributed content. The information processing apparatus according to feature 1.

3. The first learning unit, by machine learning, receives a pair of the title or content of the distributed content and a score relating to its relevance, and generates the target user estimation model that estimates the attributes of users who are relevant to the distributed content according to the score. The information processing apparatus according to feature 1.

4. The distribution unit distributes the content so that, for each comment on the distributed content, the attributes of the user who posted the comment and the type of the comment are displayed. The information processing apparatus according to feature 1.

5. The determination unit determines the attributes of the target user and the type of comment it wants to be posted, based on the attributes of users who have already posted to the distributed content and the type or keywords of existing comments that have already been posted. The generation unit generates suggestion content that proposes the posting of comments of a specific attribute and type to users with the target attribute, in order to increase the number of comments of that attribute and type that are lacking in existing comments and that the user would like to see posted. The information processing apparatus according to feature 1.

6. A reception unit that receives comments posted by users who have received the aforementioned proposed content in response to the distribution of the aforementioned proposed content, The information processing apparatus according to claim 1, further comprising:

7. The distribution unit will not distribute the suggested content to users with attributes other than those estimated, and will only distribute the distributed content and existing comments. The information processing apparatus according to feature 1.

8. The aforementioned distribution unit distributes the aforementioned content by separating the comment trees according to the user's attributes. The information processing apparatus according to feature 1.

9. An information processing method performed by an information processing device, The first learning step involves generating a target audience estimation model that learns the relationship between the title or content of the distributed content and the attributes of the users through machine learning, An estimation step is performed using the aforementioned target audience estimation model to estimate the attributes of users who are relevant to the distributed content based on the title or content of the distributed content. The second learning step involves generating a target type estimation model that uses machine learning to learn the relationship between the title or content of the distributed content, user attributes, and the type of comment that the user wants to post. A decision step is to determine the type of comment that should be posted based on the title or content of the distributed content and the estimated user attributes, using the aforementioned target type estimation model. A generation process for generating suggestion content that proposes posting comments of the determined type to the distributed content to users with the estimated attributes, A distribution process for distributing suggested content to users with the estimated attributes, along with the distributed content and existing comments on the distributed content, which suggests posting comments of the determined type on the distributed content. An information processing method characterized by including

10. A first learning procedure for generating a target estimation model that learns the relationship between the title or content of distributed content and the attributes of users by machine learning, An estimation procedure for estimating the attributes of users relevant to the distributed content based on the title or content of the distributed content, using the aforementioned target audience estimation model, A second learning step involves generating a target type estimation model that uses machine learning to learn the relationship between the title or content of the distributed content, user attributes, and the type of comment that the user wants to post. A decision procedure for determining the type of comment to be posted, using the aforementioned target type estimation model, based on the title or content of the distributed content and the estimated user attributes, A generation procedure for generating suggestion content that proposes posting comments of the determined type to the distributed content to users with the estimated attributes, A distribution procedure for distributing suggested content to users with the estimated attributes, along with the distributed content and existing comments on the distributed content, suggesting the posting of comments of the determined type on the distributed content; An information processing program characterized by causing a computer to execute it.

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