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

Abstract

To provide proper information according to interests of a user.SOLUTION: An information processing device comprises an estimation unit, a determination unit, and a provision unit. The estimation unit estimates whether evaluation of each of a plurality of objects in which a user is estimated to be interested among the plurality of objects in an interest space in which each of the plurality of objects is indicated by a vector according to relevance to interests of a plurality of users and the vector is updated per prescribed period is positive or negative. The determination unit determines a positive region which is a distribution region in an interest space of an object whose evaluation is positive, and a negative region which is a distribution region in an interest space of an object whose evaluation is negative, on the basis of estimation results by the estimation unit. The provision unit provides the user with information for suppressing movement of an object whose evaluation is estimated to be positive by the estimation unit to the negative region.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, various techniques for providing information to users have been proposed. For example, Patent Document 1 proposes a technique for delivering advertisements according to a quadrant to which a category in which a user has an interest belongs, out of a plurality of quadrants into which categories in which the user has an interest are classified based on factors related to commercial transactions. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-099631 Summary of the Invention [Problem to be solved by the invention]

[0004] However, while the above-mentioned conventional technology can provide information according to the quadrant to which the category in which the user is interested belongs, it does not take into account the fact that users have negative as well as positive interests, and there is room for improvement.

[0005] The present application has been made in consideration of the above, and aims to provide an information processing device, an information processing method, and an information processing program that can provide appropriate information according to a user's interests. [Means for solving the problem]

[0006] The information processing device according to the present application includes an estimation unit, a determination unit, and a provision unit. The estimation unit estimates whether each of a plurality of targets estimated to be of interest to a user has a positive or negative evaluation among a plurality of targets in an interest space in which each of the targets is represented by a vector according to relevance to the interests of a plurality of users and the vector is updated at predetermined intervals. The determination unit determines, based on the estimation result by the estimation unit, a positive region which is a distribution region in the interest space of targets with positive evaluations and a negative region which is a distribution region in the interest space of targets with negative evaluations. The provision unit provides the user with information that inhibits movement of the targets estimated by the estimation unit to the negative region. [Effects of the Invention]

[0007] According to one aspect of the embodiment, it is possible to provide appropriate information according to the user's interests. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a 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 including the information processing device according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of a user information table stored in the user information storage unit of the information processing device according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a content table stored in the content storage unit of the information processing device according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of interest space information stored in the interest space information storage unit of the information processing device according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of a subject of interest identified by the identification unit of the information processing device according to the embodiment. [Figure 7]FIG. 7 is a diagram illustrating an example of a positive region and a negative region estimated by the estimation unit of the information processing device according to the embodiment. [Figure 8] FIG. 8 is a diagram showing an example of a subject determined by the determination unit of the information processing device according to the embodiment to be a subject of interest moving from a positive area toward a negative area. [Figure 9] FIG. 9 is a diagram showing an example of a subject determined by the determination unit of the information processing device according to the embodiment to be a subject of interest moving from a negative area toward a positive area. [Figure 10] FIG. 10 is a flowchart showing a processing procedure performed by the processing unit of the information processing apparatus according to the embodiment. [Figure 11] FIG. 11 is a flowchart illustrating a user interest determination process performed by the processing unit of the information processing device according to the embodiment. [Figure 12] FIG. 12 is a flowchart showing an information providing procedure performed by the processing unit of the information processing device according to the embodiment. [Figure 13] FIG. 13 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, modes for implementing 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 respective embodiments can be appropriately combined within the scope of not causing any contradiction in the processing content. Furthermore, the same components in the following embodiments will be assigned the same reference numerals, and redundant explanations will be omitted.

[0010] [1. An example of information processing] First, an example of information processing according to the embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of information processing according to the embodiment. The information processing according to the embodiment is processing executed by an information processing device 1, and includes search processing, model generation processing, and information provision processing.

[0011] First, the search process and the model generation process will be described. n For example, the information processing device 1 has a search target database, which is a database in which search targets are indexed and stored, and executes a search process using information from the search target database, etc. For example, the information from the search target database is stored in a storage unit 11 (see FIG. 2).

[0012] As shown in FIG. 1, users U1 to U n Terminal devices 21-2 n By operating the terminal devices 21-2 n The terminal devices 21-2 perform a process of transmitting a search query to the information processing device 1. n (Steps S11 to S1 n ) where n is an integer greater than or equal to 2.

[0013] For example, in step S11, the user U1 operates the terminal device 21 to cause the terminal device 21 to execute a process of transmitting a search query from the terminal device 21 to the information processing device 1. n In this case, user U n Terminal device 2 n By operating the terminal device 2 n The terminal device 2 performs a process of transmitting a search query to the information processing device 1. n In the following, users U1 to U n When referring to each of the terminal devices 21 to 22 without distinguishing them individually, they are referred to as a user U, and n When referring to each of these without distinguishing them individually, they may be referred to as terminal devices 2.

[0014] The search query includes one or more search terms (search keywords) input by the user U into the terminal device 2. For example, if the user U inputs "sneakers" into the terminal device 2 as one or more search terms, the search query includes "sneakers." Also, if the user U inputs "women's sneakers" into the terminal device 2 as one or more search terms, the search query includes "women's sneakers." "Women's sneakers" includes the two search terms "sneakers" and "women's," separated by a space.

[0015] The information processing device 1 includes terminal devices 21-2 n The search queries sent from the respective n For example, the information processing device 1 receives a search query from the terminal device 21 in step S21, and n And terminal device 2 n Accepts search queries from.

[0016] Next, the information processing device 1 performs steps S21 to S2 n The search process is performed based on the received search query (steps S31 to S3 n For example, in step S31, the information processing device 1 executes a search process to search the search target database for an object corresponding to one or more search terms included in the search query transmitted from the terminal device 21 and accepted in step S21. n In the terminal device 2 n Step S2 n The search process is performed to search the search target database for targets corresponding to one or more search terms included in the received search query.

[0017] Next, the information processing device 1 performs steps S31 to S3 n The terminal devices 21-2 display search results that are the results of the search processing. n (Steps S41 to S4 nFor example, in step S41, the information processing device 1 transmits the search results, which are the results of the search process in step S31, to the terminal device 21. n In step S3 n The search results, which are the results of the search process, are displayed on the terminal device 2. n Send to.

[0018] Next, the information processing device 1 performs steps S21 to S2 n An interest model is generated based on the search query received (step S5). The interest model generated in step S5 is a model that receives information indicating each of a plurality of targets as input and outputs an M-dimensional vector. M is, for example, an integer in the range of 500 to 2000, but is not limited to this example. The M-dimensional vector may be represented by, for example, a distributed representation, or may be represented by a representation other than the distributed representation. Hereinafter, the M-dimensional vector will be simply referred to as a vector.

[0019] In step S5, the information processing device 1 generates an interest model, which is a trained model, by learning the characteristics of each of the multiple search terms, regarding two or more search terms that satisfy a predetermined condition as having similar characteristics. The two or more search terms that satisfy the predetermined condition are multiple search terms included in the same search query, or search terms included in multiple search queries sent from the terminal device 2 by the same user U within a predetermined time period.

[0020] The information processing device 1 performs learning using two or more search terms that satisfy predetermined conditions as learning data so that the vectors of the two or more search terms are similar to each other. The information processing device 1 can also treat search terms included in search queries sent from the terminal device 2 by the same user U as two or more search terms that satisfy the predetermined conditions, regardless of when the search queries were sent.

[0021] A search term is composed of one search keyword, but may be composed of two or more search keywords. For example, if the search query includes the character string "sneakers for women," the information processing device 1 treats "sneakers" and "women's" as different search terms, but can also treat the set of "sneakers" and "women's" as a single search term.

[0022] The information processing device 1 generates an interest model that outputs a vector (e.g., a distributed representation) from information indicating a target such as a search term using, for example, a technology of a deep structured semantic model (DSSM) that uses a long short-term memory (LSTM), which is a type of recurrent neural network (RNN), also known as a recursive neural network, for vector generation (e.g., distributed representation generation). Note that the method of generating an interest model that outputs a vector from information indicating a target is not limited to the above-mentioned example.

[0023] The information processing device 1 generates an interest model for each predetermined period TA. For example, if the predetermined period TA is one month, the information processing device 1 generates an interest model for each month of January, February, March, and so on in 2022.

[0024] Next, the information provision process will be described. The information processing device 1 estimates a plurality of targets that the user U is interested in from a plurality of targets included in the interest space that is updated every period TA (step S6). In the interest space, each of the plurality of targets is represented by a vector according to its relevance to the interests of the plurality of users U. In the interest space, the targets represented by the vectors are not limited to targets represented by search terms, and may be targets other than those represented by search terms.

[0025] In step S6, the information processing device 1 first identifies the interest range of each user U in the interest space updated for each period TA. The information processing device 1 inputs information indicating the target into the interest model generated in step S5, and performs a process for each target to obtain the target vector output from the interest model, thereby generating an interest space including the vector of each target. The information processing device 1 generates an interest space for each period TA, for example, using the interest model for each period TA.

[0026] Then, the information processing device 1 inputs the search terms contained in multiple search queries sent from the terminal device 2 by the same user U into the interest model generated in step S5, and performs a process for each search term to obtain the vector of the search terms output from the interest model.

[0027] The information processing device 1 calculates an average vector by averaging vectors of search terms included in multiple search queries sent from the terminal device 2 by the same user U, and determines the calculated average vector as the user interest vector. Then, the information processing device 1 identifies a range similar to the user interest vector as the interest range of each user U. Note that the information processing device 1 can also calculate the average vector by, for example, weighting the vector of a search term more heavily for a search term in a search query that is received more recently.

[0028] The information processing device 1 identifies the range of interests of each user U for each period TA using an interest model for each period TA. For example, the information processing device 1 identifies the range of interests for each user U for the period TA based on multiple search queries sent by the same user U from the terminal device 2 within the period TA.

[0029] Then, the information processing device 1 performs a process for estimating, for each user U, a plurality of targets included in the interest space, which are included in the interest range of the user U, as a plurality of targets in which the user U has an interest.

[0030] Next, the information processing device 1 estimates, for each user U, an evaluation of the user U for each of the plurality of subjects of interest that are the plurality of subjects that the user U is estimated to be interested in in step S6 (step S7). In step S7, the information processing device 1 estimates, as the user U's evaluation of the subjects of interest, whether the user U has a positive evaluation or a negative evaluation of the subjects of interest.

[0031] The information processing device 1 estimates whether the user U has a positive or negative evaluation of a target of interest based on the user U's behavior on various online services, etc. For example, the information processing device 1 estimates the user U's evaluation of a target of interest based on the user U's evaluation of a target in a search query sent from the terminal device 2 by the user U, the user U's evaluation of a target on a social networking service (SNS) or posting site, the user U's purchase of and evaluation of a target (e.g., a trading object such as a product or service) on an electronic commerce (EC) site, etc.

[0032] For example, if a search query sent by user U from terminal device 2 contains information such as "maritozzo is bad," the information processing device 1 infers that user U has a negative evaluation of the object "maritozzo." Furthermore, if a post by user U on an SNS or posting site or a post by another person that user U has given a favorable evaluation of contains "tapioca is delicious," the information processing device 1 infers that user U has a positive evaluation of the object "tapioca."

[0033] Next, the information processing device 1 determines a positive area and a negative area for each user U based on the estimation result of step S7 (step S8). The positive area is a distribution area in the interest space of the subjects of interest that are estimated to be positively evaluated by the user U. The negative area is a distribution area in the interest space of the subjects of interest that are estimated to be negatively evaluated by the user U.

[0034] Next, the information processing device 1 performs a process of providing, for each user U, movement suppression information that is information that suppresses movement of objects of interest that have been positively evaluated by the user U to negative areas (step S9). For example, the information processing device 1 suppresses movement of objects of interest that have been positively evaluated by the user U to negative areas by repeatedly providing the user U with information about objects of interest that are included in the positive area of ​​the user U as movement suppression information.

[0035] The movement suppression information is, for example, information that causes the user U to have positive feelings about an object of interest included in the positive area of ​​the user U. For example, if the object of interest included in the positive area of ​​the user U is "tapioca," the movement suppression information may include information about a store that sells the object "tapioca," information introducing new products of the object "tapioca," information showing a popularity ranking of the object "tapioca," and the like.

[0036] Furthermore, in a region of interest where vectors of multiple targets are updated for each period TA, it is assumed that among the targets of interest that have been positively evaluated by the user U, there is a target of interest that is moving toward a negative region of the user U. In this case, the information processing device 1 can provide the user U with information to suppress the movement of the target of interest that has been positively evaluated by the user U in the region of interest and is moving toward the negative region of the user U, to the negative region of the user U, as movement suppression information.

[0037] Furthermore, the information processing device 1 can provide the user U with information according to the movement pattern of the user U of a target of interest that has been positively evaluated by the user U in the region of interest to a negative region as movement suppression information.

[0038] For example, the information processing device 1 can provide the user U with information that provides a higher incentive for a positive interest target that is faster for the interest target of the user U to move into the negative area. For example, if the interest target that the user U has positively evaluated is "tapioca," the incentive is a discount coupon for the target "tapioca." The information processing device 1 can provide the movement suppression information with information for obtaining a discount coupon with a higher discount rate for an interest target that is faster for the interest target of the user U to move into the negative area.

[0039] Furthermore, the information processing device 1 can increase the frequency of providing movement suppression information to the user U in place of or in addition to information with a higher incentive for a subject of interest that moves the user U faster into the negative area.

[0040] In this way, the information processing device 1 provides the user U with information that suppresses movement of subjects of interest that have been positively evaluated by the user U to negative areas. This allows the information processing device 1 to provide appropriate information according to the interests of the user U.

[0041] [2. Information Processing System Configuration] 2 is a diagram showing an example of the configuration of an information processing system including an information processing device 1 according to an embodiment. As shown in FIG. 2, the information processing system 100 includes an information processing device 1 and a plurality of terminal devices 21 to 22. n The information processing device 1 and the plurality of terminal devices 21-2 n are communicably connected via a network N by wire or wirelessly.

[0042] The information processing device 1 is an information processing device capable of communicating with various devices via a predetermined network N such as the Internet, and is realized by, for example, a server device or a cloud system. For example, the information processing device 1 is connected to various other devices via the network N so as to be able to communicate with them.

[0043] The information processing device 1 also provides online services such as web services to the terminal device 2 of each user U. For example, the information processing device 1 provides online services such as SNS, e-commerce sites, posting sites, electronic payments, 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 addition to the search service and information provision service described above. The information processing device 1 can also act as an intermediary for online services by cooperating with various servers that provide the online services described above.

[0044] The terminal device 2 is an information processing device used by a user U to access content such as web pages displayed in a browser or content for applications. For example, the terminal device 2 is a desktop personal computer (PC), a notebook PC, a tablet terminal, a mobile phone, a personal digital assistant (PDA), etc. Note that the terminal device 2 is not limited to the above-mentioned examples and may be, for example, a smart watch or a wearable device.

[0045] 3. Configuration of Information Processing Device 1 The following describes an example of the functional configuration of the information processing device 1. As shown in FIG.

[0046] [3.1. Communication Unit 10] The communication unit 10 is realized by, for example, a NIC (Network Interface Card). The communication unit 10 is connected to a network N by wire or wirelessly, and transmits and receives information to and from various other devices. For example, the communication unit 10 may be connected to terminal devices 21 to 22. n and transmits and receives information between them via network N.

[0047] [3.2. Storage section 11] The storage unit 11 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 a hard disk or an optical disk. The storage unit 11 also has a search information storage unit 20, a user information storage unit 21, a content storage unit 22, and an interest space information storage unit 23.

[0048] 3.2.1. Search Information Storage Unit 20 The search information storage unit 20 stores information on a plurality of search targets provided by the information processing device 1 in the search service. For example, the search information storage unit 20 stores a search target database, which is a database in which each of a plurality of search targets is indexed and stored.

[0049] The information to be searched is, for example, information on various contents such as web pages collected by a crawler, etc. The information to be searched stored in the search information storage unit 20 is, but is not limited to, the URL (Uniform Resource Locator) and summary of the content.

[0050] 3.2.2. User Information Storage Unit 21 The user information storage unit 21 stores information about users U1 to U n 3 is a diagram showing an example of a user information table stored in the user information storage unit 21 of the information processing device 1 according to the embodiment.

[0051] As shown in FIG. 3, the user information table stored in the user information storage unit 21 includes information such as a "user ID (identifier)," "user name," "attributes," "purchase history," "search history," and "other history" for each user U. The "user ID" is identification information unique to each user U. The "user name" is information indicating the name of the user U.

[0052] "Attributes" is information indicating the attributes of user U. The attributes of user U include demographic attributes or psychographic attributes of user U. Demographic attributes are demographic attributes of user U. Psychographic attributes are attributes that indicate user U's values, lifestyle, personality, interests, etc.

[0053] In the example shown in FIG. 3, the demographic attributes of user U include information such as "gender" and "age." "Gender" is information indicating the gender of user U, and "age" is information indicating the age of user U. Note that the demographic attributes of user U further include, for example, user U's job title, job responsibilities, annual income, address, commuting route, training history, family composition, etc. User U's preferences include, for example, user U's level of interest in each of items such as clothes, travel, cars, motorcycles, computers, and lunch.

[0054] The "purchase history" includes a site purchase history, which is information about transaction objects (goods or services) purchased by the user U through services on an e-commerce site provided by the information processing device 1, and a physical store purchase history, which is information about transaction objects purchased by the user U at a physical store. The site purchase history and physical store purchase history include, for example, information about the transaction objects purchased by the user U, information about the purchase date and time, and information about the user U's evaluation of the transaction objects purchased by the user U (including comments, evaluation scores, etc.).

[0055] The "search history" is information about the search history of an online service provided by the information processing device 1 or an online service mediated by the information processing device 1 by the user U. For each search query, the "search history" includes, for example, information indicating the date and time when the search query was received by the information processing device 1, and information about one or more search terms (search keywords) included in the search query.

[0056] "Other history" is, for example, history other than purchase history and search history, and includes various histories of the user U's use of online services (information such as usage content and usage date and time). For example, "other history" includes the history of posts made by the user U to SNS or posting sites using the terminal device 2, and the history of web pages viewed by the user U.

[0057] 3.2.3. Content Storage Unit 22 The content storage unit 22 stores content provided by the information processing device 1 through online services other than search services. Fig. 4 is a diagram showing an example of a content table stored in the content storage unit 22 of the information processing device 1 according to the embodiment. In the example shown in Fig. 4, the content storage unit 22 includes a "content ID," "content," and the like for each piece of content.

[0058] "Content ID" is identification information unique to each piece of content. "Content" is information related to the content associated with the "Content ID." Specifically, the content may indicate information related to the content. For example, the content is content provided by an online service. For example, the content is content related to a portal site, a news site, an auction site, a weather forecast site, a shopping site, or a finance (stock price) site. The content may also be content related to a route search site, a map provider site, a travel site, a restaurant introduction site, a blog site, a posting site, a music distribution site, a video distribution site, or a social networking site.

[0059] For example, in Fig. 4, the content with content ID "C1" is "CO1." Note that in the example shown in Fig. 4, the content is expressed by an abstract code such as "CO1," but the content may be in a file format containing specific numerical values, specific character strings, and various information. Note that the content storage unit 22 is not limited to the above example, and may store various information depending on the purpose.

[0060] 3.2.4. Interest Space Information Storage Unit 23 The interest space information storage unit 23 stores interest space information for each predetermined period TA. The interest space information includes information such as vectors of multiple objects placed in the interest space. The interest space is an M-dimensional space. The period TA is, for example, one month, but is not limited to this example and may be, for example, one week, two weeks, or three months. The period TA may also be a period during which the number of new search queries or new search terms received by the information processing device 1 exceeds a preset threshold.

[0061] 5 is a diagram showing an example of interest space information stored in the interest space information storage unit 23 of the information processing device 1 according to the embodiment. In the example shown in FIG. 5, the interest space information stored in the interest space information storage unit 23 includes, for each object, a "object ID," a "object," and a "vector."

[0062] The "object ID" is identification information unique to each object. An "object" is an object that may be of interest to a user U and is placed in the interest space, and may belong to various categories such as shopping, travel, news, sports, entertainment, finance, games, movies, or music.

[0063] For example, in FIG. 5, the object of object ID "Q1" is "O1" and the vector is "V1." Note that in the example shown in FIG. 5, the object is expressed by an abstract code such as "O1," but the object may be represented by a specific character string or may be represented by an image, etc. Also, in the example shown in FIG. 5, the vector is expressed by an abstract code such as "V1," but the vector is an M-dimensional vector and is represented, for example, by the values ​​of the vector components of each dimension.

[0064] [3.3. Processing Unit 12] The processing unit 12 is a controller, and is realized by a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) using RAM as a work area to execute various programs (one example of an information processing program) stored in a storage device inside the information processing device 1. The processing unit 12 is also a controller, and is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0065] 2, the processing unit 12 has a receiving unit 30, a searching unit 31, a learning unit 32, an identifying unit 33, an estimating unit 34, a determining unit 35, and a providing unit 36, and realizes or executes the functions and actions of information processing described below. Note that the internal configuration of the processing unit 12 is not limited to the configuration shown in FIG. 2, and may have any other configuration as long as it performs the information processing described below.

[0066] 3.3.1. Reception unit 30 The reception unit 30 receives various requests. The reception unit 30 receives various requests from external information processing devices. For example, the reception unit 30 receives requests from each terminal device 2.

[0067] The reception unit 30 receives a search query including one or more search terms (search keywords) input by the user U from the terminal device 2 via the network N and the communication unit 10. The reception unit 30 also receives a content transmission request from the user U from the terminal device 2 via the network N and the communication unit 10. The content transmission request is a request that specifies content.

[0068] [3.3.2. Search Unit 31] The search unit 31 searches for information to be searched for that corresponds to one or more search terms included in the search query received by the reception unit 30 from among the plurality of pieces of information to be searched for that are stored in the search information storage unit 20. The search unit 31 transmits the searched information to the terminal device 2 that sent the search query as a search result via the network N and the communication unit 10.

[0069] The search unit 31 can also search for information to be searched for according to one or more search terms included in the search query received by the reception unit 30 from among a plurality of contents stored in the content storage unit 22. The search unit 31 transmits the searched contents as search results via the network N and the communication unit 10 to the terminal device 2 that sent the search query.

[0070] [3.3.3. Learning Section 32] The learning unit 32 generates an interest model for each period TA, which is a trained model that has learned the characteristics of each of a plurality of search terms, assuming that two or more search terms that satisfy predetermined conditions have similar characteristics.

[0071] The two or more search terms that satisfy the predetermined condition are multiple search terms included in the same search query, or search terms included in multiple search queries sent from the terminal device 2 by the same user U within a predetermined time period. Note that the learning unit 32 can also treat search terms included in search queries sent from the terminal device 2 by the same user U as two or more search terms that satisfy the predetermined condition, regardless of the time when the search queries were sent.

[0072] The learning unit 32 generates training data using search terms included in search queries with search dates and times within the time period TA. For example, the learning unit 32 acquires two or more search terms that satisfy predetermined conditions for each time period TA from the user information storage unit 21, and generates an interest model using the acquired two or more search terms as training data. For example, the learning unit 32 performs training using the two or more search terms that satisfy the predetermined conditions as training data, so that the vectors of the two or more search terms are similar to each other.

[0073] The learning unit 32 generates an interest model that outputs vectors from information indicating targets such as search terms, using, for example, DSSM technology, which uses LSTM, a type of RNN also known as a recursive neural network, for vector generation. Note that the method of generating an interest model that outputs vectors from information indicating targets such as search terms is not limited to the above-mentioned example, and various known techniques can be used as long as learning can be performed so that vectors indicating multiple similar targets are similar to each other.

[0074] The learning unit 32 acquires vectors for each of the multiple targets using the generated interest model for each period TA, and stores information including the acquired vectors of the multiple targets as interest space information in the interest space information memory unit 23 in the memory unit 11 for each period TA.

[0075] [3.3.4. Specification part 33] The identification unit 33 determines, as the target of interest of the user U, a target included in the range of interest of the user U in an interest space in which each of a plurality of targets is represented by a vector according to the relevance to the interests of the plurality of users U and the vector is updated for each period TA. The target of interest of the user U is a target in which the user U is estimated to be interested.

[0076] The identification unit 33 identifies, as the target of interest of user U, a target included in the range of interest of user U in an interest space in which each of multiple targets is represented by a vector according to its relevance to the interests of multiple users U and the vector is updated every period TA.

[0077] The identification unit 33, for example, identifies the interest range of each user U for each period TA using an interest model for each period TA. For example, the identification unit 33 acquires, for each user U, search terms included in all search queries sent from the terminal device 2 by the same user U within each period TA from the user information storage unit 21. Then, the identification unit 33 performs a process for each period TA in which the identification unit 33 averages vectors of the acquired search terms to calculate an average vector and determines the calculated average vector as a user interest vector for each user U. Note that the identification unit 33 can also calculate the average vector by, for example, weighting the vector of the search term more heavily the more recent the date and time it is received.

[0078] The identification unit 33 identifies, for each user U, a range similar to the user interest vector of the user U as the user U's interest range. The similarity range to the user interest vector is, for example, a range of cosine similarity determined in advance, but is not limited to this example. Note that the identification unit 33 can also identify the user U's interest range each time the number of new search queries or new search terms for the same user U exceeds a predetermined threshold, for example.

[0079] The identification unit 33 identifies, from among multiple objects in the space of interests updated for each period TA, an object included in the range of interests of the user U as an object of interest of the user U. The space of interests updated for each period TA is, for example, from October 2021 to February 2022, with the period TA being one month. In this case, the space of interests is updated sequentially each month from October 2021 to February 2022.

[0080] The interest space for each month from October 2021 to February 2022 is obtained using the interest model for each month from October 2021 to February 2022. The interest space for October 2021 is obtained by inputting information indicating each of multiple targets into the interest model for October 2021, and the interest space for February 2022 is obtained by inputting information indicating each of multiple targets into the interest model for February 2022. In addition, the interest space is updated sequentially to the state of the interest space for October 2021, the state of the interest space for November 2021, the state of the interest space for December 2021, the state of the interest space for January 2022, and the state of the interest space for February 2022.

[0081] 6 is a diagram showing an example of a subject of interest identified by the identification unit 33 of the information processing device 1 according to the embodiment. FIG. 6 shows a part of an interest space obtained by visualizing an M-dimensional interest space through reduction of the dimension. In the example shown in FIG. 6, in the interest space, a subject O 11 ,O 12 ,O 13 ,O 14 ,O 15 ,O 16 ,O 17 ,O 18 ,O 19 ,O 21 ,O 22 ,O 23 ,O 24 ,O 25 ,O 26 ,O 27 ,O 28 ,O 29 Contains:

[0082] In the example shown in Figure 6, the target O 11 ,O 12 ,O 13 ,O 14 ,O 15 ,O 16 ,O 17 ,O 18 ,O 19 is within the range of interest of the user U1. 11 ,O 12 ,O 13 ,O14 ,O 15 ,O 16 ,O 17 ,O 18 ,O 19 It is estimated that the interest of user U1 is similar to that of user U1. 21 ,O 22 ,O 23 ,O 24 ,O 25 ,O 26 ,O 27 ,O 28 ,O 29 is outside the range of interest of the user U1. 21 ,O 22 ,O 23 ,O 24 ,O 25 ,O 26 ,O 27 ,O 28 ,O 29 It is estimated that the interests of user U1 are not similar to those of user U1.

[0083] [3.3.5. Estimation section 34] The estimation unit 34 performs a process of estimating, for each user U, whether the evaluation of each subject of interest of the user U identified by the identification unit 33 in the space of interest that is updated for each period TA is positive or negative.

[0084] The estimation unit 34 estimates whether the user U has a positive or negative evaluation of the subject of interest, based on, for example, the user U's behavior in various online services provided or mediated by the information processing device 1. The estimation unit 34 estimates whether the user U's evaluation of each subject of interest is positive or negative, based on, for example, various histories included in the user information stored in the user information storage unit 21.

[0085] For example, if a past search query included in the search history of user U includes information such as "maritozzo is bad," the estimation unit 34 estimates that user U has a negative evaluation of the object "maritozzo." Furthermore, if a post by user U on an SNS or posting site or a post by another person that user U has given a favorable evaluation of includes "tapioca is delicious," the estimation unit 34 estimates that user U has a positive evaluation of the object "tapioca."

[0086] Furthermore, if there are many positive words in user U's posts about news B on the news site, the estimation unit 34 estimates that user U's evaluation of news B is positive. Furthermore, if there are many negative words in user U's posts about news B, the estimation unit 34 estimates that user U's evaluation of news B is negative.

[0087] In addition, for example, if user U frequently purchases the product "pistachio" on an e-commerce site and writes favorable reviews, the estimation unit 34 can also estimate that user U has a positive evaluation of the item "pistachio."

[0088] The estimation of whether user U's evaluation of each subject of interest is positive or negative is performed, for example, using a classification dictionary in which words contain information in which index values ​​are associated with words. In the classification dictionary, index values ​​for positive words are indicated by positive values, and index values ​​for negative words are indicated by negative values. In addition, in the classification dictionary, the higher the degree of positivity, the larger the absolute value of the index value, and the higher the degree of negativity, the larger the absolute value of the index value.

[0089] The estimation unit 34, for example, determines the index values ​​of multiple words included in the historical information of user U's subjects of interest from a classification dictionary, and can estimate user U's evaluation of each subject of interest based on the total index value of these multiple words.

[0090] For example, the estimation unit 34 estimates that the user U's evaluation of the subject of interest is positive when the sum or average of the index values ​​of multiple words is equal to or greater than a first threshold Nth1. Furthermore, the estimation unit 34 estimates that the user U's evaluation of the subject of interest is negative when the sum or average of the index values ​​of multiple words is equal to or less than a second threshold Nth2. The first threshold Nth1 is a positive value, and the second threshold Nth2 is a negative value.

[0091] The classification dictionary can be generated by machine learning such as linear regression, logistic regression, and support vector machine. The classification dictionary can also be generated using, for example, deep learning technology. For example, the classification dictionary can be generated by appropriately using various deep learning technologies such as DNN (Deep Neural Network), RNN (Recurrent Neural Network), and CNN (Convolutional Neural Network).

[0092] [3.3.6. Judgment unit 35] The determination unit 35 determines the positive area and the negative area of ​​the user U based on the estimation result by the estimation unit 34. The positive area of ​​the user U is a distribution area in the interest space of the subjects of interest that the user U has evaluated positively. The negative area of ​​the user U is a distribution area in the interest space of the subjects of interest that the user U has evaluated negatively.

[0093] 7 is a diagram showing an example of a positive region and a negative region estimated by the estimation unit 34 of the information processing device 1 according to the embodiment. As in FIG. 6, FIG. 7 shows a part of an interest space obtained by reducing the dimension of an M-dimensional interest space and visualizing it. In the example shown in FIG. 7, the object O 11 ,O 12 ,O 13 ,O 14 ,O 15 User U's evaluation of O is positive, 16 ,O 17 ,O 18,O 19 In the example shown in FIG. 7, the determination unit 35 determines whether the target O 11 ,O 12 ,O 13 ,O 14 ,O 15 The region containing O is judged as a positive region. 16 ,O 17 ,O 18 ,O 19 The region containing the above is determined to be a positive region.

[0094] The determination unit 35 can determine the positive and negative regions using, for example, various clustering methods. For example, the determination unit 35 can determine the positive and negative regions using machine learning algorithms such as a support vector machine or a k-means method. Note that the determination unit 35 only needs to be able to determine the positive and negative regions, and the determination method is not limited to the above-mentioned example, and various determination methods can be applied in the estimation unit 34.

[0095] In addition, the judgment unit 35 judges whether there are any objects of interest that have been estimated by the estimation unit 34 to be positively evaluated by the user U in the areas of interest that are updated every period TA and are moving toward a negative area.

[0096] 8 is a diagram showing an example of an object determined by the determination unit 35 of the information processing device 1 according to the embodiment to be an object of interest moving from a positive area to a negative area. As in FIG. 7, FIG. 8 shows a part of the interest space visualized by reducing the dimension of the M-dimensional interest space, and objects other than the object O15 are not shown. In the example shown in FIG. 8, the position P of the object O15 in the interest space one period ago is 15 (t -1 ) and the position P of object O15 in the latest interest space. 15 (t0) is shown. Object O15 is an example of an object of interest.

[0097] As shown in Figure 8, the position P 15 (t -1 ) was in the positive area in the interest space one period ago, but has moved out of the positive area and toward the negative area in the latest interest space. In this case, the determination unit 35 determines that the interest object O15 is the one that has moved from the positive area toward the negative area among the interests estimated by the estimation unit 34 to be positive for user U. Note that if the period TA is one month and the latest interest space is the interest space for February 2022, the interest space one period ago is, for example, the interest space for January 2022.

[0098] Furthermore, for example, the determination unit 35 can determine the manner in which a target of interest, which is estimated by the estimation unit 34 to be positively evaluated by the user U, moves from a positive area to a negative area in the area of ​​interest updated for each period TA. The manner of movement is specified by, for example, the speed and direction of movement.

[0099] In addition, the judgment unit 35 judges whether there are any objects of interest that the estimation unit 34 has estimated as having a negative evaluation by the user U in the areas of interest that are updated every period TA, and that are moving toward a positive area.

[0100] 9 is a diagram showing an example of an object determined by the determination unit 35 of the information processing device 1 according to the embodiment to be an object of interest moving from a negative area to a positive area. As in FIG. 7, FIG. 9 shows a part of the interest space visualized by reducing the dimension of the M-dimensional interest space, and objects other than the object O19 are not shown. In the example shown in FIG. 9, the position P of the object O19 in the interest space one period ago is 19 (t -1 ) and the position P of the object O19 in the latest interest space. 19 (t0) is shown. Object O19 is an example of an object of interest.

[0101] As shown in Figure 9, the position P 19 (t -1 ) was in the negative area in the interest space one period ago, but has moved out of the negative area and toward the positive area in the latest interest space. In this case, the determination unit 35 determines that the interest object O19 is the one that has moved from the negative area toward the positive area among the interests estimated by the estimation unit 34 to have a negative evaluation by user U. Note that if the period TA is one month and the latest interest space is the interest space for February 2022, the interest space one period ago is, for example, the interest space for January 2022.

[0102] Furthermore, the determination unit 35 determines the movement pattern of the target of interest that is moving from a negative area to a positive area among the target of interest that is estimated to have been negatively evaluated by the estimation unit 34 in the target of interest that is updated every period TA. The movement pattern is specified by, for example, the speed and direction of movement.

[0103] [3.3.7.Providing Department 36] The providing unit 36 ​​provides the user U with movement suppression information, which is information that suppresses movement of an object of interest that the estimation unit 34 estimates to be a positive evaluation of the user U in the area of ​​interest that is updated every period TA, to a negative area. The movement suppression information is provided to the user U by transmitting it from the providing unit 36 ​​to the terminal device 2 via the communication unit 10 and the network N. This allows the providing unit 36 ​​to provide appropriate information according to the interests of the user U. Hereinafter, an object of interest that the estimation unit 34 estimates to be a positive evaluation of the user U may be referred to as a positive object.

[0104] The movement suppression information is, for example, information that makes the user U feel positive about the positive object. For example, if the positive object is "tapioca," the movement suppression information may be information about a store that sells the object "tapioca," information introducing new products for the object "tapioca," information showing the popularity ranking of the object "tapioca," and the like.

[0105] Also, assume that the search query sent by the user U using the terminal device 2 includes a search term indicating a positive object. In this case, the information processing device 1 can set, as the movement inhibition information, information that causes the user U to have positive feelings toward the positive object indicated in the search query as a search result corresponding to the search query.

[0106] Furthermore, the movement suppression information is not limited to information about a positive target. For example, it may be information about a target that shares at least some of the attributes of the positive target. The attributes of the target include various attributes corresponding to the target, such as the category, usage scenario, specifications (such as the specifications or functions of the transaction target), the date of appearance of the target (such as the release date of the product), problems that can be solved by the target, and human concerns that can be solved by the target.

[0107] Furthermore, the providing unit 36 ​​provides the user U with information that suppresses the movement of positive objects that are moving toward the negative area to the negative area as movement suppression information. For example, the providing unit 36 ​​can suppress the movement of positive objects to the negative area by repeatedly providing the user U with information about positive objects that are moving toward the negative area as movement suppression information. This allows the providing unit 36 ​​to provide appropriate information in response to changes in the interests of the user U.

[0108] The providing unit 36 ​​can also provide information according to the movement mode of the positive target to the negative area as movement suppression information to the user U. For example, the providing unit 36 ​​can provide more movement suppression information or provide movement suppression information that increases the incentive that the user U receives as the speed of movement to the negative area increases or the direction of movement to the negative area increases.

[0109] For example, if the target that the user U has positively evaluated is "tapioca," the incentive is a discount coupon for the target "tapioca." The providing unit 36 ​​can set the movement suppression information as information for obtaining a discount coupon with a higher discount rate for a target that the user U moves to the negative area at a faster rate.

[0110] The information for suppressing the movement of a positive object moving toward a negative area to a negative area is not limited to information about the positive object. For example, if there are multiple positive objects moving toward a negative area, the providing unit 36 ​​can use information according to the commonality of the attributes of these multiple positive objects as the movement suppression information.

[0111] Information corresponding to the commonality of attributes of multiple positive objects is, for example, information about the common category when the attributes of multiple positive objects have a common category, such as information about the ranking of objects belonging to the common category.

[0112] Furthermore, the providing unit 36 ​​provides the user U with movement promotion information, which is information that encourages movement of an object of interest that the estimation unit 34 estimates to be negatively rated by the user U in the area of ​​interest that is updated every period TA, from a negative area to a positive area. The movement promotion information is, for example, information that makes the user U feel positive about an object that the estimation unit 34 estimates to be negatively rated by the user U. Hereinafter, an object of interest that the estimation unit 34 estimates to be negatively rated by the user U may be referred to as a negative object.

[0113] For example, suppose the positive object is "tapioca" and the negative object is "maritozzo," and a user U who likes the object "tapioca" is likely to like the object "maritozzo." In this case, the travel promotion information may be information indicating that a user U who likes the object "tapioca" is likely to like the object "maritozzo." The travel promotion information may also be information about a discount coupon when purchasing the object "tapioca" and the object "maritozzo" as a set.

[0114] Furthermore, the providing unit 36 ​​provides information that promotes the movement of negative objects of interest that are moving toward the positive area to the positive area as movement promotion information to the user U. For example, the providing unit 36 ​​promotes the movement of negative objects to the positive area by repeatedly providing the user U with information about negative objects that are moving toward the positive area as movement promotion information.

[0115] Furthermore, the providing unit 36 ​​provides information according to the movement mode of the negative object to the positive area as movement promotion information to the user U. For example, the providing unit 36 ​​can provide more movement promotion information or provide movement promotion information that gives the user U a higher incentive as the speed of movement to the positive area slows or the direction of movement to the positive area deviates from the direction toward the center of the positive area.

[0116] The providing unit 36 ​​can provide the movement restriction information and the movement promotion information to the user U in a push manner or in a pull manner. For example, the providing unit 36 ​​can display the movement restriction information and the movement promotion information as a pop-up on the terminal device 2 using an application installed on the terminal device 2, or can send the information to the email address of the user U by email. Furthermore, when the user U accesses the information processing device 1 using the terminal device 2, the providing unit 36 ​​can provide the movement restriction information and the movement promotion information to the user U by transmitting the movement restriction information and the movement promotion information to the terminal device 2. Note that the method of providing the movement restriction information and the movement promotion information to the user U is not limited to these methods.

[0117] [4. Processing Procedure] Next, a processing procedure by the information processing device 1 according to the embodiment will be described with reference to Fig. 10. Fig. 10 is a flowchart showing a processing procedure by the processing unit 12 of the information processing device 1 according to the embodiment.

[0118] 10, the processing unit 12 of the information processing device 1 determines whether or not it is time to perform a learning process for the interest model (step S10). The learning process for the interest model is, for example, a timing that occurs every period TA, but is not limited to this example.

[0119] When the processing unit 12 determines that it is time to start the learning process of the interest model (step S10: Yes), it generates the interest model (step S11). Then, the processing unit 12 generates interest space information using the interest model generated in step S11, and stores the generated interest space information in the storage unit 11 (step S12).

[0120] When the processing of step S12 is completed or when it is determined that the timing for the learning process of the interest model has not come (step S10: No), the processing unit 12 determines whether the timing for determining user interests has come (step S13). The timing for determining user interests is, for example, a timing that occurs every period TA, but is not limited to this example.

[0121] When the processing unit 12 determines that the time has come to determine user interests and concerns (step S13: Yes), the processing unit 12 executes a user interests and concerns determination process (step S14). The process of step S14 is the process of steps S20 to S24 shown in FIG. 11, which will be described in detail later.

[0122] When the processing of step S14 is completed or when it is determined that the timing for determining user interests has not come (step S13: No), the processing unit 12 determines whether the timing for determining information provision target has come (step S15). The timing for determining information provision target is the timing after the position of interest of the user U is identified in step S14. For example, the timing for determining information provision target is the timing immediately after the position of interest of the user U is identified in step S14 or the timing when the user U accesses the information processing device 1 using the terminal device 2 after the position of interest of the user U is identified in step S14.

[0123] When it is determined that the timing for determining whether or not to provide information has arrived (step S15: Yes), the processing unit 12 performs information provision processing (step S16). The processing in step S16 is the processing in steps S30 and S31 shown in FIG. 12, and will be described in detail later.

[0124] When the processing of step S16 is completed or when it is determined that the timing for determining whether or not the information subject has been provided has arrived (step S15: No), the processing unit 12 determines whether or not the timing for ending the operation has arrived (step S17). The processing unit 12 determines that the timing for ending the operation has arrived when, for example, the power supply of the information processing device 1 is turned off.

[0125] If the processing unit 12 determines that the operation end time has not yet arrived (step S17: No), it proceeds to step S10, and if it determines that the operation end time has arrived (step S17: Yes), it terminates the processing shown in Figure 10.

[0126] 11 is a flowchart showing the procedure of a user interest determination process performed by the processing unit 12 of the information processing device 1 according to the embodiment. As shown in FIG. 11, the processing unit 12 identifies the interest range of each user U (step S20).

[0127] Next, the processing unit 12 identifies, for each user U, objects included in the range of interests of the user U as objects of interest of the user U (step S21). Then, the processing unit 12 estimates, for each user U, an evaluation by the user U of each object of interest identified in step S21 (step S22).

[0128] Next, the processing unit 12 determines the positive and negative areas of the user U based on the user U's evaluation of each of the objects of interest estimated in step S22 (step S23), determines the movement state of the objects of interest (step S24), and terminates the processing shown in Figure 11.

[0129] Fig. 12 is a flowchart showing an information provision procedure by the processing unit 12 of the information processing device 1 according to the embodiment. As shown in Fig. 12, the processing unit 12 provides movement restriction information to the user U (step S30), and further provides movement promotion information to the user U (step S31), and ends the process shown in Fig. 12.

[0130] [5. Modifications] The above-described information processing device 1 may be implemented in various different forms other than the above-described embodiment, so other embodiments of the information processing device 1 will be described below.

[0131] The processing unit 12 of the information processing device 1 can generate multiple types of interest models. For example, the processing unit 12 can generate an interest model for each region and form an interest space for each region based on the interest model for each region. For example, the processing unit 12 can generate an interest model for each region by learning the characteristics of each of multiple search terms, assuming that two or more search terms that satisfy predetermined conditions among multiple search terms used by multiple users U in the target region have similar characteristics.

[0132] Furthermore, the processing unit 12 can form an interest space for each attribute of the user U based on an interest model for each attribute of the user U. For example, the processing unit 12 can generate an interest model for each specific attribute by learning the characteristics of each of a plurality of search terms, regarding two or more search terms that satisfy a predetermined condition among a plurality of search terms used by a plurality of users U having a specific attribute as having similar characteristics.

[0133] Furthermore, the processing unit 12 generates an interest model for a period TA based on a plurality of search queries sent by a plurality of users U from a plurality of terminal devices 2 within the period TA, but is not limited to this example. For example, the processing unit 12 can also generate an interest model for the latest period TA based on a plurality of search queries sent by a plurality of users U from a plurality of terminal devices 2 within the period TA in a period including the period TA P periods before and the latest period TA. P is an integer of 1 or greater.

[0134] The processing unit 12 can also identify the range of interests of each user U for each period TB based on multiple search queries sent by the same user UA from the terminal device 2 during the period TB. The period TB is, for example, longer or shorter than the period TA. Note that the period TB may be a period during which the number of search queries or new search terms newly received by the information processing device 1 from the same user U becomes equal to or exceeds a preset threshold.

[0135] [6. Hardware Configuration] The information processing device 1 according to the embodiment described above is realized by, for example, a computer 80 configured as shown in Fig. 13. Fig. 13 is a hardware configuration diagram showing an example of the computer 80 that realizes the functions of the information processing device 1 according to the embodiment. The computer 80 has a CPU 81, a RAM 82, a ROM (Read Only Memory) 83, an HDD (Hard Disk Drive) 84, a communication interface (I / F) 85, an input / output interface (I / F) 86, and a media interface (I / F) 87.

[0136] The CPU 81 operates and controls each part based on programs stored in the ROM 83 or the HDD 84. The ROM 83 stores a boot program executed by the CPU 81 when the computer 80 starts up, programs that depend on the hardware of the computer 80, and the like.

[0137] The HDD 84 stores programs executed by the CPU 81, data used by such programs, etc. The communication interface 85 receives data from other devices via the network N (see FIG. 2) and sends it to the CPU 81, and transmits data generated by the CPU 81 to other devices via the network N.

[0138] The CPU 81 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse, via the input / output interface 86. The CPU 81 acquires data from the input devices via the input / output interface 86. The CPU 81 also outputs generated data to the output devices via the input / output interface 86.

[0139] The media interface 87 reads a program or data stored in a recording medium 88 and provides it to the CPU 81 via the RAM 82. The CPU 81 loads the program or data from the recording medium 88 onto the RAM 82 via the media interface 87 and executes the loaded program. The recording medium 88 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0140] The CPU 81 of the computer 80 executes the programs loaded onto the RAM 82 to implement the functions of the processing unit 12. In addition, the HDD 84 stores data in the storage unit 11. The CPU 81 of the computer 80 reads and executes these programs from a recording medium 88, but as another example, these programs may be acquired from another device via the network N.

[0141] [7. Other] Furthermore, among the processes described in the above-mentioned embodiments and variations, 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 known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above-mentioned 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.

[0142] 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.

[0143] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0144] [8. Effects] As described above, the information processing device 1 according to the embodiment includes an estimation unit 34, a determination unit 35, and a provision unit 36. The estimation unit 34 estimates whether the evaluation of each of a plurality of targets in which the user U is estimated to have an interest is positive or negative among the plurality of targets in an interest space in which each of the plurality of targets is represented by a vector according to the relevance to the interests of a plurality of users U and the vector is updated at predetermined intervals. Based on the estimation result by the estimation unit 34, the determination unit 35 determines a positive region, which is a distribution region in the interest space of targets with positive evaluations, and a negative region, which is a distribution region in the interest space of targets with negative evaluations. The provision unit 36 ​​provides the user U with information that suppresses movement of the targets estimated by the estimation unit 34 to the negative region. This allows the information processing device 1 to provide appropriate information in response to changes in the user U's interests, etc.

[0145] Furthermore, the providing unit 36 ​​provides the user U with information that suppresses the movement of an object that is moving toward a negative area among the objects that have been estimated to have a positive evaluation by the estimation unit 34. This allows the information processing device 1 to provide more appropriate information in accordance with changes in the interests of the user U.

[0146] Furthermore, the providing unit 36 ​​provides the user U with information according to the movement pattern to the negative area estimated to have a positive evaluation by the estimation unit 34 as information for suppressing movement to the negative area. This allows the information processing device 1 to provide more appropriate information according to changes in the interests of the user U.

[0147] Furthermore, the providing unit 36 ​​provides the user U with information that encourages the user U to move from the negative area to the positive area of ​​the target that has been estimated to have a negative evaluation by the estimation unit 34. This allows the information processing device 1 to provide more appropriate information in response to changes in the user U's interests and concerns.

[0148] Furthermore, the providing unit 36 ​​provides the user U with information that encourages the movement of objects that are moving toward a positive area among the objects that have been estimated to have a negative evaluation by the estimation unit 34 to the positive area. This allows the information processing device 1 to provide more appropriate information in accordance with changes in the interests of the user U.

[0149] Furthermore, the providing unit 36 ​​provides the user U with information according to the movement of the target estimated to have a negative evaluation by the estimation unit 34 to the positive area as information encouraging the movement to the positive area. This allows the information processing device 1 to provide more appropriate information according to changes in the user U's interests, etc.

[0150] The interest space is formed based on a trained model that has learned the characteristics of each of a plurality of search terms, regarding two or more search terms that satisfy predetermined conditions among a plurality of search terms used by a plurality of users U as having similar characteristics. This allows the information processing device 1 to provide more appropriate information in response to changes in the interests of the user U.

[0151] The information processing device 1 also includes a learning unit 32 that generates a trained model using multiple search terms used by multiple users U. This allows the information processing device 1 to more appropriately provide information on subjects that may interest the users U.

[0152] The above describes the embodiments of the present application in detail based on the drawings, but this is merely an example, and the present invention can be implemented in other forms that include the embodiments described in the Disclosure of the Invention section and that have been modified and improved in various ways based on the knowledge of those skilled in the art.

[0153] 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]

[0154] 1. Information processing equipment 2,21~2 n terminal device 10. Communications Department 11 Storage section 12 Processing section 20 Search information storage unit 21 User information storage unit 22 Content storage unit 23 Interest spatial information storage unit 30 Reception 31 Search Section 32 Learning Department 33 Specific part 34 Estimation part 35 Judgment section 36 Providing Department 100 Information Processing Systems N Network

Claims

1. An estimation unit that estimates whether an evaluation of each of a plurality of objects that a user is estimated to be interested in among a plurality of objects in an interest space in which each of the plurality of objects is represented by a vector and the vector is updated at predetermined intervals is positive or negative; A determination unit that determines a positive area, which is a distribution area in the space of interests of objects whose evaluation is positive, and a negative area, which is a distribution area in the space of interests of objects whose evaluation is negative, based on the estimation result by the estimation unit; a provision unit that provides the user with information that inhibits movement of the object whose evaluation is estimated to be positive by the estimation unit to the negative area, The interest space is The database includes a plurality of vectors obtained by inputting information on the plurality of targets into a trained model that has learned the characteristics of two or more search terms that satisfy predetermined conditions among a plurality of search terms used by a plurality of users, assuming that the search terms have similar characteristics.

1. An information processing device comprising:

2. The providing unit and providing the user with information to suppress movement of an object, which is moving toward the negative area among the objects whose evaluations are estimated to be positive by the estimation unit, to the negative area.

2. The information processing apparatus according to claim 1, wherein:

3. The providing unit Provide the user with information according to a movement pattern of the object whose evaluation is estimated to be positive by the estimation unit to the negative area as information to suppress movement to the negative area.

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

4. The providing unit Provide the user with information that encourages the target, the evaluation of which has been estimated to be negative by the estimation unit, to move from the negative area to the positive area.

4. The information processing device according to claim 1, wherein the information processing device is a computer.

5. The providing unit and providing the user with information to encourage a target, which is moving toward the positive area among targets whose evaluations have been estimated to be negative by the estimation unit, to move toward the positive area.

5. The information processing apparatus according to claim 4,

6. The providing unit Provide the user with information according to a movement mode of the object whose evaluation has been estimated to be negative by the estimation unit to the positive area as information to encourage movement to the positive area.

6. The information processing apparatus according to claim 5,

7. a learning unit that generates the trained model using the plurality of search terms used by the plurality of users; 7. The information processing device according to claim 1, wherein:

8. A computer-implemented information processing method, comprising: an estimation step of estimating whether an evaluation of each of a plurality of objects in which the user is estimated to have an interest among the plurality of objects in an interest space in which each of the plurality of objects is represented by a vector and the vector is updated every predetermined period is positive or negative; A determination process for determining a positive region, which is a distribution region in the space of interests of objects whose evaluation is positive, and a negative region, which is a distribution region in the space of interests of objects whose evaluation is negative, based on the estimation result by the estimation process; a providing step of providing the user with information that inhibits movement of the object whose evaluation is estimated to be positive by the estimation step to the negative area, The interest space is The database includes a plurality of vectors obtained by inputting information on the plurality of targets into a trained model that has learned the characteristics of two or more search terms that satisfy predetermined conditions among a plurality of search terms used by a plurality of users, assuming that the search terms have similar characteristics.

1. An information processing method comprising:

9. An estimation procedure for estimating whether an evaluation of each of a plurality of objects in an interest space in which the plurality of objects are represented by vectors and the vectors are updated at predetermined intervals and in which the evaluation of each of a plurality of objects in which the user is estimated to have an interest is positive or negative; A determination procedure for determining a positive region, which is a distribution region in the space of interests of objects whose evaluation is positive, and a negative region, which is a distribution region in the space of interests of objects whose evaluation is negative, based on the estimation result by the estimation procedure; a providing step of providing the user with information that inhibits movement of the object, the evaluation of which is estimated to be positive by the estimation step, to the negative area; The interest space is The database includes a plurality of vectors obtained by inputting information on the plurality of targets into a trained model that has learned the characteristics of two or more search terms that satisfy predetermined conditions among a plurality of search terms used by a plurality of users, assuming that the search terms have similar characteristics. An information processing program characterized by:

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