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

Abstract

To properly provide information on an object in which a plurality of users may be interested in common.SOLUTION: An information processing device comprises an estimation unit and a provision unit. The estimation unit estimates differences in recognition to a specific object among two or more users on the basis of differences between two or more users at the past point of time at which interests of the two or more users and the specific object were similar in an interest space in which each of a 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. The provision unit provides at least one user among the two or more users with information according to the differences in recognition estimated by the estimation unit.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, there is room for improvement in the above-mentioned conventional technology, which provides information depending on the quadrant, and there is room for improvement in terms of providing information flexibly, and it is desired to appropriately provide information on subjects that may be of common interest to multiple users.

[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 appropriately provide information on subjects that multiple users may have a common interest in. [Means for solving the problem]

[0006] The information processing device according to the present application includes an estimation unit and a provision unit. The estimation unit estimates a misrecognition between two or more users regarding a specific object based on differences between the interests of the two or more users at past times when the interests of the two or more users were similar to the specific object in an interest space in which each of a plurality of objects is represented by a vector according to relevance to the interests of the plurality of users and the vector is updated at predetermined intervals. The provision unit provides information corresponding to the misrecognition estimated by the estimation unit to at least one of the two or more users. [Effects of the Invention]

[0007] According to one aspect of the embodiment, it is possible to provide information on a subject that may be of common interest to multiple users in an appropriate manner. [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 past time point when the interest identified by the second identification unit of the information processing device according to the embodiment was similar to a specific target. [Figure 7] FIG. 7 is a diagram illustrating an example of a process of estimating a misrecognition of a specific target between two or more specific target users by an estimation unit of the information processing device according to the embodiment. [Figure 8]FIG. 8 is a flowchart showing a processing procedure performed by the processing unit of the information processing device according to the embodiment. [Figure 9] FIG. 9 is a flowchart showing an information providing procedure performed by the processing unit of the information processing device according to the embodiment. [Figure 10] FIG. 10 is a hardware configuration diagram showing 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 nTerminal 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 targets 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 n For 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 determines a past time point when the interests of two or more users U were similar to a specific target in the interest space updated every period TA (step S6).

[0025] In step S6, the information processing device 1 first identifies the interest ranges of each of two or more users U in an interest space that is updated every period TA. In the interest space, each of multiple targets is represented by a vector according to the relevance to the interests of the multiple 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Furthermore, the information processing device 1 identifies a plurality of users U having a predetermined relationship as the above-mentioned two or more users U. The two or more users U are a plurality of users U having a predetermined relationship.

[0031] The predetermined relationship is, for example, a relationship in which users U communicate with each other. The communication is a conversation between users U using text or voice, and is carried out, for example, by voice call, chat, email, or video conference. The information processing device 1 provides a voice call service, chat service, email service, video conference service, or the like, and can identify two or more users U who communicate with each other based on information obtained from these services.

[0032] The information processing device 1 can also identify two or more users U who are set in the information processing device 1 as a family such as a married couple or a parent and child, as multiple users U having a predetermined relationship. The information processing device 1 can also determine two or more users U who are presumed to have acted together in the past, based on location information detected by each of the multiple terminal devices 2 and transmitted from each of the multiple terminal devices 2, and identify the determined two or more users U as multiple users U having a predetermined relationship.

[0033] Furthermore, the information processing device 1 determines, as the specific target, an object that has been similar to the interests of two or more users U who have a predetermined relationship in the interest range updated for each period TA. Then, the information processing device 1 determines a past time when the interests of two or more users U who have a predetermined relationship were similar to the specific target. Note that the specific target may be information that has been predetermined by the user of the information processing device 1, etc.

[0034] Next, the information processing device 1 determines an inter-user time difference, which is the difference between the two or more users U in the past when the specific target was similar to the interests of each of the two or more users U (step S7). In step S7, if the specific target was previously included in the interest range of the user U, the information processing device 1 determines that the specific target was similar to the interest range of the user U in the past.

[0035] In step S7, the information processing device 1 determines the latest time among past times at which the interests of the user U were similar to the specific target. Then, the information processing device 1 can determine the difference between the latest times between two or more users U as the inter-user time difference.

[0036] Next, the information processing device 1 estimates a difference in perception of a specific target between two or more users U based on the time difference between users determined in step S7 (step S8). Then, the information processing device 1 provides information according to the difference in perception estimated in step S8 to at least one user U of the two or more users U (step S9).

[0037] For example, two or more users U having a predetermined relationship are users U1, U2, U3, U4, U5, U6, U7, U8, U9, U10, U11, U12, U13, U14, U15, U16, U17, U18, U19, U20, U21, U22, U23, U24, U25, U26, U27, U28, U29, U30, U3 n and user U1 and user U n Assume that both of the interests of the user and the serial novel B in the monthly magazine A have been similar in the past to the serial novel B in the monthly magazine A. In this case, the information processing device 1 determines that the serial novel B in the monthly magazine A is the specific target.

[0038] In addition, the latest interest space of user U1 that was similar to serialized novel B in monthly magazine A in the past was the interest space three periods ago, TA3 (= TA × 3), and user U n Let us assume that the interest space one period TA1 (= TA × 1) ago is the latest interest space among interest spaces that were similar to serialized novel B in monthly magazine A in the past. In this case, the information processing device 1 determines that the time difference between users is two periods TA2 (= TA3 - TA1).

[0039] Then, the information processing device 1 presumes that the user U1 is aware of the contents of the serialized novel B in the monthly magazine A up to three periods TA3 ago, and determines the contents of the serialized novel B in the monthly magazine A up to one period TA1 ago as the user U1 is aware of the contents of the serialized novel B in the monthly magazine A up to one period TA1 ago. n In this way, the information processing device 1 estimates that the users U1 and U2 are aware of the time difference between the users. n Estimate the differences in perception of a particular subject between the two groups.

[0040] Then, the information processing device 1 receives the user U1, U n The information corresponding to the difference in recognition of a specific object between users U1 and U2 is n For example, the information processing device 1 may provide to the user U1 information that the user U1 may only be aware of the contents of the serialized novel B in the monthly magazine A up to three periods TA3 ago.n The information processing device 1 also provides the contents of the serialized novel B in the monthly magazine A up to one period TA1 to the user U. n It is also possible to provide user U1 with information that the person in question may be aware of the information.

[0041] In this way, the information processing device 1 estimates differences in perception of a specific target between two or more users U based on differences between the two or more users U at past times when the interests of the two or more users U were similar to the specific target. Then, the information processing device 1 provides information indicating the estimated differences in perception to at least one user U of the two or more users U. This allows the information processing device 1 to appropriately provide information on targets that multiple users U may have a common interest in.

[0042] [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.

[0043] 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.

[0044] 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 (Social Networking Service), electronic commerce (EC) 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.

[0045] 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.

[0046] 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.

[0047] [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.

[0048] [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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] "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.

[0054] 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.

[0055] 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.).

[0056] 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.

[0057] "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.

[0058] 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.

[0059] "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.

[0060] 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.

[0061] 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.

[0062] 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."

[0063] 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.

[0064] 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.

[0065] [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).

[0066] 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, and a providing unit 35, 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 be any other configuration that performs the information processing described below.

[0067] 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.

[0068] 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.

[0069] [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.

[0070] 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.

[0071] [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.

[0072] 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.

[0073] The learning unit 32 generates training data using search terms included in search queries whose search time is 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.

[0074] 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.

[0075] 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.

[0076] [3.3.4. Specification part 33] The identification unit 33 identifies a past time when the interests of two or more users U were similar to a specific target in an interest space in which each of multiple targets is represented by a vector according to the relevance to the interests of multiple users U and the vector is updated for each period TA. The identification unit 33 includes a first identification unit 40 and a second identification unit 41.

[0077] [3.3.4.1. 1st specific part 40] The first identification unit 40 identifies, as an identification target user, each of two or more users U having a predetermined relationship among the multiple users U. The predetermined relationship is, for example, a relationship in which the users communicate with each other.

[0078] The communication is a conversation between users U using text or voice, and is carried out, for example, by voice call, chat, email, video conference, etc. The first identification unit 40 can identify two or more users U who communicate with each other based on the usage history of the users U in the voice call service, chat service, email service, or video conference service.

[0079] In addition, the first identification unit 40 can also identify two or more users U who are set as a family such as a married couple or a parent and child as multiple users U having a predetermined relationship based on the user information stored in the memory unit 11.

[0080] In addition, the first identification unit 40 can determine two or more users U who are presumed to have acted together in the past based on location information detected by each of the multiple terminal devices 2 and transmitted from each of the multiple terminal devices 2, and identify the determined two or more users U as multiple users U having a predetermined relationship.

[0081] [3.3.4.2. Second specific part 41] The second identification unit 41 identifies a past time when the interests of two or more users U identified as identified target users by the first identification unit 40 in the interest space updated every period TA were similar to a specific target.

[0082] The second identification unit 41 determines that the interests of the specific target user and the specific target are similar in the interest space for a certain period when the specific target is included in the interest range of the specific target user in the interest space for a certain period.

[0083] For example, if there is an interest space for each month from October 2021 to February 2022, the latest interest space is the interest space for February 2022. The interest space for each month from October 2021 to February 2022 is obtained by the interest model for each month from October 2021 to February 2022.

[0084] For example, 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.

[0085] The second identification unit 41 identifies, as the target of interest of the specific target user, a target included in the range of interests of the specific target user in the interest space. For example, the second identification unit 41 identifies the range of interests of each specific target user for each period TA using an interest model for each period TA.

[0086] For example, the second identification unit 41 acquires, for each specific target user, search terms included in all search queries sent from the terminal device 2 by the same specific target user within each period TA from the user information storage unit 21. Then, the second identification unit 41 performs a process of averaging the vectors of the acquired multiple search terms to calculate an average vector and determining the calculated average vector as a user interest vector for each specific target user for each period TA. Note that the second identification unit 41 can also calculate the average vector by, for example, weighting the vector of a search term that is more recently received as the search term of the search query.

[0087] The second identification unit 41 identifies, for each specific target user, a range similar to the user interest vector of the specific target user as the interest range of the user U. 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 second identification unit 41 can also identify the interest range of the specific target user, for example, each time the number of new search queries or new search terms for the same specific target user exceeds a predetermined threshold.

[0088] The specific target may be a preset target or may be a target identified by the second identification unit 41. The second identification unit 41 can determine, as the specific target, a target that has been similar in common to the interests of two or more users U who have a predetermined relationship in the interest range updated for each period TA.

[0089] The second identification unit 41 identifies, for each specific target user, each time point when a specific object was within the range of interests of the specific target user in the interest space. For example, the second identification unit 41 identifies a time point when a specific object was within the range of interests of the specific target user in the latest interest space from a predetermined period TC ago.

[0090] FIG. 6 is a diagram showing an example of a past time point when the interest identified by the second identification unit 41 of the information processing device 1 according to the embodiment was similar to a specific object. FIG. 6 shows a part of an interest space visualized by reducing the dimension of the M-dimensional interest space. In FIG. 6, the position P1(t -2 ) and the position P1(t -1 ) and the position P1(t0) of O1 in the latest interest space are shown.

[0091] In addition, in FIG. 6, the interest range R1(t -2 ) and the interest range R1(t -1 ), the latest interest range R1(t0) of user U1, and the interest range R2(t -2 ) and the interest range R2(t -1 6, the latest interest range R2(t0) of user U1 and the latest interest range R2(t0) of user U2 are shown. In the example shown in FIG.

[0092] As shown in Figure 6, the target O1 is within the range of user U1's interests from the interest space two periods ago to the latest interest space, but only the interest space two periods ago is within the range of user U2's interests. In this case, the second identification unit 41 determines that the time when user U1's interests were similar to the specific target was from the period TA two periods ago to the latest period TA, and determines that the time when the specific target was within user U2's range of interests was the period TA two periods ago. If the latest interest space is the interest space of February 2022, the interest space one period ago would be, for example, the interest space of January 2022, and the interest space two periods ago would be, for example, the interest space of December 2021.

[0093] [3.3.5. Estimation section 34] The estimation unit 34 estimates the difference in perception between the two or more specific target users regarding the specific target based on the difference between the two or more specific target users at a past point in time when the interests of the two or more specific target users identified by the identification unit 33 were similar to the specific target.

[0094] For example, the estimation unit 34 estimates the misperception of a specific target between two or more specific target users based on the difference between the two or more specific target users at the most recent point in time in the past when the interests of the specific target users were similar to the specific target.

[0095] 7 is a diagram showing an example of the estimation process of the misrecognition of a specific target between two or more specific target users by the estimation unit 34 of the information processing device 1 according to the embodiment. In FIG. 7, a part of the interest space obtained by reducing the dimension of the M-dimensional interest space and visualizing it is shown, similar to FIG. 6, and the differences between users U1, U2, U3, U4, U5, U6, U7, U8, U9, U10, U11, U12, U13, U14, U15, U16, U17, U18, U19, U20, U21, U22, U23, U24, U25, U26, U27, U28, U29, U30, U31, U32, U33, U34, U35, U36, U37, U38, U39, U40, U41, U42, U43, U44, U45, U46, U47, U48, U49, U50, U51, U52, U53, U54, U55, U56, U57, U58, U59, U60, U61, U62, U63, U64, U65, U66, U67, U68, U69, U70, U71, U72, U73, U74, U75, U76, U77, U78, U79, U80, U81, U82, U83, U84, U85, U90, U91, U92, U93, U94, U95, U96, U106, U117, U118, U120, U130, U141, U152, U163, U174, U185, U19, U206, U1 n The presence or absence of similarity between and subject O1 is shown.

[0096] In the example shown in Fig. 7, in the interest space two periods ago TA2, user U n and the target O1 are similar, but in the interest space one period ago TA1 and the latest interest space, user U nOn the other hand, the user U1 is similar to the target O1 from the interest space two periods ago TA2 to the latest interest space.

[0097] Therefore, the estimation unit 34 estimates the content of the object O1 two periods TA2 ago by the user U n The estimation unit 34 estimates that the user U1 is aware of the content of the target O1 in the latest period TA. n The time when user U1 recognized the content of object O1 was the time of the period TA two periods ago, but the time when user U1 recognized the content of object O1 was the time of the latest period TA. n This is estimated as a difference in perception of the subject O1 between the two groups.

[0098] Furthermore, the estimation unit 34 can estimate a misrecognition of a specific target between two or more specific target users when the difference between the interests of the specific target users and the specific target at the most recent time among past times when the interests of the specific target users were similar is equal to or greater than a predetermined threshold. As a result, the estimation unit 34 does not estimate a misrecognition when the difference in recognition of the specific target between the two or more specific target users is small, thereby reducing the processing load. Note that the predetermined period is determined for each target category, but may also be determined uniformly.

[0099] In addition, the estimation unit 34 estimates the contents of the object O1 up to two periods TA2 ago by the user U n It can also be estimated that the user U1 has recognized the content of the object O1 from two periods TA2 before to the latest period TA. In this case, the estimation unit 34 estimates that the users U1, U n As a difference in the recognition of the target O1 between the two periods TA2, the content of the target O1 from the user U n It is estimated that the user U1 recognizes the contents of the object O1 from the period TA two periods TA2 ago to the latest period TA.

[0100] [3.3.6.Providing Department 35] The providing unit 35 provides misrecognition information, which is information indicating a misrecognition of a specific target between two or more specific target users estimated by the estimation unit 34, to at least one of the two or more specific target users.

[0101] The misrecognition information is provided to the user U by transmitting it from the providing unit 35 to the terminal device 2 via the communication unit 10 and the network N. This allows the providing unit 35 to appropriately provide information on a specific subject that may be of common interest to multiple users U.

[0102] Here, the interest space of users U1, U2 from two periods ago TA2 to the latest interest space n 7 shows the state of similarity between the user U1 and the object O1. In this case, the providing unit 35 determines whether the user U1 has been able to recognize the content and state of the object O1 up to the latest point in time, but whether the user U1 has been able to recognize the content and state of the object O1 up to the latest point in time. n provides information indicating that the content or state of the target O1 may have been recognized only up to two periods TA2 ago as misidentification information to at least one of the two or more specific target users.

[0103] In addition, the providing unit 35 may determine that the user U1 has been able to recognize the contents and state of the object O1 from the time two periods TA2 ago to the latest time. n It is also possible to provide information indicating that the content or state of the target O1 could only be recognized two periods TA2 ago as misidentification information to at least one of the two or more specific target users.

[0104] The providing unit 35 provides the misidentification information to, for example, the first recognized user who is the specific target user who was most recently similar to the specific target among two or more specific target users. In this case, the providing unit 35 provides the misidentification information to, for example, the first recognized user who is the specific target user who was most recently similar to the specific target among two or more specific target users. n The providing unit 35 can provide the first recognized user with information indicating that the user U may not have recognized the content or state of the object O1 at the time two periods TA2 ago as misidentification information.n It is also possible to provide the first recognized user with information indicating that the first recognized user was only able to recognize the content or state of the object O1 at a point two periods TA2 ago as misidentification information.

[0105] The providing unit 35 can also provide information about a specific target to, for example, a second recognized user who is a specific target user other than the first recognized user among the two or more specific target users. In this case, the providing unit 35 can provide the second recognized user with information indicating that the user U1 may have recognized the content or state of the target O1 at the most recent time point as misidentification information. The providing unit 35 can also provide the second recognized user with information indicating that the user U1 may have recognized the content or state of the target O1 from the period TA two periods TA2 ago to the most recent period TA as misidentification information.

[0106] The providing unit 35 can also provide each specific target user with information indicating a ranking of the similarity of the interests of the specific target user with a specific target as misrecognition information. For example, the providing unit 35 calculates the score of each specific target user with respect to a specific target by weighting and adding the degree of similarity of the interests of the specific target user with respect to the specific target for each period TA from a predetermined period before the period TA to the latest period TA. For example, the weight is set to a larger value for newer periods. The providing unit 35 can also provide each specific target user with ranking information in which two or more specific target users are ranked in descending order of their scores with respect to the specific target.

[0107] The providing unit 35 can provide the misrecognition information to the user U in a push manner or a pull manner. For example, the providing unit 35 can display the misrecognition information as a pop-up on the terminal device 2 using an application installed on the terminal device 2, or can send the misrecognition information to the email address of the user U by email. The providing unit 35 can also provide the misrecognition information to the user U by sending the misrecognition information to the terminal device 2 when the user U accesses the information processing device 1 using the terminal device 2. The method of providing the misrecognition information to the user U is not limited to these methods.

[0108] [4. Processing Procedure] Next, a processing procedure by the information processing device 1 according to the embodiment will be described with reference to Fig. 8. Fig. 8 is a flowchart showing a processing procedure by the processing unit 12 of the information processing device 1 according to the embodiment.

[0109] 8, the processing unit 12 of the information processing device 1 determines whether or not it is time to start learning the interest model (step S10). The timing for learning the interest model is, for example, a timing that occurs every period TA, but is not limited to this example.

[0110] 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).

[0111] 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.

[0112] When the processing unit 12 determines that it is time to determine the user's interest (step S13: Yes), the processing unit 12 identifies the user's position of interest based on the user's search query (step S14).

[0113] 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.

[0114] 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 S20 to S24 shown in Fig. 9, and will be described in detail later.

[0115] 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.

[0116] 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 8.

[0117] 9 is a flowchart showing an information providing procedure by the processing unit 12 of the information processing device 1 according to the embodiment. As shown in FIG. 9, the processing unit 12 identifies two or more users U having a predetermined relationship (step S20).

[0118] Next, the processing unit 12 identifies the interest ranges of the two or more users U identified in step S20 (step S21). Then, the processing unit 12 estimates the misunderstanding of the two or more users U regarding the specific object based on the identified difference between the two or more users U at the past time point (step S22).

[0119] Next, the processing unit 12 provides the user U with information corresponding to the misrecognition estimated in step S22 (step S23), and ends the processing shown in FIG.

[0120] [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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 U 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 exceeds a preset threshold.

[0125] [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. 10. Fig. 10 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [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.

[0132] 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.

[0133] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0134] [8. Effects] As described above, the information processing device 1 according to the embodiment includes the estimation unit 34 and the providing unit 35. The estimation unit 34 estimates a misrecognition of a specific target between two or more users U based on a difference between the interests of the two or more users U at a past time when the interests of the two or more users U were similar to the specific target in an interest space in which each of a plurality of targets is represented by a vector according to relevance to the interests of the plurality of users U and the vector is updated at predetermined intervals. The providing unit 35 provides information according to the misrecognition estimated by the estimation unit 34 to at least one user U of the two or more users U. This allows the information processing device 1 to appropriately provide information on targets that the plurality of users U may have a common interest in.

[0135] Furthermore, the estimation unit 34 estimates a misrecognition of the specific target between two or more users U when the difference between the interests of two or more users U and the specific target at a past time when these interests were similar is equal to or greater than a predetermined threshold. As a result, the information processing device 1 does not estimate a misrecognition when there is little difference in recognition of the specific target between two or more specific target users, thereby reducing the processing load.

[0136] Furthermore, the providing unit 35 provides misrecognition information, which is information based on the misrecognition, to a first user who is the most recent user U among two or more users U who was similar to the specific target at a past time point. This allows the information processing device 1 to notify the first user of content that a second user U other than the first user recognizes as content of the specific target. Therefore, the information processing device 1 can prevent the first user from having a conversation that spoils content that the first user recognizes but the second user does not recognize to the second user, for example.

[0137] Furthermore, the providing unit 35 provides information about the specific target to a second user U other than the user U whose past time point was most recent among the two or more users U when the specific target was similar. This allows the information processing device 1 to notify the second user of the content that the first user recognizes as the content of the specific target. Therefore, the information processing device 1 allows the second user to grasp the content of the specific target that the second user did not know, for example, by having the second user ask the first user about the content that the second user does not recognize but the first user recognizes.

[0138] The information processing device 1 also includes a first identification unit 40 that identifies, among the multiple users U, multiple users U who have a predetermined relationship as two or more users U. This allows the information processing device 1 to identify two or more users U without the users U having to perform any setting operation, for example.

[0139] Furthermore, the first identification unit 40 identifies multiple users U who communicate with each other as multiple users U having a predetermined relationship. This allows the information processing device 1 to identify multiple users U who are significantly affected by misunderstandings about a specific target among the users U.

[0140] The information processing device 1 also includes a second identification unit 41. The second identification unit 41 identifies the interests of the user U based on a vector of search terms obtained by inputting the search terms used by the user U into a trained model that has learned the characteristics of each of the search terms used by the multiple users U, assuming that two or more search terms that satisfy predetermined conditions have similar characteristics. This allows the information processing device 1 to more appropriately provide information on subjects that may interest the user U.

[0141] 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.

[0142] 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.

[0143] 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]

[0144] 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 Provision Department 40 First Specific Part 41 Second Specific Part 100 Information Processing Systems N Network

Claims

1. An estimation unit that estimates the content of a specific object between two or more users at a past time point as a misperception of the specific object between the two or more users based on the difference between the interests of the two or more users at a past time point when the specific object was similar to each of the interests of the two or more users in an interest space in which each of multiple objects is represented by a vector and the vector is updated at predetermined intervals; a providing unit that provides information according to the misrecognition estimated by the estimating unit to at least one of the two or more users, The interest space is The database includes vectors of the plurality of objects obtained by inputting information of the plurality of objects into a trained model that has learned the characteristics of each of the plurality of search terms, which are two or more search terms that satisfy predetermined conditions among the plurality of search terms used by a plurality of users, as having similar characteristics.

1. An information processing device comprising:

2. The estimation unit If the difference is equal to or greater than a predetermined threshold, a misrecognition of the specific object between the two or more users is estimated.

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

3. The providing unit Provide information based on the misrecognition to the most recent user among the two or more users who was similar to the specific target at a past time point.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

4. The providing unit Providing information about the specific object to users other than the user who was most recently similar to the specific object among the two or more users 4. The information processing device according to claim 1, wherein the information processing device is a computer.

5. a first identification unit that identifies, among the plurality of users, a plurality of users having a predetermined relationship as the two or more users; 5. The information processing device according to claim 1, wherein:

6. The first identification unit Identifying a plurality of users who communicate with each other as the plurality of users having the predetermined relationship.

6. The information processing apparatus according to claim 5,

7. a second identification unit that identifies the user's interests based on vectors of search terms obtained by inputting the search terms used by the users into the trained model that has learned the characteristics of each of the search terms used by the users, with two or more search terms that satisfy predetermined conditions being considered to have similar characteristics; 7. The information processing device according to claim 1, wherein:

8. a learning unit that generates the trained model using the search terms used by the users; 8. The information processing apparatus according to claim 7,

9. 1. A computer-implemented information processing method, comprising: an estimation process for estimating the content of the specific object between the two or more users at a past time point as a misunderstanding of the specific object between the two or more users based on a difference between the two or more users at a past time point when each of the interests of the two or more users was similar to the specific object in an interest space in which each of a plurality of objects is represented by a vector and the vector is updated every predetermined period of time; a providing step of providing information corresponding to the misrecognition estimated by the estimating step to at least one of the two or more users, The interest space is The database includes vectors of the plurality of objects obtained by inputting information of the plurality of objects into a trained model that has learned the characteristics of each of the plurality of search terms, which are two or more search terms that satisfy predetermined conditions among the plurality of search terms used by a plurality of users, as having similar characteristics. An information processing method comprising:

10. An estimation procedure for estimating the content of a specific object between two or more users at a past time when each of the interests of the two or more users was similar to the specific object in an interest space in which each of multiple objects is represented by a vector and the vector is updated at predetermined intervals, as a misperception of the specific object between the two or more users; and a providing step of providing information corresponding to the misrecognition estimated by the estimation step to at least one of the two or more users; The interest space is The database includes vectors of the plurality of objects obtained by inputting information of the plurality of objects into a trained model that has learned the characteristics of each of the plurality of search terms, which are two or more search terms that satisfy predetermined conditions among the plurality of search terms used by a plurality of users, as having similar characteristics. An information processing program characterized by:

Images