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

Abstract

To accurately analyze a transition of interests to an object of a user.SOLUTION: An information processing device comprises an identification unit and an estimation unit. The identification unit identifies a second object whose interest mode is similar to a first object in an interest space in which each of a plurality of objects including the first object is indicated by a vector according to relevance to interests of a plurality of users and the vector is updated per prescribed period. The estimation unit estimates a reason for the transition of the second object on the basis of commonality between the first object and the second object.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 classify objects such as elements related to commercial transactions into categories in which the user is interested, it is not a technology that actively analyzes changes in the user's interests in the objects. If changes in the user's interests in the objects could be analyzed accurately, for example, information could be provided to the user more appropriately, and there is room for improvement in this regard.

[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 accurately analyze changes in a user's interests in a subject. [Means for solving the problem]

[0006] The information processing device according to the present application includes an identification unit and an estimation unit. The identification unit identifies a second object whose transition pattern is similar to that of the first object in an interest space in which each of a plurality of objects including a first object is represented by a vector according to relevance to the interests of a plurality of users and the vector is updated every predetermined period. The estimation unit estimates the reason for the transition of the second object based on the commonality between the first object and the second object. [Effects of the Invention]

[0007] According to one aspect of the embodiment, it is possible to provide an effect of accurately analyzing changes in a user's interest in a subject. [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 first target identified by a first identification unit of the information processing device according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of a first target identified by a first identification unit of the information processing device according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of a second target identified by the second identification unit of the information processing device according to the embodiment. [Figure 9]FIG. 9 is a diagram illustrating another example of a second target identified by the second identification unit of the information processing device according to the embodiment. [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 showing an information providing procedure performed by the processing unit of the information processing device according to the embodiment. [Figure 12] FIG. 12 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. nFor 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 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 nAn 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 providing process will be described. The information processing device 1 determines the transition state of the first object in the interest space that is updated every predetermined period TA (step S6).

[0025] In the interest space, each of a plurality of objects is represented by a vector according to their relevance to the interests of a plurality of users U. In the interest space, the objects represented by vectors are not limited to objects represented by search terms, and may be objects other than those represented by search terms. The information processing device 1 generates an interest space including vectors for each object by inputting information representing the object into the interest model generated in step S5 and performing a process for each object to obtain a vector of the object output from the interest model. The information processing device 1 generates an interest space that is updated for each period TA using the interest model generated for each period TA in step S5.

[0026] The first object is, for example, an object among a plurality of objects that satisfies a predetermined condition. The predetermined condition is, for example, that the first object has a predetermined transition pattern based on an interest vector, which is a vector indicating the interests in the interest space of the user U to be determined among a plurality of users U. The predetermined transition pattern is, for example, a transition pattern in which the first object approaches a reference vector in the interest space and enters an interest range, which is a range similar to the reference vector, or a transition pattern in which the first object moves away from the reference vector in the interest space and moves from within the interest range to outside the interest range.

[0027] Here, the interest vector and interest range of the user U to be determined will be described. The information processing device 1 inputs search terms included in multiple search queries sent from the terminal device 2 by the same user U to be determined into the interest model generated in step S5, and performs a process of obtaining the vector of the search term output from the interest model for each search term. The information processing device 1 calculates an average vector by averaging the vectors of the search terms included in multiple search queries sent from the terminal device 2 by the same user U to be determined, and determines the calculated average vector as the interest vector. Note that the information processing device 1 can also calculate the average vector by, for example, weighting the vector of the search term more heavily for search terms in search queries that are received more recently.

[0028] The information processing device 1 can also identify an object whose change mode is a specific change mode among multiple objects in the interest space updated every period TA as the first object. The information processing device 1 can also treat an object designated by a user U of the information processing device 1 as the first object.

[0029] Next, the information processing device 1 identifies a second object whose transition pattern is similar to that of the first object in the interest space (step S7). For example, the information processing device 1 identifies, as the second object, an object whose transition pattern is similar to that of the first object in the latest interest space from the interest space up to K periods ago, and which approaches the interest vector of the user U. K is, for example, an integer equal to or greater than 2.

[0030] Next, the information processing device 1 estimates the reason for the transition of the second object based on the commonality between the first object and the second object (step S8). The commonality between the first object and the second object is, for example, the commonality of the object's attributes. If the object is a trading object, the object's attributes include various attributes corresponding to the object, such as the category, usage scenario, specifications (such as the specifications or functions of the trading object), the appearance date of the object (such as the release date of the product), the problems that can be solved by the object, and the worries of people that can be solved by the object.

[0031] For example, when the first object and the second object are in the same category, the information processing device 1 estimates that the transition of the second object is similar to that of the first object because they share a common category. For example, assume that an object that approaches the interest vector of user U in the interest space and enters a range of interests that is similar to the interest vector of user U is identified as the first object. In this case, the information processing device 1 estimates that the reason for the transition of the second object is that user U's interest in the category common to the first object and the second object is increasing.

[0032] Also, for example, suppose that an object that has moved away from the interest vector of the user U in the interest space and from within the interest range to outside the interest range is identified as the first object. In this case, the information processing device 1 estimates that the reason for the change of the second object is that the user U's interest in a category common to the first object and the second object has decreased.

[0033] Next, the information processing device 1 provides information indicating the reason for the change of the second target estimated in step S8 to the user UA of the information processing device 1 (step S9). The information processing device 1 provides the information indicating the reason for the change of the second target to the user UA of the information processing device 1 by transmitting the information indicating the reason for the change of the second target estimated in step S8 to the terminal device 3 of the user UA of the information processing device 1. This allows the user UA of the information processing device 1 to know the reason for the change of the second target and to understand the change in the user U's interest in the second target.

[0034] In this way, the information processing device 1 identifies a second object whose transition pattern is similar to that of the first object in the interest space updated at predetermined intervals, and estimates the reason for the transition of the second object based on the commonality between the first object and the second object. This allows the information processing device 1 to accurately analyze the transition of the user U's interest in objects.

[0035] [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 and a terminal device 3. The information processing device 1, a plurality of terminal devices 21-2 n , and the terminal device 3 are connected via a network N so as to be able to communicate with each other by wire or wirelessly.

[0036] The information processing device 1 according to the embodiment 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.

[0037] 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 the above-mentioned search service and information provision service as online services, as well as services such as SNS (Social Networking Service), electronic commerce (EC), posting sites, electronic payments, online games, online banking, online trading, hotel and ticket reservations, video and music distribution, news, maps, route search, route guidance, line information, operation information, and weather forecasts. The information processing device 1 can also cooperate with various servers that provide the above-mentioned online services and act as an intermediary for the online services.

[0038] The terminal device 3 is, for example, a terminal device operated by a user UA of the information processing device 1. The user UA can operate the terminal device 3 to acquire, from the information processing device 1, information such as information indicating a change in the interests of the user U, which information is provided by the information processing device 1. The information indicating a change in the interests of the user U includes, for example, information indicating the reason for the change in the second target described above.

[0039] 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.

[0040] [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.

[0041] [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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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," and "search 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.

[0046] "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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] "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.

[0051] 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.

[0052] 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.

[0053] 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."

[0054] 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.

[0055] 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.

[0056] [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).

[0057] 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.

[0058] 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.

[0059] 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.

[0060] The reception unit 30 also receives requests from the terminal device 3. For example, the reception unit 30 receives a target-specifying query including information on a target specified as a first target by the user U from the terminal device 3 via the network N and the communication unit 10. The reception unit 30 also receives a user-specified query including information indicating the user U to be determined from the terminal device 3 via the network N and the communication unit 10.

[0061] [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.

[0062] 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.

[0063] [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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] [3.3.4. Specification part 33] The identification unit 33 identifies a second object whose transition manner is similar to that of the first object in an interest space in which each of a plurality of objects is represented by a vector according to the relevance to the interests of a plurality of 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.

[0069] [3.3.4.1. First specific part 40] The first identification unit 40 identifies an object among the plurality of objects that satisfies a predetermined condition as the first object. The object that satisfies the predetermined condition may be, for example, an object that has a predetermined transition pattern based on the interest vector of the user U to be determined, an object among the plurality of objects that has a specific change pattern, or an object designated by the user UA of the information processing device 1. The interest vector of the user U to be determined is a vector in the latest interest space, but may also be a vector in the interest space of each period TA.

[0070] The target specified by the user UA of the information processing device 1 is, for example, a target indicated in a target specifying query received by the receiving unit 30. In this case, the first identifying unit 40 can determine the user U to be determined based on the relationship with the target specified by the user UA. Furthermore, the user U to be determined may be, for example, the user UA indicated in the user specifying query received by the receiving unit 30. Furthermore, the information processing device 1 can also identify, as the first target, a target whose transition pattern is a specific transition pattern among multiple targets in the interest space updated every period TA.

[0071] The transition patterns predetermined based on the interest vector of user U are, for example, a transition pattern in which the user approaches the interest vector of user U in the interest space and enters an interest range that is a range similar to the interest vector of user U, or a transition pattern in which the user moves away from the interest vector of user U in the interest space and moves from within the interest range to outside the interest range.

[0072] The interest space is updated for each period TA. 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 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 will be updated sequentially to the state of the interest space in October 2021, the state of the interest space in November 2021, the state of the interest space in December 2021, the state of the interest space in January 2022, and the state of the interest space in February 2022.

[0073] The first identification unit 40, for example, identifies the range of interests of each user U for each period TA using an interest model for each period TA. For example, the first identification unit 40 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 first identification unit 40 performs a process for each period TA in which the first identification unit 40 averages the vectors of the acquired search terms to calculate an average vector, and determines the calculated average vector as the user interest vector for each user U. Note that the first identification unit 40 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.

[0074] The first identification unit 40 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 first identification unit 40 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.

[0075] After identifying the interest vector of the user U, the first identification unit 40 identifies, for example, as the first object, an object having a predetermined transition pattern based on the interest vector of the user U to be determined. The interest vector of the user U is a vector in the latest interest space, but may also be a vector in the interest space of each period TA.

[0076] FIG. 6 is a diagram showing an example of a first object identified by the first identification unit 40 of the information processing device 1 according to the embodiment. FIG. 6 shows a part of the interest space obtained by reducing the dimension of the M-dimensional interest space and visualizing it. In the example shown in FIG. 6, the position P1(t -3 ) and 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.

[0077] As shown in FIG. 6, the target O1 is not within the range of interests of user U1 in the interest spaces from three periods ago to one period ago, but is within the range of interests of user U1 in the most recent interest space. Furthermore, the more recent the interest space in the interest space from three periods ago to one period ago, the closer it is to user U1's range of interests. In this case, the first identification unit 40 determines that the target O1 has a transition pattern in which it approaches the range of interests of user U1 in the interest space and enters the range of interests of user U1, and identifies the target O1 as the first target. The first identification unit 40 can also identify as the first target a target that has transitioned into the range of interests of user U1 even if it has moved away from the range of interests of user U1 at some point.

[0078] FIG. 7 is a diagram showing an example of a first object identified by the first identification unit 40 of the information processing device 1 according to the embodiment. Similar to FIG. 6, FIG. 7 shows a part of an interest space visualized by reducing the dimension of an M-dimensional interest space. In the example shown in FIG. 7, similar to FIG. 6, the position of an object O1 from three periods before to the latest interest space, P1(t -3 )P1(t -2 ),P1(t -1 ),P1(t0) are shown.

[0079] In the example shown in Figure 7, the target O1 was within the range of interests of user U1 in the interest space three periods ago, but was outside the range of interests of user U1 in the interest space two periods ago. The closer the target O1 is to the most recent interest space, the further it is from user U1's range of interests. In this case, the first identification unit 40 determines that the target O1 has a transition pattern in the interest space that moves from within user U1's range of interests to outside the range of interests, and identifies the target O1 as the first target. Note that the first identification unit 40 can also identify as the first target a target that has transitioned further away from user U1's range of interests, even if it has approached the range of interests of user U1 at some point.

[0080] Furthermore, the first identifying unit 40 can identify as the first target an object whose transition manner is a specific transition manner among a plurality of targets, or can identify as the first target an object designated by a user U of the first identifying unit 40. Furthermore, the first identifying unit 40 can also identify as the first target an object whose movement speed or movement amount in a predetermined period is equal to or greater than a threshold value.

[0081] [3.3.4.2.Second Specification Section 41] The second identification unit 41 identifies a second object whose transition manner is similar to that of the first object identified by the first identification unit 40. For example, the second identification unit 41 determines, in an interest space updated for each period TA, a change in the difference between the vectors of the object between two consecutive periods of interest spaces as the transition manner of the object.

[0082] For example, the vector of the first object from three periods ago to the latest interest space is V 13 ,V 12 ,V 11 ,V 10 In addition, the difference in the vector of the first object between two consecutive interest spaces is ΔV 132 ,ΔV 121 ,ΔV 110 Let ΔV 132 is ΔV 132 =V 13 -V 12 and ΔV 121 is V 12 -V 11 and ΔV 110 is ΔV 110 =V 11 -V 10 Let us assume that:

[0083] Also, the first object is different from the object O. X The vector from three periods before to the latest interest space is V X3 ,V X2 ,V X1 ,V X0 In addition, the target O between two interest spaces with consecutive periods X The vector difference is ΔVX32 ,ΔV X21 ,ΔV X10 Let ΔV X32 is ΔV X32 =V X3 -V X2 and ΔV X21 is ΔV X21 =V X2 -V X1 and ΔV X10 is ΔV X10 =V X1 -V X0 Let us assume that:

[0084] In this case, the second specifying unit 41 determines, for example, ΔV 132 and ΔV X32 Similarity with ΔV 121 and ΔV X21 Similarity with, and ΔV 110 and ΔV X10 If the result of weighting and adding the similarity to the target O is less than the threshold, X is identified as a second object having a transition manner similar to that of the first object. The similarity is, for example, cosine similarity, but is not limited to this example.

[0085] In addition, the second identification unit 41 may, for example, identify the transition state of the first object from the interest space K periods ago to the latest interest space and the object O. X If the transition pattern of the vector of the target O is similar to that of the target O in the interest space for a certain period, X can also be specified as the second target. K is, for example, an integer equal to or greater than 5. The partial period may be the same for the first target and the second target, or may be different for the first target and the second target.

[0086] The same period is, for example, a period from P periods ago to the latest period, where P is an integer greater than 1 and less than M. The different periods are, for example, a first target period from PN periods ago to the latest period, and a second target period from P periods ago to N periods ago, where N is, for example, an integer greater than or equal to 1.

[0087] FIG. 8 is a diagram showing an example of a second object identified by the second identification unit 41 of the information processing device 1 according to the embodiment. Similar to FIGS. 6 and 7, FIG. 8 shows a part of an M-dimensional interest space visualized by reducing the dimension of the M-dimensional interest space. In the example shown in FIG. 8, the position P1(t -3 ),P1(t -2 ),P1(t -1 ),P1(t0) plus the target O X Position P X (t -3 ),P X (t -2 ),P X (t -1 ),P X (t0) is shown.

[0088] position P X (t -3 ) is the target O in the interest space three periods ago. X and position P X (t -2 ) is the target O in the interest space two periods ago. X Also, the position P X (t -1 ) is the target O in the interest space one period ago. X and position P X (t0) is the object O in the latest interest space. X This is the position.

[0089] In the example shown in FIG. 8, the position P1(t -3 ) to position P1(t0) X Position P X (t -3 ) to position P X The transition of the object O1 from the interest space three periods ago to the latest interest space is similar to the transition of the object O1 from the interest space three periods ago to the latest interest space. X Therefore, the second identification unit 41 determines whether the target O X is identified as the second target.

[0090] 9 is a diagram showing another example of a second object identified by the second identification unit 41 of the information processing device 1 according to the embodiment. In FIG. 9, in addition to the state of FIG. 8, the position P1(t -4 ) has been added, and the target O X Position P X (t -3 ),P X (t -2 ),P X (t -1 ),P X (t0) is different from the position shown in FIG.

[0091] In the example shown in FIG. 9, the position P1(t -3 ) to position P1(t -1 ) The transitional state is the target X Position P X (t -2 ) to position P X The transition of the object O1 from the interest space three periods ago to the interest space one period ago is similar to the transition of the object O1 from the interest space two periods ago to the latest interest space. X Therefore, the second identification unit 41 determines whether the target O X In this way, an object that is partially similar to the transition mode of the second identification unit 41 can be identified as the second object.

[0092] In addition, the second identification unit 41 can also treat the difference in the vector of the object between the space of interest K periods ago and the latest space of interest as a transition pattern, instead of or in addition to the change in the difference in the vector of the object between two spaces of interest of consecutive periods.

[0093] A change in the difference between the vectors of an object between two consecutive interest spaces is a change in the vector of the object, but either a change in the magnitude of the vector of the object or a change in the direction of the vector of the object can also be treated as the transition mode of the object. A change in the magnitude of the vector of the object indicates a change in the amount of movement or a change in the speed of movement of the vector of the object, and a change in the direction of movement of the vector of the object indicates a change in the direction of movement of the vector of the object.

[0094] [3.3.5. Estimation section 34] The estimation unit 34 estimates the reason for the transition of the second object based on the commonality between the first object and the second object identified by the identification unit 33. The commonality between the first object and the second object is, for example, the commonality of the attributes of the objects.

[0095] The attributes of the object include various attributes corresponding to the object, such as the category, usage scenario, specifications (such as the specifications or functions of the transaction object), the appearance date of the object (such as the release date of the product), the issues that can be solved by the object, and the worries of people that can be solved by the object. The object is, for example, a transaction object, and the transaction object is, for example, a product or a service.

[0096] For example, when the first object and the second object are in the same category, the estimation unit 34 estimates that the transition of the second object is similar to that of the first object because they share a common category. For example, assume that an object that approaches the interest vector of the user U in the interest space and enters a range of interest similar to the interest vector of the user U is identified as the first object. In this case, the estimation unit 34 estimates that the reason for the transition of the second object is that the user U has become increasingly interested in the category common to the first object and the second object.

[0097] Also, for example, suppose that an object that has moved away from the interest vector of the user U in the interest space from within the interest range to outside the interest range is identified as the first object. In this case, the estimation unit 34 estimates that the reason for the change of the second object is that the user U has become less interested in a category common to the first object and the second object.

[0098] Furthermore, when the first object and the second object have multiple attributes in common, the estimation unit 34 estimates that the reason for the change in the second object is that the second object has multiple attributes in common with the first object. For example, suppose that the first object and the second object are both bags and are the same brand, Brand A, and that Brand A also has wallets, clothes, and other products in addition to bags. Then, suppose that the first object is moving away from the interest vector of user U. In this case, the estimation unit 34 estimates that the reason for the change in the second object is that user U is losing interest in Brand A and shoes.

[0099] In the above example, the estimation unit 34 estimates the reason for the change of the second object from the change in the interest vector and interest range of the user U, but the present invention is not limited to such an example. For example, suppose that the first identification unit 40 identifies an object with a specific change as the first object, or that the first identification unit 40 identifies an object identified by the user U as the first object. In this case, too, the estimation unit 34 can estimate the reason for the change of the second object based on the commonality between the first object and the second object.

[0100] [3.3.6.Providing Department 35] The providing unit 35 provides the user UA with transition reason information, which is information indicating the transition reason of the second object estimated by the estimation unit 34. The transition reason information is provided to the user UA by transmitting it from the providing unit 35 to the terminal device 3 via the communication unit 10 and the network N. This allows the providing unit 35 to provide the user UA with information indicating the transition reason of the second object.

[0101] The transition reason information is text information indicating the second object and the reason for the transition of the second object, but in addition to or instead of such text information, it may also include information illustrating the transition pattern between the first object and the second object in the visualized interest space, for example, as shown in Figure 8 or Figure 9.

[0102] The providing unit 35 can provide the transition reason information to the user UA in a push manner or in a pull manner. For example, the providing unit 35 can display the transition reason information as a pop-up on the terminal device 3 using an application installed on the terminal device 3, or can send the transition reason information to the user UA's email address by email. The providing unit 35 can also provide the transition reason information to the user UA by sending the transition reason information to the terminal device 3 when the user UA accesses the information processing device 1 using the terminal device 3. The method of providing the transition reason information to the user UA is not limited to these methods.

[0103] [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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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 S25 shown in FIG. 11, and will be described in detail later.

[0110] 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.

[0111] 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.

[0112] Fig. 11 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. 11, the processing unit 12 identifies, as a first object, an object that satisfies a predetermined condition among a number of objects in the interest space indicated by the interest space information generated in step S12 of Fig. 11 and in which the vector of each object is updated for a period TA (step S20).

[0113] Next, the processing unit 12 identifies a second object whose transition manner is similar to that of the first object identified in step S20 (step S21). Then, the processing unit 12 estimates the reason for the transition of the second object based on the commonality between the first object identified in step S20 and the second object identified in step S21 (step S22). Thereafter, the processing unit 12 provides the user UA with information indicating the reason for the transition of the second object estimated in step S22 (step S23), and ends the processing shown in FIG. 11.

[0114] [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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [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. 12. Fig. 12 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [7. Other] Furthermore, among the processes described in the above embodiments and modifications, 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 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.

[0126] 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.

[0127] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0128] [8. Effects] As described above, the information processing device 1 according to the embodiment includes the identification unit 33 and the estimation unit 34. The identification unit 33 identifies a second object whose transition pattern is similar to that of the first object in an interest space in which each of a plurality of objects including a first object is represented by a vector according to the relevance to the interests of a plurality of users U and the vector is updated every predetermined period. The estimation unit 34 estimates the reason for the transition of the second object based on the commonality between the first object and the second object. This allows the information processing device 1 to accurately analyze the transition of the user U's interests in objects.

[0129] The estimation unit 34 estimates the reason for the change in the second object by considering at least one of the categories, specifications, and brands of the first object and the second object as commonalities between the first object and the second object. This allows the information processing device 1 to accurately analyze the change in the interest and concern of the user U in objects.

[0130] The identification unit 33 also identifies, as a first object, an object having a predetermined transition pattern based on a vector indicating the interests in the interest space of the user U to be determined among the multiple users U. This allows the information processing device 1 to accurately analyze the transition of the user U's interests in objects.

[0131] In addition, the identification unit 33 identifies, as the first object, an object that approaches the reference in the interest space and falls within a range of interest similar to the reference. This allows the information processing device 1 to accurately analyze the transition of the user U's interest in objects.

[0132] In addition, the identification unit 33 identifies, as the first object, an object that has moved away from the reference in the interest space from within a range of interests similar to the reference to outside the range of interests. This allows the information processing device 1 to accurately analyze the transition of the user U's interests in objects.

[0133] The information processing device 1 also includes a providing unit 35 that provides information indicating the reason for the transition of the second target estimated by the estimation unit 34. This allows the information processing device 1 to provide information indicating the reason for the transition of the second target.

[0134] The interest space is formed based on a trained model that has learned the characteristics of each of a plurality of objects, regarding two or more objects that satisfy predetermined conditions among the plurality of objects as having similar characteristics. This allows the information processing device 1 to analyze the transition of the user U's interest in objects with higher accuracy.

[0135] The information processing device 1 also includes a learning unit 32 that generates a trained model using a plurality of search terms used by a plurality of users U. This enables the information processing device 1 to analyze changes in the interests and concerns of users U with higher accuracy.

[0136] 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.

[0137] 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]

[0138] 1. Information processing equipment 2,21~2 n ,3 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 identification unit that identifies a second object whose vector change is similar to that of the first object in an interest space in which each of a plurality of objects including a first object is represented by a vector and the vector is updated at predetermined intervals; an estimation unit that estimates a reason for the change in the second object based on a commonality between the first object and the second object; 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 Inferring the reason for the change of the second object by using at least one of the category, specifications, and brand of the first object and the second object as a commonality between the first object and the second object.

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

3. The identification unit Identifying, as the first object, an object having a predetermined vector change based on a vector indicating the interests of a user to be determined among the plurality of users in the interest space.

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

4. The identification unit An object that approaches the reference in the interest space and falls within a range similar to the reference is identified as the first object.

4. The information processing apparatus according to claim 3,

5. The identification unit Identifying an object that moves away from the reference in the space of interest from within a range similar to the reference to outside the range as the first object.

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

6. a providing unit that provides information indicating the reason for the change in the second object estimated by the estimation unit; 6. The information processing device according to claim 1, wherein:

7. a learning unit that generates the trained model using a plurality of search terms used by the plurality of users; 6. The information processing device according to claim 1, wherein:

8. A computer-implemented information processing method, comprising: An identification step of identifying a second object whose vector change is similar to that of the first object in an interest space in which each of a plurality of objects including a first object is represented by a vector and the vector is updated every predetermined period; an estimation step of estimating a reason for the change in the second object based on a commonality between the first object and the second object, 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 method comprising:

9. An identification procedure for identifying a second object whose vector change is similar to that of the first object in an interest space in which each of a plurality of objects including a first object is represented by a vector and the vector is updated at predetermined intervals; an estimation procedure for estimating a reason for the change in the second object based on a commonality between the first object and the second object; 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:

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