JP2023121024A - Information processing device, information processing method, and information processing program - Google Patents

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

The present invention relates to an information processing device, an information processing method, and an information processing program.

Conventionally, various techniques have been provided for providing information to users. For example, in Patent Document 1, among a plurality of quadrants in which categories in which the user is interested are classified based on elements related to commercial transactions, based on the quadrant to which the category in which the user is interested belongs , a technique for distributing advertisements corresponding to quadrants has been proposed.

JP 2021-099631 A

However, the above conventional techniques have room for improvement. In the conventional technology described above, information is provided depending on the quadrant, and there is room for improvement in terms of flexible information provision. Appropriate provision is desired.

The present application has been made in view of the above, and provides an information processing apparatus, information processing method, and information processing that can appropriately provide information on a target that may be of common interest to a plurality of users. The purpose is to provide a program.

An information processing apparatus according to the present application includes an estimating unit and a providing unit. The estimating unit estimates the interests of two or more users in an interest space in which each of the plurality of objects is indicated by a vector according to the relevance to the interests of the plurality of users, and in which the vector is updated every predetermined period. Based on the difference between two or more users in the past when the object was similar, the difference in recognition of the specific object between two or more users is estimated. The providing unit provides information according to the recognition difference estimated by the estimating unit to at least one of the two or more users.

According to one aspect of the embodiment, it is possible to appropriately provide information on a target that may be of common interest to a plurality of users.

FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. FIG. 2 is a diagram illustrating an example of a configuration of an information processing system including an information processing apparatus according to an embodiment; FIG. 3 is a diagram illustrating an example of a user information table stored in a user information storage unit of the information processing apparatus according to the embodiment; FIG. 4 is a diagram illustrating an example of a content table stored in a content storage unit of the information processing apparatus according to the embodiment; FIG. 5 is a diagram illustrating an example of space-of-interest information stored in a space-of-interest information storage unit of the information processing apparatus according to the embodiment. FIG. 6 is a diagram illustrating an example of a past point in time when an interest identified by a second identification unit of the information processing apparatus according to the embodiment and a specific target were similar. FIG. 7 is a diagram illustrating an example of a process of estimating misrecognition of a specific target between two or more specific target users by the estimating unit of the information processing apparatus according to the embodiment. FIG. 8 is a flowchart illustrating a processing procedure performed by the processing unit of the information processing apparatus according to the embodiment; FIG. 9 is a flowchart illustrating an information providing procedure by the processing unit of the information processing apparatus according to the embodiment; FIG. 10 is a hardware configuration diagram showing an example of a computer that implements the functions of the information processing apparatus according to the embodiment.

Hereinafter, modes for implementing an information processing apparatus, an information processing method, and an information processing program (hereinafter referred to as "embodiments") according to the present application will be described in detail with reference to the drawings. The information processing apparatus, information processing method, and information processing program according to the present application are not limited to this embodiment. Further, each embodiment can be appropriately combined within a range that does not contradict the processing contents. Also, in each of the following embodiments, the same parts are denoted by the same reference numerals, and overlapping descriptions are omitted.

[1. Example of information processing]
First, an example of information processing according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. Information processing according to the embodiment is processing executed by the information processing apparatus 1, and includes search processing, model generation processing, and information provision processing.

First, search processing and model generation processing will be described. The information processing apparatus 1 shown in FIG. 1 provides search services to users U ₁ to U _n . For example, the information processing apparatus 1 has a search target database, which is a database in which search targets are indexed and stored, and executes search processing on information such as the search target database. For example, the information of the search target database is stored in the storage unit 11 (see FIG. 2).

As shown in FIG. 1, users U ₁ to U _n operate the terminal devices 2 ₁ to 2 _n to perform processing for transmitting search queries from the terminal devices 2 ₁ to 2 _n to the information processing device 1. 2 ₁ to 2 _n are executed (steps S1 ₁ to S1 _n ). n is an integer of 2 or more.

For example, in _{step S1-1} , the user _U1 causes the terminal device _2-1 to transmit a search query to the information processing device 1 by operating the terminal device _2-1 . _In step _S1n , the user _Un operates the terminal device _2n to cause the terminal device _2n to transmit a search query to the information processing device 1. FIG. Hereinafter, when each of the users U ₁ to U _n is individually indicated without distinction, it is referred to as user U, and when each of the terminal devices 2 ₁ to 2 _n is individually indicated without distinction, it is referred to as terminal device 2. It may be described.

The search query includes one or more search terms (search keywords) input by the user U to the terminal device 2 . For example, when the user U inputs "sneakers" as one or more search terms into the terminal device 2, the search query includes "sneakers". Further, when the user U inputs "sneakers ladies" as one or more search words to the terminal device 2, the search query includes "sneakers ladies". "Sneakers ladies" includes two search terms "sneakers" and "ladies" separated by spaces.

The information processing device 1 receives search queries transmitted from the terminal devices 2 ₁ to 2 _n (steps S2 ₁ to S2 _n ). For example, the information processing device 1 receives a search query from the terminal device _2-1 in step _S2-1 , and receives a search query from the terminal device 2- _n in step S2- _n .

Next, the information processing device 1 executes search processing based on the search queries received in steps S2 ₁ to S2 _n (steps S3 ₁ to S3 _n ). For example, in step _S3-1 , the information processing device 1 performs a search process of searching the search target database for targets corresponding to one or more search terms included in the search query transmitted from the terminal device _2-1 and received in step _S2-1 . Execute. Further, in step _S3n , the information processing device 1 performs a search process of searching the search target database for targets corresponding to one or more search terms included in the search query transmitted from the terminal device _2n and received in step _S2n . Execute.

Next, the information processing device 1 transmits search results, which are results of the search processing in steps S3 ₁ to S3 _n , to the terminal devices 2 ₁ to 2 _n (steps S4 ₁ to S4 _n ). For example, in step _S4-1 , the information processing device 1 transmits the search result, which is the result of the search processing in step _S3-1 , to the terminal device _2-1 . Further, in step S4- _n , the information processing device 1 transmits the search result, which is the result of the search processing in step S3- _n, to the terminal device 2- _n .

Next, the information processing device 1 generates an interest model based on the search queries received in steps S2 ₁ to S2 _n (step S5). The interest model generated in step S5 is a model that inputs information indicating each of a plurality of objects and outputs an M-dimensional vector. M is, for example, an integer ranging from 500 to 2000, but is not limited to such examples. Also, the M-dimensional vector may be represented by, for example, a distributed representation, or may be represented by a non-distributed representation. Hereinafter, an M-dimensional vector is simply referred to as a vector.

In step S5, the information processing apparatus 1 regards two or more search terms that satisfy a predetermined condition as having similar features, and learns the features of each of the plurality of search terms to obtain a learned model of interest. Generate an interest model. The two or more search terms that satisfy the predetermined condition are a plurality of search terms included in the same search query, or a plurality of search queries transmitted from the terminal device 2 by the same user U within a predetermined time. Search terms to include.

The information processing device 1 uses two or more search words that satisfy a predetermined condition as learning data, and performs learning so that the vectors of the two or more search words are similar to each other. The information processing device 1 treats search terms included in search queries transmitted from the terminal device 2 by the same user U as two or more search terms that satisfy a predetermined condition regardless of when the search queries are transmitted. can also

Note that the search term is composed of one search keyword, but may be composed of two or more search keywords. For example, when the search query includes the character string “sneakers ladies”, the information processing apparatus 1 treats “sneakers” and “ladies” as different search terms, but sets “sneakers” and “ladies” as one set. It can also be treated as one search term.

The information processing apparatus 1 is, for example, DSSM (Deep Structured Semantic Memory) using LSTM (Long Short-Term Memory), which is a kind of RNN (Recurrent Neural Network) also called recursive neural network, for vector generation (for example, distributed representation generation). Model) technology is used to generate an interest model that outputs a vector (for example, a distributed expression) from information indicating the target such as a search term. Note that the method of generating an interest model that outputs a vector from information indicating an object is not limited to the example described above.

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 apparatus 1 generates an interest model for each month of January, February, March, 2022, and so on.

Next, information providing processing will be described. The information processing apparatus 1 determines a past point in time when the interest of two or more users U and the specific object were similar in the interest space updated every period TA (step S6).

In step S6, the information processing apparatus 1 first identifies interest ranges of each of the two or more users U in the interest space updated every period TA. In the interest space, each of the plurality of objects is indicated by a vector according to the relevance to the interests of the plurality of users U. In the interest space, the target indicated by the vector is not limited to the target indicated by the search term, and may be a target other than the target indicated by the search term.

The information processing apparatus 1 inputs the information indicating the target to the interest model generated in step S5, and acquires the vector of the target output from the interest model for each target, thereby obtaining the vector of each target. Generate an interest space containing For example, the information processing apparatus 1 generates an interest space for each period TA using an interest model for each period TA.

Then, the information processing device 1 inputs search terms included in a plurality of search queries transmitted from the terminal device 2 by the same user U to the interest model generated in step S5, and outputs the interest model. A process of obtaining a search term vector is performed for each search term.

The information processing device 1 averages vectors of search words included in a plurality of search queries transmitted from the terminal device 2 by the same user U to calculate an average vector, and determines the calculated average vector as a user interest vector. do. Then, the information processing device 1 identifies a range similar to the user interest vector as the interest range of each user U. FIG. Note that the information processing apparatus 1 can calculate the average vector by increasing the weight of the vector of the search term for the search term of the search query received more recently, for example.

The information processing apparatus 1 identifies the interest range of each user U for each period TA using the interest model for each period TA. The information processing device 1 identifies, for each user U, the range of interest in the period TA based on a plurality of search queries transmitted from the terminal device 2 by the same user U within the period TA, for example.

Further, the information processing apparatus 1 identifies a plurality of users U having a predetermined relationship as the two or more users U described above. Two or more users U are a plurality of users U having a predetermined relationship.

A predetermined relationship is, for example, a relationship of communicating between each other. Communication is a conversation between users U using text or voice, such as voice calls, chats, emails, or video conferences. The information processing device 1 provides a voice call service, a chat service, an e-mail service, a video conference service, or the like, and identifies two or more users U who communicate with each other based on information obtained from these services. can do.

The information processing device 1 can also identify two or more users U set in the information processing device 1 as a family such as a couple or a parent and child as a plurality of users U having a predetermined relationship. Further, the information processing device 1 detects two or more users U who are estimated to have acted together in the past based on position information detected by each of the plurality of terminal devices 2 and transmitted from each of the plurality of terminal devices 2. can be determined, and the determined two or more users U can be specified as a plurality of users U having a predetermined relationship.

Further, the information processing apparatus 1 designates, as a specific target, a target that has been commonly similar to the interests of two or more users U who have a predetermined relationship in the range of interests updated every period TA. decide. Then, the information processing apparatus 1 determines a past point in time when the interests of two or more users U having a predetermined relationship and the specific object were similar. Note that the specific target may be information predetermined by the user of the information processing device 1 or the like.

Next, the information processing apparatus 1 determines an inter-user time difference, which is the difference between the two or more users U in the past when the interests of the two or more users U were similar to the specific object. (Step S7). In step S7, the information processing device 1 determines that the specific object of the interest of the user U was similar in the past when the specific object was included in the interest range of the user U in the past.

In addition, in step S7, the information processing apparatus 1 determines the latest point in time among past points in time when the interest of the user U and the specific object were similar. Then, the information processing apparatus 1 can determine the latest time difference between two or more users U as the inter-user time difference.

Next, the information processing device 1 estimates a difference in recognition of a specific target between two or more users U based on the inter-user time difference determined in step S7 (step S8). Then, the information processing apparatus 1 provides at least one of the two or more users U with information corresponding to the difference in recognition estimated in step S8 (step S9).

For example, two or more users U having a predetermined relationship are users U ₁ and _Un , and the interests of both user U ₁ and user _Un were similar to serial novel B in monthly magazine A in the past. Suppose In this case, the information processing apparatus 1 determines that the serialized novel B in the monthly magazine A is the specific target.

In addition, the latest space of interest among the spaces of interest in which the user _U1 was similar to the serialized novel B of the monthly magazine A in the past is the space of interest three periods TA3 (=TA×3) before. Let _n be the newest space of interest one period TA1 (=TA×1) before the space of interest that was similar to serial novel B in monthly magazine A in the past. In this case, the information processing apparatus 1 determines that the time difference between users is two periods TA2 (=TA3-TA1).

Then, the information processing apparatus 1 presumes that the user _U1 recognizes the contents of the serialized novel B in the monthly magazine A up to three periods TA3 before, It is assumed that the user U _n is aware of the content. In this way, the information processing apparatus 1 estimates the difference in recognition of the specific target between the users U ₁ and U _n based on the time difference between the users.

Then, the information processing apparatus 1 provides at least one of the users U ₁ and _Un with information according to the difference in recognition of the specific object between the users U ₁ and _Un . For example, the information processing apparatus 1 provides the user _Un with information to the effect that the user _U1 may have only recognized the contents of the serialized novel B in the monthly magazine A up to three periods TA3 before. The information processing apparatus 1 can also provide the user _U1 with information indicating that the user _Un may have recognized the contents of the serialized novel B in the monthly magazine A up to one period TA1 before.

In this way, the information processing apparatus 1 determines whether the interest of each of the two or more users U is similar to the specific object based on the difference between the two or more users U in the past. Estimate the difference in recognition of a particular object between U. Then, the information processing apparatus 1 provides at least one of the two or more users U with information indicating the estimated difference in recognition. Thereby, the information processing device 1 can appropriately provide information on a target that may be of interest to a plurality of users U in common.

[2. Configuration of information processing system]
FIG. 2 is a diagram showing an example of the configuration of an information processing system including the information processing device 1 according to the embodiment. As shown in FIG. 2, the information processing system 100 includes an information processing device 1 and a plurality of terminal devices 2 ₁ to 2 _n . The information processing device 1 and the plurality of terminal devices 2 ₁ to 2 _n are connected via a network N so as to be communicable by wire or wirelessly.

The information processing device 1 is an information processing device that can communicate 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 communicably connected to various other devices via a network N. As shown in FIG.

The information processing device 1 also provides the terminal device 2 of each user U with an online service such as a web service. For example, the information processing apparatus 1 provides online services such as SNS (Social Networking Service), electronic commerce (EC) sites, posting sites, electronic payment, online games, in addition to the above-described search service and information providing service. , online banking, online trading, accommodation/ticket reservation, video/music distribution, news, maps, route search, route guidance, route information, operation information, weather forecast, etc. Note that the information processing apparatus 1 can cooperate with various servers that provide the above-described online services and mediate the online services.

The terminal device 2 is an information processing device used by a user U who accesses content such as web pages displayed on a browser and content for applications. For example, the terminal device 2 is a desktop PC (Personal Computer), a notebook PC, a tablet terminal, a mobile phone, a PDA (Personal Digital Assistant), or the like. Note that the terminal device 2 is not limited to the example described above, and may be, for example, a smart watch or a wearable device.

[3. Configuration of Information Processing Device 1]
An example of the functional configuration of the information processing apparatus 1 described above will be described below. As shown in FIG. 2 , the information processing device 1 has a communication section 10 , a storage section 11 and a processing section 12 .

[3.1. communication unit 10]
The communication unit 10 is implemented by, for example, a NIC (Network Interface Card). The communication unit 10 is connected to the network N by wire or wirelessly, and transmits and receives information to and from other various devices. For example, the communication unit 10 transmits and receives information via the network N to and from the terminal devices 2 ₁ to 2 _n .

[3.2. Storage unit 11]
The storage unit 11 is realized by, for example, a semiconductor memory device 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 .

[3.2.1. Search information storage unit 20]
The search information storage unit 20 stores a plurality of search target information provided by the information processing apparatus 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.

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

[3.2.2. User information storage unit 21]
The user information storage unit 21 stores user information including information on users U ₁ to U _n . FIG. 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.

As shown in FIG. 3, the user information table stored in the user information storage unit 21 includes "user ID (Identifier)", "user name", "attribute", "purchase history", "search history", and " Other history” is included for each user U. “User ID” is identification information unique to each user U. “User name” is information indicating the user U's name.

The “attribute” is information indicating the user U's attribute. The attributes of the user U are user U's demographic attributes, psychographic attributes, or the like. Demographic attributes are attributes of user U demographically. Psychographic attributes are attributes that indicate the user U's values, lifestyle, personality, interests, and the like.

In the example shown in FIG. 3, the user U's demographic attributes include information such as "sex" and "age". “Gender” is information indicating the gender of the user U, and “age” is information indicating the age of the user U. Note that the user U's demographic attributes further include, for example, the user U's job title, work in charge, annual income, address, commuting route, training history, family structure, and the like. The preferences of the user U include, for example, the user U's degree of interest in each object such as clothes, travel, cars, motorcycles, computers, and lunch.

"Purchase history" is a site purchase history that is information of transaction objects (products and services) purchased by the user U in services on the electronic commerce site provided by the information processing apparatus 1, and transaction objects purchased by the user U at a physical store. including physical store purchase history, etc. The site purchase history and the physical store purchase history include, for example, information on the transaction target purchased by the user U, information such as the date and time of purchase, user U's evaluation of the transaction target purchased by the user U (writing, evaluation scores, etc.) including).

The “search history” is information of search history in an online service provided by the information processing device 1 or an online service in which the user U mediates the information processing device 1 . The "search history" includes, for each search query, information indicating the date and time when the search query was received by the information processing apparatus 1, information on one or more search words (search keywords) included in the search query, and the like.

"Other histories" are, for example, histories other than purchase histories and search histories, and include various histories of user U's use of online services (information such as details of use and date and time of use). For example, the “other history” includes a history of writing on SNS or a posting site using the terminal device 2 by the user U, a history of web pages viewed by the user U, and the like.

[3.2.3. Content storage unit 22]
The content storage unit 22 stores content provided by the information processing apparatus 1 as an online service other than the search service. 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 "content ID" and "content" for each content.

"Content ID" is identification information unique to each content. "Content" is information about the content associated with the "content ID". Specifically, the content may indicate information about the contents of the content. For example, the content is content provided by an online service. For example, the content is content related to a portal site, news site, auction site, weather forecast site, shopping site, finance (stock price) site, or the like. Also, the content may be content related to a route search site, a map providing site, a travel site, a restaurant introduction site, a web blog site, a posting site, a music distribution site, a video distribution site, or an SNS site.

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

[3.2.4. Interest space information storage unit 23]
The space-of-interest information storage unit 23 stores space-of-interest information for each predetermined period TA. The interest space information includes information such as vectors of multiple objects arranged 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 such an example, and may be, for example, one week, two weeks, or three months. Also, the period TA may be a period during which the number of search queries or the number of new search terms newly received by the information processing apparatus 1 is equal to or greater than a preset threshold.

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

"Target ID" is identification information unique to each target. The “object” is an object that can be of interest to the user U and is placed in the interest space, and belongs to various categories such as shopping, travel, news, sports, entertainment, finance, games, movies, and music. Target.

For example, in FIG. 5, the target with the target ID "Q1" is "O1" and the vector is "V1". In the example shown in FIG. 5, the target is expressed by an abstract code such as "O1", but the target is indicated by a specific character string, but may be indicated by an image or the like. In addition, in the example shown in FIG. 5, the vector is represented by an abstract code such as "V1", but the vector is an M-dimensional vector and is indicated by, for example, the value of each dimensional vector component.

[3.3. processing unit 12]
The processing unit 12 is a controller, and for example, various programs (information processing An example of a program) is implemented by executing the RAM as a work area. Also, the processing unit 12 is a controller, and is implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 2, the processing unit 12 includes 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 is configured to process information described below. Realize or perform the function or action of a process. The internal configuration of the processing unit 12 is not limited to the configuration shown in FIG. 2, and may be another configuration as long as it performs information processing described later.

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

The receiving 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 . A content transmission request is a request specifying content.

[3.3.2. search unit 31]
The search unit 31 searches for search target information corresponding to one or more search terms included in the search query received by the reception unit 30 from a plurality of search target information stored in the search information storage unit 20. do. The search unit 31 transmits the searched search target information as a search result to the terminal device 2 that transmitted the search query via the network N and the communication unit 10 .

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

[3.3.3. learning unit 32]
The learning unit 32 assumes that two or more search terms that satisfy a predetermined condition have similar features, and creates an interest model, which is a learned model that has learned the features of each of the plurality of search terms, for each period TA. Generate.

The two or more search terms that satisfy the predetermined condition are a plurality of search terms included in the same search query, or a plurality of search queries transmitted from the terminal device 2 by the same user U within a predetermined time. Search terms to include. Note that the learning unit 32 regards the search terms included in the search queries transmitted from the terminal device 2 by the same user U as two or more search terms that satisfy a predetermined condition regardless of when the search queries are transmitted. can also handle.

The learning unit 32 generates learning data using a search term included in a search query whose search time is within the period TA. For example, the learning unit 32 acquires two or more search terms that satisfy a predetermined condition for each period TA from the user information storage unit 21, and uses the acquired two or more search terms as data for learning an interest model. to generate For example, the learning unit 32 uses two or more search words that satisfy a predetermined condition as learning data, and performs learning so that the vectors of the two or more search words are similar to each other.

For example, the learning unit 32 uses DSSM technology that uses LSTM, which is a type of RNN also called a recursive neural network, for vector generation, and generates an interest model that outputs a vector from information indicating a target such as a search term. do. Note that the method of generating an interest model that outputs a vector from information indicating a target such as a search term is not limited to the above example, and learning can be performed so that vectors indicating a plurality of similar targets are similar to each other. If possible, various known techniques can be used.

The learning unit 32 acquires vectors for each of the plurality of targets using the generated interest model for each period TA, and uses information including information on the acquired vectors of the plurality of targets as space-of-interest information for each period TA. It is stored in the interest space information storage unit 23 in the storage unit 11 .

[3.3.4. Identification unit 33]
The specifying unit 33 determines the interests of two or more users U in an interest space in which each of a plurality of objects is indicated by a vector according to the relevance to the interests of the plurality of users U and the vector is updated every period TA. identify past points in time when a specific object was similar to The identifying section 33 includes a first identifying section 40 and a second identifying section 41 .

[3.3.4.1. First identification unit 40]
The first identification unit 40 identifies each of two or more users U having a predetermined relationship among the plurality of users U as identification target users. A predetermined relationship is, for example, a relationship of communicating between each other.

Communication is a conversation between users U using text or voice, such as voice calls, chats, emails, or video conferences. The first identification unit 40 can identify two or more users U who communicate with each other, based on the user U's usage history of the voice call service, chat service, mail service, or video conference service. .

Further, based on the user information stored in the storage unit 11, the first identification unit 40 identifies two or more users U who are set as a family such as a couple or a parent and child as a plurality of users having a predetermined relationship. It can also be identified as U.

In addition, the first identification unit 40 detects two or more users who are presumed to have acted together in the past based on the position information detected by each of the plurality of terminal devices 2 and transmitted from each of the plurality of terminal devices 2. U may be determined, and two or more determined users U may be identified as a plurality of users U having a predetermined relationship.

[3.3.4.2. Second specifying unit 41]
The second identifying unit 41 determines whether the interests of the two or more users U identified as the specific target users by the first identifying unit 40 in the interest space updated every period TA are similar to the specific target in the past. Identify the time point of

When the range of interest of the specific target user in the space of interest for a certain period includes the specific target, the second specifying unit 41 determines that the interest of the specific target user and the specific target are different from each other in the space of interest for a certain period. Judged as similar.

For example, if there is a space of interest for each month from October 2021 to February 2022, the latest space of interest is the space of interest 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.

For example, the interest space for October 2021 is obtained by inputting information indicating each of the plurality of subjects into the interest model for October 2021, and the interest space for February 2022 is Obtained by inputting information representing each of a plurality of objects into the lunar interest model. Also, the interest space is 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, and the state of the interest space in January 2022. , is sequentially updated to the state of the interest space in February 2022.

The second identifying unit 41 identifies an object included in the specific target user's range of interest in the interest space as the specific target user's interest target. The second identifying unit 41 identifies the interest range of each specific target user for each period TA, for example, using an interest model for each period TA.

For example, the second identification unit 41 retrieves search terms included in all search queries transmitted from the terminal device 2 by the same specific target user within each period TA from the user information storage unit 21 for each specific target user. get. Then, the second identifying unit 41 averages the obtained vectors of the plurality of search terms to calculate an average vector, and performs a process of determining the calculated average vector as a user interest vector for each specific target user every period TA. conduct. Note that the second identifying unit 41 can calculate the average vector by increasing the weight of the vector of the search term for the search term of the search query received more recently, for example.

The second identifying unit 41 identifies a range similar to the user interest vector of the specific target user as the interest range of the user U for each specific target user. The range of similarity with the user interest vector is, for example, a range in which cosine similarity is predetermined, but is not limited to such an example. Note that the second identifying unit 41, for example, each time the number of new search queries or the number of new search terms for the same specific target user exceeds a preset threshold value, the range of interest of the specific target user is determined. can also be specified.

The specified target may be a preset target or a target specified by the second specifying unit 41 . The second specifying unit 41 determines, as specified targets, targets that have been commonly similar to the interests of two or more users U who have a predetermined relationship in the range of interests updated every period TA. can do.

The second identifying unit 41 identifies, for each specific target user, each point in time when the specific target is within the range of interest of the specific target user in the interest space. For example, the second identifying unit 41 identifies the point in time when the specific target was within the range of interest of the specific target user in the latest interest space from before the predetermined period TC.

FIG. 6 is a diagram showing an example of past points in time when the interest identified by the second identification unit 41 of the information processing apparatus 1 according to the embodiment and the specific target were similar. FIG. 6 shows a part of the interest space visualized by reducing the dimension of the M-dimensional interest space. In FIG. 6, the position P ₁ (t ₋₂ ) of the object O1 in the interest space two periods ago, the position P ₁ (t ₋₁ ) of the object O1 in the interest space one period ago, and the latest interest The position P ₁ (t ₀ ) of O1 in the space of interest is shown.

Further, in FIG. 6, the interest range R ₁ (t ₋₂ ) of the user U ₁ two periods ago, the interest range R ₁ (t ₋₁ ) of the user U ₁ one period ago, and the latest user The interest range R ₁ (t ₀ ) of U ₁ , the interest range R ₂ (t ₋₂ ) of _user U ₂ two periods ago, and the interest range R ₂ (t − ) of user U ₂ one period ago ₁ ) and the interest range R ₂ (t ₀ ) of the most recent user U ₂ . In the example shown in FIG. 6, users U ₁ and U ₂ are specific target users.

As shown in FIG. 6, object O1 is within the range of interest of user _U1 from the space of interest two periods ago to the latest space of interest, but is within the range of interest of user _U2. Only the interest space of two periods ago. In this _case , the second identifying unit 41 determines that the point in time when the interest of user _U1 was similar to the specific object was from the period TA two periods earlier to the latest period TA, and It is determined that the time when the specific object was within the range of interest was the period TA two periods ago. If the latest space of interest is the space of interest in February 2022, the space of interest one period ago is, for example, the space of interest in January 2022, and the space of interest two periods ago is, for example, , is the interest space of December 2021.

[3.3.5. Estimation unit 34]
The estimating unit 34 calculates two or more specific target users based on the difference between the two or more specific target users in the past when the specific targets were similar to the interests of the two or more specific target users identified by the identifying unit 33. to estimate the difference in recognition of a specific target among specific target users.

For example, the estimating unit 34 determines two or more specific targets based on the difference between the two or more specific target users at the latest point in time in the past when the specific target user's interest and the specific target were similar. Estimate the recognition difference for a specific object between users.

FIG. 7 is a diagram illustrating an example of a process of estimating a recognition difference of a specific target between two or more specific target users by the estimating unit 34 of the information processing device 1 according to the embodiment. As in FIG. 6, FIG. 7 shows a part of the interest space visualized by reducing the dimension of the M-dimensional interest space. The presence or absence of similarity between users U ₁ and U _n and object O ₁ is shown.

In the example shown in FIG. 7, the user _Un and the object O1 are similar in the interest space two periods TA2 before, but the user Un and the object _O1 are similar in the interest space one period TA1 and the latest interest space. _Un and object O ₁ are dissimilar. On the other hand, user _U1 is similar to object _O1 from the space of interest two periods TA2 ago to the latest space of interest.

Therefore, the estimation unit 34 estimates that the user U _n recognizes the content of the target _O1 two periods TA2 before, and that the user _U1 recognizes the content of the target _O1 in the latest period TA. presume. Then, the estimating unit 34 determines that the time when the user U _n recognizes the contents of the object _O1 is the time of the period TA two periods TA2 before, but the time when the user _U1 recognizes the contents of the object _O1 is the point in time of the latest period TA as the difference in recognition of the object O ₁ between the users U ₁ and _Un .

In addition, the estimation unit 34 determines that the difference between two or more specific target users at the latest point in time among the past points in time when the specific target user's interest and the specific target were similar is equal to or greater than a predetermined threshold. In this case, it is possible to estimate the difference in recognition of a specific target between two or more specific target users. As a result, the estimating unit 34 does not estimate the recognition difference when there is little difference in recognition of the specific target between two or more specific target users, so that the processing load can be reduced. In addition, although the predetermined period is determined for each target category, it may be determined uniformly.

_The estimating unit 34 also presumes that the user U _n recognizes the contents of the target _O1 up to two periods TA2 before, and the user U It can also be inferred that ₁ recognizes. In this case, the estimating unit 34 estimates that the user U _n recognizes the content of the target O ₁ two periods TA2 before, as a difference in recognition of the target O ₁ between the users U 1 and _{U n} . Assume that user _U1 is aware of the contents of object _O1 from period TA before TA2 to the latest period TA.

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

The misrecognition information is provided to the user U by transmission from the providing unit 35 to the terminal device 2 via the communication unit 10 and the network N. FIG. Thereby, the provision part 35 can provide appropriately the information of the specific object which several users U may have interest in common.

Here, it is assumed that the presence or absence of similarity between users U ₁ and U _n and object O ₁ in the space of interest two periods TA2 before to the latest space of interest is shown in FIG. 7 . In this case, the providing unit 35 may recognize the contents and status of the object _O1 up to the latest point in time, but the _{user Un} may recognize the contents of the object _O1 only up to the point two periods TA2 before. At least one specific target user among two or more specific target users is provided with information indicating that it is possible that the user is not able to recognize the status or state as misunderstanding information.

In addition, the providing unit 35 considers that the user _U1 may be able to recognize the content and state of the target O1 from the time two periods TA2 before to the latest time, but the user _Un can only recognize the contents and state of the object _O1 from the time two periods TA2 before. It is also possible to provide at least one specific target user among two or more specific target users with information indicating that it is possible that the content and state of target _O1 cannot be recognized as misunderstanding information.

For example, the providing unit 35 provides misidentification information to a first recognition user who is a specific target user who is the most recent of past points in time similar to a specific target among two or more specific target users. In this case, the providing unit 35 sends information indicating that the user _Un could not recognize the contents and state of the target _O1 two periods TA2 before, for example, to the first recognized user as misunderstanding information. can provide. Further, the providing unit 35 provides the first recognized user with, as misunderstanding information, information indicating that it is possible that the user U _n could only recognize the contents and state of the target _O1 at the time two periods TA2 before. can also

Further, the providing unit 35 can also provide, for example, information about a specific target to a second recognized user who is a specified target user other than the first recognized user among two or more specified target users. In this case, the providing unit 35 can provide the second recognized user with information indicating that the user _U1 may be able to recognize the contents and state of the target _O1 at the latest point in time as misunderstanding information. can. In addition, the providing unit 35 uses information indicating that the user _U1 may be able to recognize the contents and state of the target _O1 from the period TA two periods TA2 before to the latest period TA as the misunderstanding information. 2-aware users can also be provided.

Note that the providing unit 35 can also provide each specific target user with, for example, information indicating a ranking of the degree of similarity between the interest of the specific target user and the specific target as recognition difference information. For example, the provision unit 35 specifies the degree of similarity of the interest of the specific target user to the specific target from the period TA before a predetermined period to the latest period TA by weighting and adding the degree of similarity for each period TA. A score for a specific target of the target user is calculated for each specific target user. 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 arranged in descending order of scores for a specific target.

The providing unit 35 can provide the misrecognition information to the user U in a push-type manner or in a pull-type manner. For example, the providing unit 35 can cause the terminal device 2 to display the misrecognition information as a pop-up using an application installed in the terminal device 2, or can transmit the misrecognition information to the user U's mail address by e-mail. Further, the providing unit 35 may provide the user U with the misrecognition information by transmitting the misrecognition information to the terminal device 2 when the user U accesses the information processing device 1 using the terminal device 2. can. Note that the method of providing the recognition error information to the user U is not limited to these methods.

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

As shown in FIG. 8, the processing unit 12 of the information processing device 1 determines whether or not the learning processing timing of the interest model has come (step S10). The learning processing timing of the interest model is, for example, timing that occurs every period TA, but is not limited to such an example.

When the processing unit 12 determines that the learning process timing for the interest model has come (step S10: Yes), the processing unit 12 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).

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

If the processing unit 12 determines that the user interest determination timing has come (step S13: Yes), the processing unit 12 specifies the user U's interest position based on the user U's search query (step S14).

When the processing of step S14 is finished, or when it is determined that the user interest determination timing has not come (step S13: No), the processing unit 12 determines whether or not the information provision target determination timing has come (step S15). The information provision target determination timing is the timing after the position of interest of the user U is specified in step S14. For example, the information provision target determination timing 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 .

If the processing unit 12 determines that it is the information provision target determination timing (step S15: Yes), the processing unit 12 performs information provision processing (step S16). The process of step S16 is the process of steps S20 to S24 shown in FIG. 9, and will be described in detail later.

When the process of step S16 is completed, or when it is determined that the information provision target determination timing has come (step S15: No), the processing unit 12 determines whether or not the operation end timing has come (step S17). The processing unit 12 determines that the operation end timing has come, for example, when the power of the information processing apparatus 1 is turned off.

If the processing unit 12 determines that the operation end timing has not come (step S17: No), the process proceeds to step S10, and if it determines that the operation end timing has come (step S17: Yes), a Terminate the indicated process.

FIG. 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).

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 a recognition difference between the two or more users U with respect to the specific object based on the difference between the two or more users U at the specified past time (step S22).

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

[5. Modification]
The information processing apparatus 1 described above may be embodied in various different forms other than the embodiments described above. Therefore, other embodiments of the information processing apparatus 1 will be described below.

The processing unit 12 of the information processing device 1 can generate a plurality of 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 regards two or more search terms that satisfy a predetermined condition among the plurality of search terms used by the plurality of users U in the target area as having similar characteristics, and each of the plurality of search terms has similar characteristics. It is also possible to generate an interest/interest model for each region by learning the features of the .

Further, the processing unit 12 can form an interest space for each attribute of the user U based on the interest model for each attribute of the user U. For example, the processing unit 12 regards 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. It is also possible to generate an interest/interest model for each specific attribute by learning the features of the .

In addition, the processing unit 12 generates an interest model for the period TA based on a plurality of search queries transmitted from a plurality of terminal devices 2 by a plurality of users U within the period TA, but is not limited to this example. For example, the processing unit 12 updates a plurality of search queries transmitted from a plurality of terminal devices 2 by a plurality of users U within the period TA in a period including the period TA before P period and the latest period TA. An interest model for the period TA can also be generated. P is an integer of 1 or more.

In addition, the processing unit 12 specifies the interest range of the user U for each user U based on a plurality of search queries transmitted from the terminal device 2 by the same user U within the period TB for each period TB. can also 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 the number of new search terms newly received from the same user U by the information processing apparatus 1 is equal to or greater than a preset threshold.

[6. Hardware configuration]
The information processing apparatus 1 according to the embodiments described above is realized by a computer 80 configured as shown in FIG. 10, for example. FIG. 10 is a hardware configuration diagram showing an example of a computer 80 that implements the functions of the information processing apparatus 1 according to the embodiment. The computer 80 includes a CPU 81, a RAM 82, a ROM (Read Only Memory) 83, a 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.

The CPU 81 operates based on programs stored in the ROM 83 or HDD 84 and controls each section. The ROM 83 stores a boot program executed by the CPU 81 when the computer 80 is started, a program depending on the hardware of the computer 80, and the like.

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

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

The media interface 87 reads programs or data stored in the recording medium 88 and provides them to the CPU 81 via the RAM 82 . The CPU 81 loads the program 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. and so on.

The CPU 81 of the computer 80 implements the functions of the processing section 12 by executing programs loaded on the RAM 82 . Further, the data in the storage unit 11 is stored in the HDD 84 . The CPU 81 of the computer 80 reads these programs from the recording medium 88 and executes them, but as another example, these programs may be acquired via the network N from another device.

[7. others〕
Further, among the processes described in the above-described embodiment and modifications, all or part of the processes described as being automatically performed can be manually performed, or can be manually performed. All or part of the described processing can also be performed automatically by known methods. In addition, information including processing procedures, specific names, and various data and parameters shown in the documents and drawings described above can be arbitrarily changed unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

Also, each component of each device illustrated is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution and integration of each device is not limited to the one shown in the figure, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured.

Also, the above-described embodiments and modifications can be appropriately combined within a range that does not contradict the processing content.

[8. effect〕
As described above, the information processing device 1 according to the embodiment includes the estimating unit 34 and the providing unit 35 . The estimation unit 34 estimates the interests of two or more users U in an interest space in which each of a plurality of objects is indicated by a vector according to the relevance to the interests of the plurality of users U, and the vector is updated every predetermined period. Based on the difference between the two or more users U in the past when the specific object was similar to the specific object, the difference in recognition of the specific object between the two or more users U is estimated. The providing unit 35 provides at least one of the two or more users U with information according to the recognition difference estimated by the estimating unit 34 . Thereby, the information processing device 1 can appropriately provide information on a target that may be of interest to a plurality of users U in common.

In addition, the estimation unit 34, if the difference between the two or more users U in the past when the interests of the two or more users U and the specific object were similar is equal to or greater than a predetermined threshold, A recognition difference for a specific object between the above users U is estimated. As a result, the information processing apparatus 1 does not estimate the recognition difference when there is little difference in recognition of the specific target between two or more specific target users, so that the processing load can be reduced.

In addition, the providing unit 35 provides misrecognition information, which is information based on misrecognition, to the first user U, who is the user U at the latest point in time among past points in time similar to the specific target among the two or more users U. provide. Thereby, the information processing apparatus 1 can notify the first user of the content recognized as the content of the specific target by the second user who is the user U other than the first user. Therefore, the information processing apparatus 1 can suppress, for example, a conversation in which the first user spoils the content that the first user recognizes but the second user does not recognize. .

In addition, the providing unit 35 provides information about a specific target to a second user U other than the user U who is the most recent past time similar to the specific target among the two or more users U. I will provide a. Thereby, the information processing device 1 can notify the second user of the content that the first user recognizes as the specific target content. Therefore, for example, the information processing apparatus 1 asks the first user what the second user does not recognize but what the first user recognizes. The second user can grasp the contents that the second user did not know.

The information processing apparatus 1 also includes a first identification unit 40 that identifies, as two or more users U, a plurality of users U having a predetermined relationship among the plurality of users U. Thereby, the information processing apparatus 1 can identify two or more users U without the user U performing a setting operation or the like.

In addition, the first identifying unit 40 identifies multiple users U who communicate with each other as multiple users U having a predetermined relationship. As a result, the information processing device 1 can identify a plurality of users U who are significantly influenced by misunderstandings of a specific target among the users U. FIG.

The information processing device 1 also includes a second identification unit 41 . The second identifying unit 41 regards two or more search terms that satisfy a predetermined condition as having similar features, and uses a learned model that has learned the features of each of the plurality of search terms used by the plurality of users U. User U's interests are identified based on a search term vector obtained by inputting a search term used by user U. FIG. Thereby, the information processing apparatus 1 can more appropriately provide the information of the target that the user U may be interested in.

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. FIG. Thereby, the information processing apparatus 1 can more appropriately provide the information of the target that the user U may be interested in.

As described above, the embodiments of the present application have been described in detail with reference to the drawings, but these are examples, and various modifications and improvements, including the aspects described in the section of the disclosure of the invention, can be made based on the knowledge of those skilled in the art. It is possible to implement the invention in other forms.

Also, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

1 information processing device 2, _{2 1} to 2 _n terminal device 10 communication unit 11 storage unit 12 processing unit 20 search information storage unit 21 user information storage unit 22 content storage unit 23 interest space information storage unit 30 reception unit 31 search unit 32 Learning unit 33 Identifying unit 34 Estimating unit 35 Providing unit 40 First identifying unit 41 Second identifying unit 100 Information processing system N Network

Claims (10)

two or more users' interests and a specific object in an interest space in which each of a plurality of objects is indicated by a vector according to relevance to the interests of a plurality of users, and the vector is updated every predetermined period; an estimating unit for estimating the difference in recognition of the specific target between the two or more users based on the difference between the two or more users at similar past times;
and a providing unit configured to provide at least one of the two or more users with information corresponding to the recognition difference estimated by the estimating unit. The estimation unit
2. The information processing apparatus according to claim 1, wherein, when the difference is equal to or greater than a predetermined threshold, a difference in recognition of the specific object between the two or more users is estimated. The providing unit
3. Information according to claim 1 or 2, wherein the information based on the misrecognition is provided to the user who is the most recent of the past points in time similar to the specific object among the two or more users. processing equipment. The providing unit
Information on the specific target is provided to a user other than the user whose point in time is the newest among past points in time similar to the specific target among the two or more users. 4. The information processing device according to any one of 3. The information according to any one of claims 1 to 4, further comprising a first identifying unit that identifies a plurality of users having a predetermined relationship among the plurality of users as the two or more users. processing equipment. The first specifying unit
6. The information processing apparatus according to claim 5, wherein a plurality of users who communicate with each other are identified as the plurality of users having the predetermined relationship. The search used by the user in a trained model that has learned the features of each of the plurality of search terms used by the plurality of users as having similar features to two or more search terms that satisfy a predetermined condition. The information according to any one of claims 1 to 6, further comprising a second specifying unit that specifies the interest of the user based on the vector of the search word obtained by inputting the word. processing equipment. The information processing apparatus according to claim 7, further comprising a learning unit that generates the trained model using the plurality of search terms used by the plurality of users. A computer-executed information processing method comprising:
two or more users' interests and a specific object in an interest space in which each of a plurality of objects is indicated by a vector according to relevance to the interests of a plurality of users, and the vector is updated every predetermined period; an estimation step of estimating a difference in recognition of the specific object between the two or more users based on the difference between the two or more users at similar past times;
and a providing step of providing at least one of the two or more users with information corresponding to the recognition difference estimated by the estimating step. two or more users' interests and a specific object in an interest space in which each of a plurality of objects is indicated by a vector according to relevance to the interests of a plurality of users, and the vector is updated every predetermined period; an estimation procedure for estimating the difference in recognition of the specific target between the two or more users based on the difference between the two or more users at similar past times;
and a provision step of providing at least one of the two or more users with information corresponding to the recognition difference estimated by the estimation step.

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