JP7791003B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

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

Various technologies have been proposed for providing information to users. For example, Patent Document 1 proposes a technology for delivering advertisements according to the quadrant to which a user's category of interest belongs, out of multiple quadrants that are classified based on factors related to commercial transactions.

Japanese Patent Application Laid-Open No. 2021-099631

However, there is room for improvement in the above-mentioned conventional technology. The above-mentioned conventional technology provides information that is dependent on the quadrant, and there is room for improvement in terms of providing information flexibly. It is desirable to provide appropriate information on subjects that may be of common interest to multiple users.

The present application has been made in light of the above, and aims to provide an information processing device, information processing method, and information processing program that can appropriately provide information on subjects that may be of common interest to multiple users.

The information processing device according to the present application includes an estimation unit and a provision unit. The estimation unit estimates a misperception of a specific target between two or more users based on differences between the two or more users at past times when the interests of the two or more users were similar to the specific target in an interest space in which each of multiple targets is represented by a vector according to the relevance to the interests of the multiple users and the vectors are updated at predetermined intervals. The provision unit provides information corresponding to the misperception estimated by the estimation unit to at least one of the two or more users.

One aspect of this embodiment has the effect of appropriately providing information on subjects that may be of common interest to multiple 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 the configuration of an information processing system including the information processing apparatus according to the embodiment. FIG. 3 is a diagram illustrating an example of a user information table stored in the 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 the content storage unit of the information processing device according to the embodiment. 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. FIG. 6 is a diagram illustrating an example of a past time point when the interest identified by the second identification unit of the information processing device according to the embodiment was similar to a specific target. FIG. 7 is a diagram illustrating an example of a process of estimating a misrecognition of a specific target between two or more specific target users by an estimation unit of the information processing apparatus according to the embodiment. FIG. 8 is a flowchart showing a processing procedure performed by the processing unit of the information processing apparatus according to the embodiment. FIG. 9 is a flowchart showing an information providing procedure performed by the processing unit of the information processing apparatus according to the embodiment. FIG. 10 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing apparatus according to the embodiment.

Below, detailed explanations will be given of the information processing device, information processing method, and information processing program (hereinafter referred to as "embodiments") according to the present application, with reference to the drawings. Note that the information processing device, information processing method, and information processing program according to the present application are not limited to these embodiments. Furthermore, the embodiments can be combined as appropriate to the extent that the processing content is not contradictory. Furthermore, the same components in the following embodiments will be assigned the same reference numerals, and redundant explanations will be omitted.

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

First, the search process and model generation process will be described. The information processing device 1 shown in Fig. 1 provides search services to users _U1 to _Un . For example, the information processing device 1 has a search target database, which is a database in which search targets are indexed and stored, and executes search processing using information from the search target database. For example, the information from the search target database is stored in a storage unit 11 (see Fig. 2).

1, users U ₁ to U _n operate terminal devices 2 ₁ to 2 _n to cause terminal devices 2 ₁ to 2 _n to execute a process of transmitting a search query from terminal devices 2 ₁ to 2 _n to information processing device 1 (steps S1 ₁ to S1 _n ), where n is an integer of 2 or greater.

For example, in step _S11 , user _U1 operates terminal device ₂₁ to cause terminal device 21 to execute a process of transmitting a search query from terminal device ₂₁ to information processing device 1. Also, in step _S1n , user _Un operates terminal device _2n to cause terminal device _2n to execute a process of transmitting a search query from terminal device _2n to information processing device _1. Hereinafter, when referring to each of users _U1 to _Un without distinguishing them individually, they may be referred to as user U, and when referring to each of terminal devices ₂₁ to _2n without distinguishing them individually, they may be referred to as terminal device 2.

A search query includes one or more search terms (search keywords) entered into the terminal device 2 by the user U. For example, if the user U enters "sneakers" into the terminal device 2 as one or more search terms, the search query includes "sneakers." Also, if the user U enters "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.

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 a search process based on the search query received in steps _S21 to _S2n (steps _S31 to _S3n ). For example, in step _S31 , the information processing device 1 executes a search process to search the search target database for objects corresponding to one or more search terms included in the search query transmitted from terminal device ₂₁ and received in step _S21 . In addition, in step _S3n , the information processing device 1 executes a search process to search the search target database for objects corresponding to one or more search terms included in the search query transmitted from terminal device _2n and received in step _S2n .

Next, the information processing device 1 transmits the search results resulting from the search processing in steps _S31 to _S3n to the terminal devices ₂₁ to _2n (steps _S41 to _S4n ). For example, in step S41, the information processing device ₁ transmits the search results resulting from the search processing in step _S31 to the terminal device _21. In addition, in step _S4n , the information processing device 1 transmits the search results resulting from the search processing in step _S3n to the terminal device _2n .

Next, the information processing device 1 generates an interest model based on the search query received in steps S2 ₁ to S2 _n (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. Furthermore, the M-dimensional vector may be represented, for example, by a distributed representation or by a representation other than the distributed representation. Hereinafter, the M-dimensional vector will be simply referred to as a vector.

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 predetermined conditions as having similar characteristics. The two or more search terms that satisfy the predetermined conditions 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.

The information processing device 1 uses two or more search terms that satisfy predetermined conditions as training data and performs training 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 predetermined conditions, regardless of when the search queries were sent.

Note that a search term consists of one search keyword, but may also consist of two or more search keywords. For example, if the search query contains the string "ladies' sneakers," the information processing device 1 will treat "sneakers" and "ladies" as different search terms, but it can also treat the pair of "sneakers" and "ladies" as a single search term.

The information processing device 1 generates an interest model that outputs vectors (e.g., distributed representations) from information indicating a target, such as a search term, using, for example, Deep Structured Semantic Model (DSSM) technology, which uses Long Short-Term Memory (LSTM), 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 for generating an interest model that outputs vectors from information indicating a target is not limited to the above-mentioned example.

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, etc. in 2022.

Next, the information provision process will be described. The information processing device 1 determines a past time when the interests of two or more users U were similar to a specific target in the interest space, which is updated every period TA (step S6).

In step S6, the information processing device 1 first identifies the range of interests of each of two or more users U in the interest space, which is updated every period TA. In the interest space, each of multiple targets is represented by a vector according to its relevance to the interests of multiple users U. In the interest space, the targets represented by vectors are not limited to targets represented by search terms, and may be targets other than those represented by search terms.

The information processing device 1 inputs information indicating the target into the interest model generated in step S5, and performs a process for each target to obtain the target vector output from the interest model, thereby generating an interest space including the vectors of each target. For example, the information processing device 1 generates an interest space for each period TA using the interest model for each period TA.

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

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

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

In addition, the information processing device 1 identifies multiple users U having a predetermined relationship as the above-mentioned two or more users U. The two or more users U are multiple users U having a predetermined relationship.

A predetermined relationship is, for example, a relationship in which communication takes place between users U. Communication is a conversation between users U using text or voice, and is carried out, for example, by voice calls, chat, email, or video conferencing. The information processing device 1 provides services such as voice calls, chat services, email services, and video conferencing services, and can identify two or more users U who are communicating with each other based on information obtained from these services.

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

In addition, the information processing device 1 determines as a specific target an object that has been similar to the interests of two or more users U who have a predetermined relationship in the range of interests updated every period TA. The information processing device 1 then determines a past time when the interests of two or more users U who have a predetermined relationship were similar to the specific target. Note that the specific target may be information that has been predetermined by the user of the information processing device 1, etc.

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

Furthermore, in step S7, the information processing device 1 determines the most recent past time at which the interests of user U were similar to a specific target. The information processing device 1 can then determine the difference in the most recent time between two or more users U as the inter-user time difference.

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

For example, suppose that two or more users U having a predetermined relationship are users _U1 and _Un , and that both users _U1 and _Un have had similar interests in a serialized novel B in a monthly magazine A in the past. In this case, the information processing device 1 determines that the serialized novel B in the monthly magazine A is the specific target.

Furthermore, let us assume that the most recent interest space among user _U1 's interest spaces that were similar to serialized novel B in monthly magazine A in the past is the interest space three periods TA3 (= TA × 3) ago, and that the most recent interest space among user _Un's interest spaces that were similar to serialized novel B in monthly magazine A in the past is the interest space one period TA1 (= TA × 1) ago. In this case, the information processing device 1 determines that the time difference between users is two periods TA2 (= TA3 - TA1).

Then, the information processing device 1 estimates that user _U1 is aware of the content of serialized novel B in monthly magazine A up to three periods TA3 ago, and estimates that user _Un is aware of the content of serialized novel B in monthly magazine A up to one period TA1 ago. In this way, the information processing device 1 estimates the difference in awareness of a specific object between users _U1 and _Un based on the time difference between the users.

Then, the information processing device 1 provides at least one of the users _U1 and _Un with information corresponding to the difference in recognition of a specific object between the users _U1 and _Un . For example, the information processing device 1 provides the user _Un with information that the user _U1 may only be aware of the contents of the serialized novel B in the monthly magazine A up to three periods TA3 ago. The information processing device 1 can also provide the user _U1 with information that the user _Un may be aware of the contents of the serialized novel B in the monthly magazine A up to one period TA1 ago.

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

2. Configuration of the information processing system
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 the 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 able to communicate with each other via a wired or wireless connection.

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, for example, by 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 that it can communicate with them.

The information processing device 1 also provides online services such as web services to the terminal device 2 of each user U. For example, in addition to the search service and information provision service described above, the information processing device 1 also provides other online services such as SNS (Social Networking Service), electronic commerce (EC) sites, posting sites, electronic payments, online games, online banking, online trading, accommodation and ticket reservations, video and music distribution, news, maps, route searches, route guidance, line information, operation information, and weather forecasts. The information processing device 1 can also link with various servers that provide the online services described above and act as an intermediary for the online services.

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

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.

3.1. Communication Unit 10
The communication unit 10 is realized by, for example, a network interface card (NIC). 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 transmits and receives information to and from terminal devices 2 ₁ to 2 _n via the network N.

[3.2. Storage unit 11]
The storage unit 11 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) 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 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.

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

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 information for each user U, such as "User ID (Identifier)," "User Name," "Attributes," "Purchase History," "Search History," and "Other History." "User ID" is identification information unique to each user U. "User Name" is information indicating the name of the user U.

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

In the example shown in Figure 3, user U's demographic attributes include information such as "gender" and "age." "Gender" is information indicating user U's gender, and "age" is information indicating user U's age. User U's demographic attributes 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.

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

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

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

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.

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

For example, in Figure 4, the content with content ID "C1" is "CO1." Note that in the example shown in Figure 4, the content is expressed using abstract codes such as "CO1," but the content may also be in the form of a file containing specific numerical values, specific character strings, and various types of information. Note that the content storage unit 22 is not limited to the above example, and may store various types of information depending on the purpose.

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. Furthermore, the period TA may be a period during which the number of search queries or new search terms newly received by the information processing device 1 exceeds a predetermined threshold.

Figure 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 Figure 5, the interest space information stored in the interest space information storage unit 23 includes a "target ID," a "target," a "vector," and the like for each target.

The "target ID" is identification information unique to each target. A "target" is a target that may be of interest to 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.

For example, in Figure 5, the object with object ID "Q1" is "O1" and the vector is "V1". Note that in the example shown in Figure 5, the object is represented by an abstract code such as "O1", but the object may be represented by a specific string of characters or by an image, etc. Also, in the example shown in Figure 5, the vector is represented 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.

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 (examples of information processing programs) 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).

As shown in FIG. 2, the processing unit 12 has a reception unit 30, a search unit 31, a learning unit 32, an identification unit 33, an estimation unit 34, and a provision unit 35, and realizes or executes the information processing functions and actions 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.

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.

The reception unit 30 receives a search query containing one or more search terms (search keywords) entered 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 that specifies content.

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.

The search unit 31 can also search 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 multiple pieces of content stored in the content storage unit 22. The search unit 31 transmits the searched content as a search result to the terminal device 2 that sent the search query via the network N and the communication unit 10.

3.3.3. Learning Unit 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 multiple search terms, assuming that two or more search terms that satisfy predetermined conditions have similar characteristics.

Two or more search terms that satisfy a 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 a predetermined condition, regardless of when the search queries were sent.

The learning unit 32 generates training data using search terms included in search queries with the search time 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 two or more search terms that satisfy predetermined conditions as training data, so that the vectors of the two or more search terms are similar to each other.

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 example described above; any known technology can be used as long as learning can be performed so that vectors indicating multiple similar targets are similar to each other.

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 storage unit 23 in the storage unit 11 for each period TA.

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

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

Communication is conversation between users U using text or voice, and is carried out, for example, by voice call, chat, email, or video conference. The first identification unit 40 can identify two or more users U who are communicating with each other based on the user U's usage history of voice call services, chat services, email services, or video conference services.

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

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

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

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

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.

For example, the interest space for October 2021 is obtained by inputting information indicating each of multiple targets into the interest model for October 2021, and the interest space for February 2022 is obtained by inputting information indicating each of multiple targets into the interest model for February 2022. In addition, the interest space is updated sequentially to the state of the interest space for October 2021, the state of the interest space for November 2021, the state of the interest space for December 2021, the state of the interest space for January 2022, and the state of the interest space for February 2022.

The second identification unit 41 identifies, as the target of interest of the specific target user, objects included in the range of interests of the specific target user in the interest space. The second identification unit 41 identifies the range of interests 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 acquires, for each specific target user, from the user information storage unit 21, search terms included in all search queries sent from the terminal device 2 by the same specific target user within each period TA. Then, the second identification unit 41 performs a process for each period TA in which it 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 specific target user. Note that the second identification unit 41 can also calculate the average vector by, for example, weighting the vector of a search term that is more recently received.

The second identification unit 41 identifies, for each specific target user, a range similar to the user interest vector of the specific target user as the interest range of user U. The range of similarity to the user interest vector is, for example, a range of cosine similarity determined in advance, but is not limited to this example. Note that the second identification unit 41 can also identify the interest range of the specific target user, for example, each time the number of new search queries or new search terms for the same specific target user exceeds a predetermined threshold.

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

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

6 is a diagram showing an example of a past time point when the interest identified by the second identification unit 41 of the information processing device 1 according to the embodiment was similar to a specific object. Fig. 6 shows a portion of an interest space visualized by reducing the dimension of the M-dimensional interest space. Fig. 6 shows 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 position P ₁ (t ₀ ) of O1 in the latest interest space.

6 also shows the interest range _R1 (t _-2 ) of user _U1 two periods ago, the interest range _R1 (t _-1 ) of user _U1 one period ago, the interest range _R1 ( _t0 ) of the latest user _U1 , the interest range R2(t _-2 ) of user _U2 two periods ago, the interest range _R2 (t _-1 ) of user _{U2 one} period ago, and the interest range _R2 ( _t0 ) of the latest user _U2 . In the example shown in FIG. 6, users _U1 and _U2 are specific target users.

As shown in Figure 6, target O1 is within user _U1 's range of interests from the interest space two periods ago to the latest interest space, but only the interest space two periods ago is within user _U2 's range of interests. In this case, the second identification unit 41 determines that the time when user _U1 's interests were similar to the specific target was from the period TA two periods ago to the latest period TA, and determines that the time when the specific target was within user _U2 's range of interests was the period TA two periods ago. If the latest interest space is the interest space of February 2022, the interest space one period ago would be, for example, the interest space of January 2022, and the interest space two periods ago would be, for example, the interest space of December 2021.

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

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

7 is a diagram showing an example of an estimation process of misrecognition of a specific target between two or more specific target users by the estimation unit 34 of the information processing device 1 according to the embodiment. Similar to FIG. 6, FIG. 7 shows a part of an interest space visualized by reducing the dimension of the M-dimensional interest space, and indicates whether there is similarity between users _U1 , _Un and target _O1 from the interest space two periods ago to the latest interest space.

7, in the interest space two periods TA2 ago, user U _n and target O ₁ are similar, but in the interest space one period TA1 ago and the latest interest space, user U _n and target O ₁ are dissimilar. On the other hand, user U ₁ is similar to target O ₁ from the interest space two periods TA2 ago to the latest interest space.

Therefore, the estimation unit 34 estimates that the user U _n recognized the content of the target _O1 two periods TA2 ago, and estimates that the user _U1 recognized the content of the target _O1 in the latest period TA. Then, the estimation unit 34 estimates that the time point when the user U _n recognized the content of the target _O1 was the time point in the period TA two periods TA2 ago, but the time point when the user _U1 recognized the content of the target _O1 was the time point in the latest period TA as a difference in recognition of the target _O1 between the users U ₁ and U _n .

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

The estimation unit 34 can also estimate that the user U _n recognized the content of the target _O1 up to two periods TA2 ago, and that the user _U1 recognized the content of the target _O1 from two periods TA2 ago to the latest period TA. In this case, the estimation unit 34 estimates that the user _{U n recognized} the content of the target _O1 from two periods TA2 ago, and that the user _U1 recognized the content of the target _O1 from the period TA two periods TA2 ago to the latest period TA, as the difference in recognition of the target _O1 between the users U ₁ and U n.

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

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

Here, it is assumed that the similarity between users _U1 and U _n and target _O1 from the interest space two periods TA2 ago to the latest interest space is in the state shown in Fig. 7. In this case, the providing unit 35 provides information indicating that user _U1 may have been able to recognize the content and state of target _O1 up to the latest point in time, but user U _n may have only been able to recognize the content and state of target _O1 up to the point two periods TA2 ago, as misidentification information to at least one specific target user among the two or more specific target users.

In addition, the providing unit 35 can provide information indicating that user _U1 may have been able to recognize the content and status of target _O1 from the time two periods TA2 ago to the most recent time, but user _Un may have only been able to recognize the content and status of target _O1 from the time two periods TA2 ago, as misidentification information to at least one of the two or more specific target users.

The providing unit 35 provides the misidentification information to, for example, a first recognized user who is a specific target user who was most recently identified among two or more specific target users at a past time similar to the specific target. In this case, the providing unit 35 can provide, for example, information indicating that the user U _n may not have recognized the content or state of the target _O1 at a time two periods TA2 ago to the first recognized user as the misidentification information. The providing unit 35 can also provide, for example, information indicating that the user U _n may have only recognized the content or state of the target _O1 at a time two periods TA2 ago to the first recognized user as the misidentification information.

The providing unit 35 can also provide information about a specific target to, for example, a second recognized user who is a specific target user other than the first recognized user among the two or more specific target users. In this case, the providing unit 35 can provide the second recognized user with information indicating that the user _U1 may have recognized the content or state of the target _O1 at the most recent time as misidentification information. The providing unit 35 can also provide the second recognized user with information indicating that the user _U1 may have recognized the content or state of the target _O1 from the period TA two periods TA2 ago to the most recent period TA as misidentification information.

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

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

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

As shown in FIG. 8, the processing unit 12 of the information processing device 1 determines whether it is time to start the learning process for the interest model (step S10). The learning process for the interest model occurs, for example, every period TA, but is not limited to this example.

When the processing unit 12 determines that it is time to perform the interest model learning process (Step S10: Yes), it generates an 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 memory unit 11 (Step S12).

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

If the processing unit 12 determines that it is time to determine the user's interests (step S13: Yes), it identifies the user U's points of interest based on the user U's search query (step S14).

When the processing of step S14 is completed, or when it is determined that the timing for determining user interests has not yet arrived (step S13: No), the processing unit 12 determines whether the timing for determining whether the user is a target of information provision has arrived (step S15). The timing for determining whether the user is a target of information provision is the timing after the user U's location of interests has been identified in step S14. For example, the timing for determining whether the user is a target of information provision is the timing immediately after the user U's location of interests has been 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 the time has come to determine whether or not information should be provided (step S15: Yes), it performs information provision processing (step S16). The processing in step S16 corresponds to steps S20 to S24 shown in Figure 9 and will be described in detail later.

When the processing of step S16 is completed, or when it is determined that the time to determine whether information should be provided has arrived (step S15: No), the processing unit 12 determines whether the time to end the operation has arrived (step S17). The processing unit 12 determines that the time to end the operation has arrived, for example, when the power to the information processing device 1 is turned off.

If the processing unit 12 determines that the operation end timing has not yet arrived (step S17: No), it proceeds to step S10. If the processing unit 12 determines that the operation end timing has arrived (step S17: Yes), it terminates the processing shown in FIG. 8.

Figure 9 is a flowchart showing the information provision procedure performed by the processing unit 12 of the information processing device 1 according to the embodiment. As shown in Figure 9, the processing unit 12 identifies two or more users U who have a predetermined relationship with each other (step S20).

Next, the processing unit 12 identifies the ranges of interests of the two or more users U identified in step S20 (step S21). Then, the processing unit 12 estimates the misperceptions of the two or more users U regarding a specific object based on the identified differences between the two or more users U at past points in time (step S22).

Next, the processing unit 12 provides the user U with information corresponding to the misrecognition estimated in step S22 (step S23), and ends the processing shown in Figure 9.

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

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 create 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 that learns the characteristics of each of multiple search terms, assuming that two or more search terms that satisfy predetermined conditions out of multiple search terms used by multiple users U in the target region have similar characteristics.

The processing unit 12 can also create an interest space for each attribute of user U based on an interest model for each attribute of user U. For example, the processing unit 12 can generate an interest model for each specific attribute 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 with a specific attribute have similar characteristics.

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

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

6. Hardware Configuration
The information processing device 1 according to the embodiment described above is realized by, for example, a computer 80 configured as shown in Fig. 10. Fig. 10 is a hardware configuration diagram showing an example of the computer 80 that realizes the functions of the information processing device 1 according to the embodiment. The computer 80 has a CPU 81, a RAM 82, a ROM (Read Only Memory) 83, an HDD (Hard Disk Drive) 84, a communication interface (I/F) 85, an input/output interface (I/F) 86, and a media interface (I/F) 87.

The CPU 81 operates based on programs stored in the ROM 83 or HDD 84 and controls each component. The ROM 83 stores a boot program executed by the CPU 81 when the computer 80 starts up, as well as programs that depend on the computer 80's hardware.

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

The CPU 81 controls output devices such as displays and printers, and input devices such as keyboards and mice, 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 data generated via the input/output interface 86 to the output devices.

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

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

[7. Other]
Furthermore, among the processes described in the above-mentioned embodiments and variations, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above-mentioned documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

Furthermore, the components of each device shown in the figure are functional concepts and do not necessarily have to be physically configured as shown. In other words, the specific form of distribution and integration of each device is not limited to that shown, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

Furthermore, the above-described embodiments and variations can be combined as appropriate to the extent that the processing content is not contradictory.

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

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

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

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

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

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

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

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

The above describes in detail the embodiments of the present application based on the drawings, but this is merely an example, and the present invention can be implemented in other forms that incorporate various modifications and improvements based on the knowledge of those skilled in the art, including the aspects described in the Disclosure of the Invention section.

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

REFERENCE SIGNS LIST 1 Information processing device 2, 2 ₁ to 2 _n terminal devices 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 Identification unit 34 Estimation unit 35 Provision unit 40 First identification unit 41 Second identification unit 100 Information processing system N Network

Claims (10)

an estimation unit that estimates the content of the specific object between the two or more users at a past time point as a misrecognition of the specific object between the two or more users based on a difference between the two or more users at a past time point when each of the interests of the two or more users was similar to the specific object in an interest space in which each of a plurality of objects is represented by a vector and the vector is updated every predetermined period of time;
a providing unit that provides information according to the misrecognition estimated by the estimating unit to at least one of the two or more users ,
The interest space is
The database includes vectors of the plurality of objects obtained by inputting information of the plurality of objects into a trained model that has learned the characteristics of each of the plurality of search terms, which are two or more search terms that satisfy predetermined conditions among the plurality of search terms used by a plurality of users, as having similar characteristics.
1. An information processing device comprising: The estimation unit
The information processing apparatus according to claim 1 , wherein, when the difference is equal to or greater than a predetermined threshold, a misrecognition of the specific object between the two or more users is estimated. The providing unit
The information processing device according to claim 1 or 2, wherein information based on the misrecognition is provided to the user who, among the two or more users, was most recently similar to the specific target at a past time point. The providing unit
The information processing device according to any one of claims 1 to 3, characterized in that information about the specific object is provided to users other than the user who was most recently similar to the specific object among the two or more users. 5. The information processing device according to claim 1, further comprising a first identification unit that identifies, among the plurality of users, a plurality of users having a predetermined relationship as the two or more users. The first identification unit
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 information processing device according to any one of claims 1 to 6, further comprising a second identification unit that identifies the interests of the user based on vectors of the search terms obtained by inputting the search terms used by the users into the trained model that has learned the characteristics of each of the search terms used by the users, with two or more search terms that satisfy predetermined conditions being considered to have similar characteristics. The information processing device 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. 1. A computer-implemented information processing method, comprising:
an estimation process for estimating the content of the specific object between the two or more users at a past time point as a misunderstanding of the specific object between the two or more users based on a difference between the two or more users at a past time point when each of the interests of the two or more users was similar to the specific object in an interest space in which each of a plurality of objects is represented by a vector and the vector is updated every predetermined period of time;
a providing step of providing information corresponding to the misrecognition estimated by the estimating step to at least one of the two or more users ,
The interest space is
The database includes vectors of the plurality of objects obtained by inputting information of the plurality of objects into a trained model that has learned the characteristics of each of the plurality of search terms, which are two or more search terms that satisfy predetermined conditions among the plurality of search terms used by a plurality of users, as having similar characteristics.
An information processing method comprising: an estimation procedure for estimating the content of the specific object between the two or more users at a past time point as a misrecognition of the specific object between the two or more users based on a difference between the two or more users at a past time point when each of the interests of the two or more users was similar to the specific object in an interest space in which each of a plurality of objects is represented by a vector and the vector is updated at a predetermined interval;
a providing step of providing information corresponding to the misrecognition estimated by the estimation step to at least one of the two or more users ;
The interest space is
The database includes vectors of the plurality of objects obtained by inputting information of the plurality of objects into a trained model that has learned the characteristics of each of the plurality of search terms, which are two or more search terms that satisfy predetermined conditions among the plurality of search terms used by a plurality of users, as having similar characteristics.
An information processing program characterized by: