JP7779758B2 - 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 interest to the user.

The present application has been made in consideration 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 interest to a user.

The information processing device according to the present application comprises a first identification unit, a second identification unit, and a provision unit. The first identification unit identifies a first object that has become included in a user's range of interests in an interest space in which each of multiple objects is represented by a vector according to the relevance to the interests of multiple users, and the vector is updated at predetermined intervals. The second identification unit identifies a second object that was similar in the past to the first object identified by the first identification unit. The provision unit provides the user with information about the second object identified by the second identification unit.

One aspect of this embodiment has the effect of being able to appropriately provide information on subjects that may be of interest to the user.

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 first target identified by a first identification unit of the information processing apparatus according to the embodiment. FIG. 7 is a diagram illustrating an example of a second target identified by the second identification unit of the information processing apparatus according to the embodiment. FIG. 8 is a diagram illustrating an example of a second target identified by the second identification unit of the information processing apparatus according to the embodiment. FIG. 9 is a flowchart showing a processing 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 identifies the range of interests of each user U in the interest space (step S6). 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.

Next, the information processing device 1 identifies a first object in the interest space that has become included in the user U's range of interests (step S7). The information processing device 1 then identifies a second object in the interest space that was previously similar to the first object identified in step S7 (step S8). The information processing device 1 then provides the user U with information about the second object identified in step S8 (step S9). The processes of steps S6 to S9 are performed for each user U.

For example, suppose the generation cycle for interest models is one month, and the interest model for February 2022 is the most recent interest model. Furthermore, suppose the interest space for January 2022 is generated using the interest model for January 2022, and the interest space for February 2022 is generated using the interest model for February 2022.

Furthermore, in the interest space in January 2022, the target "tapioca" and the target "maritozzo" are similar to each other, but the target "tapioca" and the target "maritozzo" are not included in the interest range of user _U1 . Furthermore, in the interest space in February 2022, the target "maritozzo" continues to be not included in the interest range of user _U1 , but the target "tapioca" is included in the interest range of user _U1 .

In this case, the information processing device 1 identifies the target "tapioca" as the first target and the target "maritozzo" as the second target. Then, the information processing device 1 provides information on the target "maritozzo" identified as the second target to the user _U1 .

In this way, the information processing device 1 provides user U with information about a second object that was previously similar to a first object that has come to be included in user U's range of interests in the interest space. This allows the information processing device 1 to appropriately provide information about objects that may interest user U.

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 Figure 3, the user information table stored in the user information storage unit 21 includes information such as "User ID (Identifier)," "User Name," "Attributes," and "Search History" for each user U. "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.

"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.

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, a calculation 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 search dates and times within the time period TA. For example, the learning unit 32 acquires two or more search terms that satisfy predetermined conditions for each time period TA from the user information storage unit 21, and generates an interest model using the acquired two or more search terms as training data. For example, the learning unit 32 performs training using 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 first object that has become included in a range of interests of a user U in an interest space in which each of a plurality of objects is represented by a vector according to relevance to the interests of a plurality of users U and the vector is updated for each period TA, and identifies a second object that was similar to the first object in the past. 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 a first object that has become included in the range of interests of a user U in an interest space in which each of multiple objects is represented by a vector according to its relevance to the interests of multiple users U and the vector is updated every period TA.

The first identification unit 40, for example, uses an interest model for each period TA to identify the range of interests of each user U for each period TA. For example, the first identification unit 40 acquires, for each user U, from the user information storage unit 21, search terms included in all search queries sent from the terminal device 2 by the same user U within each period TA. The first identification unit 40 then averages the vectors of the acquired search terms to calculate an average vector, and performs a process for each period TA in which the calculated average vector is determined as the user interest vector for each user U. Note that the first identification unit 40 can also calculate the average vector by, for example, weighting the vector of the search term more heavily the more recent the date and time it was received.

The first identification unit 40 identifies, for each user U, a range similar to the user interest vector of the user U as the user U's interest range. The 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 first identification unit 40 can also identify the user U's interest range, for example, each time the number of new search queries or new search terms for the same user U exceeds a predetermined threshold.

The first identification unit 40 identifies a first object that has become included in the user U's range of interests in the latest interest space from among multiple objects. For example, if there are interest spaces for each month from October 2021 to February 2022, the latest interest space is the interest space for February 2022. The interest spaces for each month from October 2021 to February 2022 are obtained using the interest models 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.

Fig. 6 is a diagram showing an example of a first object identified by the first identification unit 40 of the information processing device 1 according to the embodiment. Fig. 6 shows a part of an interest space visualized by reducing the dimension of an M-dimensional interest space. The example shown in Fig. 6 shows a position P ₁ (t _-2 ) of object O1 in the interest space two periods ago, a position P ₁ (t _-1 ) of object O1 in the interest space one period ago, and a position P ₁ (t ₀ ) of O1 in the latest interest space.

As shown in Figure 6, target O1 is not within the range of interests of user U1 in the interest space two periods ago and the interest space _one period ago, but is within the range of interests of user _U1 in the latest interest space. In this case, the first identification unit 40 identifies target O1 as the first target. Note that if the latest interest space is the interest space of February 2022, the interest space of one period ago is, for example, the interest space of January 2022, and the interest space of two periods ago is, for example, the interest space of December 2021.

Furthermore, the first identification unit 40 can identify as the first object, for example, an object that was not within the range of interests of user U in the interest space up until a predetermined period of time ago, but has now become included in the range of interests of user U in the latest interest space.

In the above example, whether or not a target is within user U's range of interests is determined using the interest space obtained using the interest model for the same period TA and user U's range of interests, but this example is not limited to this. For example, the first identification unit 40 can also determine whether or not a target is within user U's range of interests by using the vector of the target for each period TA in the interest space for each period TA obtained using the interest model for each period TA and user U's range of interests obtained using the latest interest model.

[3.3.4.2. Second specific part 41]
The second identification unit 41 identifies a second object that was similar in the past to the first object identified by the first identification unit 40. Specifically, the second identification unit 41 identifies as the second object an object that was similar to the first object identified by the first identification unit 40 in the space of interest one period or more ago. The similarity range of the first object is, for example, a predetermined range of cosine similarity with the first object, but is not limited to such an example.

Figure 7 is a diagram showing an example of a second object identified by the second identification unit 41 of the information processing device 1 according to the embodiment. As with Figure 6, Figure 7 shows a portion of an interest space visualized by reducing the dimension of the M-dimensional interest space.

7, similar to the example shown in FIG. 6, the position P ₁ (t _-2 ) of the target O1 in the space of interests two periods ago, the position P 1 (t -1 ) of the target O1 in the space of interests one period ago, and the position _{P 1 (} t ₀ ) of the target O1 in the latest space of interests are shown. Also, the example shown in FIG. 7, the position P ₂ (t _-2 ) of the target O2 in the space of interests two periods ago, the position P ₂ (t _-1 ) of the target O2 in the space _of interests one period ago, and the position P ₂ (t ₀ ) of the target O2 in the latest space of interests are shown.

In the interest space two periods ago and the interest space one period ago, the similarity range of object O1 includes object O2, and the second identification unit 41 identifies object O2 as the second object.

The second identification unit 41 can also determine whether the second object was not included in the user U's range of interests during a predetermined period in the past. Figure 8 is a diagram showing an example of a second object identified by the second identification unit 41 of the information processing device 1 according to the embodiment. Similar to Figures 6 and 7, Figure 8 shows a portion of an interest space visualized by reducing the dimension of the M-dimensional interest space.

8 shows a position P ₂ (t ₋₃ ) of the target O2 in the space of interests three periods ago, compared to the state shown in FIG. 7. In the space of interests three periods ago, the target O2 is included in the range of interests of the user U. In this case, the second identification unit 41 determines that the target O2 identified as the second target was included in the range of interests of the user U during a predetermined period in the past.

The predetermined period is, for example, the period up to K periods ago, TC = TA x K. K is, for example, an integer greater than or equal to 10, but is not limited to this example. Here, it is assumed that the second object was included in user U's range of interests in the interest space K+1 periods ago, but was not included in user U's range of interests from the interest space K periods ago to the interest space one period ago, or from the interest space K periods ago to the most recent interest space. In this case, the second identification unit 41 determines that the second object was not included in user U's range of interests during the past predetermined period.

3.3.5. Calculation unit 34
The calculation unit 34 calculates a score Sc of the second object for each second object based on at least one of a past similarity period and a similarity between the second object and the first object.

The past similar period is, for example, the total period during which the second object was similar to the first object, from the period TA K periods ago to the period TA one period ago. For example, if K = 10 and TA = 1 month, then in the period from 10 months ago to 1 month ago, the similar period for the second object that is not within the similarity range of the first object in the period TA 7 months ago and the period TA 4 months ago is 8 months. In this case, the calculation unit 34 sets the score Sc to, for example, 80 (= 10 x 8).

Furthermore, the calculation unit 34 can calculate the score Sc by weighting and adding the periods in which the second object was similar to the first object, with a larger weight being assigned to the newer similar period. For example, if K=10 and the weights from the period TA k periods ago to the period TA one period ago are _wK , wK _-1 , ..., _w2 , _w1 in order, and _wK < _wK-1 < ... < _w2 < _w1 , the calculation unit 34 can calculate the score Sc using, for example, the following formula (1):
Sc=w ₁ ×a ₁ +w ₂ ×a ₂ +・・・・+w _K-1 ×a _K-1 +w _K ×a _K
...(1)

In the above formula (1), _a1 is assigned a "1" if they were similar in the period TA one period ago, and a "0" is assigned if they were not, _a2 is assigned a "1" if they were similar in the period TA two periods ago, and a "0" is assigned if they were not. Furthermore, _aK-1 is assigned a "1" if they were similar in the period TA K-1 times ago, and a "0" is assigned if they were not, and _aK is assigned a "1" if they were similar in the period TA K periods ago, and a "0" is assigned if they were not. Note that hereinafter, when the weights _wK , wK _-1 , ..., _w2 , _w1 are referred to individually without distinction, they may be referred to as weight w.

The calculation unit 34 can also calculate the score of the second object based on the past similarity of the second object to the first object. For example, the calculation unit 34 can calculate the score Sc using the above formula (1) by increasing the weight w the higher the period of time during which the second object has a higher similarity to the first object.

The calculation unit 34 can also calculate the score of the second object based on the pattern of change in the past similarity of the second object with the first object. For example, the calculation unit 34 can increase the score Sc the faster the rate of increase in the past similarity of the second object with the first object.

For example, from a period TA K periods ago to a period TA one period ago, the oldest period TA in which the second object was similar to the first object is defined as a period TA K1 periods ago, and the similarity in period Ts1 is defined as similarity Ds1. Furthermore, from a period K periods ago to a period one period ago, the newest period TA in which the second object was similar to the first object is defined as a period TA K2 periods ago, and the similarity in period Ts2 is defined as similarity Ds2. In this case, the calculation unit 34 can calculate the score Sc using the following formula (2). In formula (2), k1 is a coefficient, and Sc1 is a fixed value.
Sc=Sc1+k1(Ds2-Ds1)/(K2-K1)...(2)

The calculation unit 34 can also calculate the score Sc of the second target by, for example, adding the score Sc calculated using the above formula (1) and the score Sc calculated using the above formula (2).

Furthermore, the calculation unit 34 can increase the score Sc the faster the rate of decline in the past similarity of the second object to the first object. For example, from the period TA K periods ago to the period TA one period ago, the oldest period TA in which the second object was similar to the first object is defined as the period TA K1 periods ago, and the similarity in period Ts1 is defined as similarity Ds1. Furthermore, from the period TA K periods ago to the period TA one period ago, the most recent period in which the second object was similar to the first object is defined as the period TA K2 periods ago, and the similarity in period Ts2 is defined as similarity Ds2. In this case, the calculation unit 34 can calculate the score Sc using the following formula (3). In the following formula (3), k2 is a coefficient, and Sc2 is a fixed value.
Sc=Sc2+k2(Ds1-Ds2)/(K2-K1)...(3)

The calculation unit 34 can also calculate, for example, the score Sc of the second target by adding the score Sc calculated by the above formula (1) and the score Sc calculated by the above formula (3). The calculation unit 34 can also increase the score Sc of the second target, for example, the higher the similarity between the past change in similarity of the second target with the first target and the specific change. The calculation unit 34 can also calculate, as the score Sc of the second target, the sum of the score Sc calculated by the above formula (1) and a score that increases the higher the similarity between the past change in similarity of the second target with the first target and the specific change.

In this way, the calculation unit 34 can calculate the score Sc of each second object based on at least one of the past similarity period and the degree of similarity between the second object and the first object.

[3.3.6. Providing Department 35]
The providing unit 35 provides the user U with information on the second target identified by the identifying unit 33. The information on the second target 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 targets that may be of interest to the user U.

For example, if the second object is "maritozzo," the information on the second object is information on maritozzo, and if the second object is "tapioca," the information on tapioca. The providing unit 35 acquires the information on the second object from the content stored in the content storage unit 22 of the storage unit 11, for example, and transmits the acquired information on the second object to the terminal device 2 via the communication unit 10 and the network N.

Furthermore, when the identification unit 33 determines that the second target was not included in the user U's range of interests during a predetermined period, the provision unit 35 provides the user U with information about the second target. This allows the provision unit 35 to appropriately provide information about targets that are likely to be unknown to the user U but that may be of interest to the user U.

Furthermore, the providing unit 35 can provide the user with information about the second object if the similarity between the second object and the user U's range of interests in the interest space during the latest period TA is not outside a predetermined range. This also allows the providing unit 35 to appropriately provide information about objects that are likely to be unknown to the user U but may be of interest to the user U.

The providing unit 35 can also provide the user U with information on second targets, among the plurality of second targets, whose scores Sc calculated by the calculation unit 34 satisfy predetermined conditions. For example, the providing unit 35 provides the user U with information on second targets, among the plurality of second targets, whose scores Sc calculated by the calculation unit 34 are equal to or greater than a threshold. The providing unit 35 also provides the user U with information on the second target, among the plurality of second targets, whose score Sc calculated by the calculation unit 34 is the highest.

In this way, the providing unit 35 provides information about the second target to the user U based on the score Sc calculated by the calculation unit 34, and therefore, when there are multiple second targets, it is possible to provide more appropriate information about the second target to the user U.

The providing unit 35 can provide the second target information to the user U in a push-type manner or a pull-type manner. For example, the providing unit 35 can display the second target information as a pop-up on the terminal device 2 using an application installed on the terminal device 2, or send the information to the user U's email address by email. The providing unit 35 can also provide the second target information to the user U by sending the second target 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 second target 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. 9. Fig. 9 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. 9, 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.

When the processing unit 12 determines that it is time to determine the target of information provision (Step S15: Yes), it identifies a first target that has become included in the user U's range of interests in the interest space indicated by the interest space information generated in Step S12 (Step S16). Then, the processing unit 12 identifies a second target that was previously similar to the first target identified in Step S16 (Step S17), and provides information about the second target identified in Step S17 to the user U (Step S18).

When the processing of step S18 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 S19). 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 S19: No), it proceeds to step S10. If the processing unit 12 determines that the operation end timing has arrived (step S19: Yes), it terminates the processing shown in FIG. 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 a first identification unit 40, a second identification unit 41, and a providing unit 35. The first identification unit 40 identifies a first object that has become included in a range of interests of a user U in an interest space in which each of a plurality of objects is represented by a vector according to relevance to the interests of a plurality of users U and the vector is updated every predetermined period. The second identification unit 41 identifies a second object that was previously similar to the first object identified by the first identification unit 40. The providing unit 35 provides the user U with information on the second object identified by the second identification unit 41. This allows the information processing device 1 to appropriately provide information on objects that may interest the user U.

The second identification unit 41 also determines whether the second target was not included in the user U's range of interests during a predetermined period in the past. The provision unit 35 provides information about the second target to the user U when the second identification unit 41 determines that the second target was not included in the user U's range of interests during a predetermined period. This allows the information processing device 1 to appropriately provide information about targets that are likely to be unknown to the user U but may be of interest to the user U.

The information processing device 1 also includes a calculation unit 34 that calculates a score Sc for each second object based on at least one of the past similarity period and the degree of similarity between the second object and the first object. The provision unit 35 provides the user U with information on second objects whose scores Sc calculated by the calculation unit 34 satisfy predetermined conditions among the multiple second objects. This allows the information processing device 1 to more appropriately provide the user U with information on objects that may be of interest to the user U.

The calculation unit 34 also calculates the score Sc of the second object based on at least one of the length and recency of the period of similarity between the second object and the first object. This allows the information processing device 1 to more appropriately provide information about objects that may be of interest to the user U.

The calculation unit 34 also calculates the score Sc of the second object based on the past changes in the similarity of the second object with the first object. This allows the information processing device 1 to more appropriately provide information about objects that may be of interest to the user U.

The calculation unit 34 also calculates the score Sc of the second object based on the rate of increase or decrease in the past similarity of the second object with the first object. This allows the information processing device 1 to more appropriately provide information about objects that may interest the user U.

Furthermore, the providing unit 35 provides information about the second target to the user U when the similarity between the second target and the user U's range of interests is not outside a predetermined range. This allows the information processing device 1 to more appropriately provide information about targets that are likely to be unknown to the user U but may be of interest to the user U.

The interest space also includes multiple vectors obtained by inputting information about multiple targets into a trained model that has learned the characteristics of each of multiple search terms, with two or more search terms that satisfy predetermined conditions among multiple search terms used by multiple users U being considered to have similar characteristics. The first identification unit 40 identifies the range of user U's interests based on the vectors of search terms obtained by inputting the search terms used by user U into the trained model. This allows the information processing device 1 to more appropriately provide information about targets 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 Calculation unit 35 Provision unit 40 First identification unit 41 Second identification unit 100 Information processing system N Network

Claims (11)

a first identification unit that identifies a first object that has become included in a user's interest range 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;
a second identification unit that identifies a second object that was similar to the first object identified by the first identification unit in the past;
a providing unit that provides the user with information about the second target identified by the second identifying unit ,
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 second specifying unit is
determining whether the second object was not included in the user's range of interests during a predetermined period of time in the past;
The providing unit
The information processing device according to claim 1, characterized in that, when the second identification unit determines that the second object was not included in the user's range of interests during the predetermined period, information about the second object is provided to the user. a calculation unit that calculates a score of the second object for each of the second objects based on at least one of a past similarity period and a similarity between the second object and the first object;
The providing unit
The information processing device according to claim 1 or 2, further comprising: providing the user with information on a second object whose score calculated by the calculation unit satisfies a predetermined condition among the plurality of second objects. The calculation unit
The information processing apparatus according to claim 3 , wherein the score of the second object is calculated based on at least one of the length and recency of the similar period. The calculation unit
The information processing apparatus according to claim 3 , further comprising: a processor configured to calculate a score for the second object based on a change in the degree of similarity. The calculation unit
The information processing apparatus according to claim 5 , further comprising: calculating a score of the second object based on a rate of increase or a rate of decrease of the similarity. The providing unit
The information processing device according to any one of claims 1 to 6, characterized in that, when a similarity between the second object and the user's range of interests is not outside a predetermined range, information about the second object is provided to the user. The first identification unit
The information processing device according to any one of claims 1 to 7, characterized in that the user's range of interests is identified based on a vector of the search term obtained by inputting the search term used by the user into the trained model. The information processing device according to any one of claims 1 to 8, 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:
a first identification step of identifying a first object that has become included in a user's range of interest 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;
a second identification step of identifying a second object that was similar to the first object identified by the first identification step in the past;
a providing step of providing the user with information on the second target identified by the second identifying step ,
The interest space is
The database includes vectors of the plurality of objects obtained by inputting information of the plurality of objects into a trained model that has learned the characteristics of each of the plurality of search terms, which are two or more search terms that satisfy predetermined conditions among the plurality of search terms used by a plurality of users, as having similar characteristics.
1. An information processing method comprising: a first identification step of identifying a first object that has become included in a user's range of interest 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;
a second identification procedure for identifying a second object that was previously similar to the first object identified by the first identification procedure;
a providing step of providing the user with information on the second target identified by the second identifying step ;
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: