JP2021009574A - Information processing device, information processing method, and information processing program - Google Patents

Abstract

To obtain useful information.SOLUTION: An information processing device comprises a reception unit, a generation unit, and an output unit. The reception unit accepts a plurality of pieces of object information indicating a classification object, and the number of specified clusters, which is the number of clusters when the plurality of pieces of object information is classified into clusters. The generation unit generates specified cluster information on a cluster generated by classifying the plurality of pieces of object information into clusters of the number of specified cluster. The generation unit outputs the specified cluster information generated by the generation unit.SELECTED DRAWING: Figure 1

Description

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

In recent years, with the rapid spread of the Internet, for example, techniques related to analysis using various information on the Internet have been provided. For example, a technique has been proposed in which information on needs for a target provided by a predetermined business operator is extracted based on a search query entered by a user.

JP-A-2019-32776

However, it is not always possible to obtain useful information with the above-mentioned prior art. For example, in the above-mentioned prior art, since only the information regarding the needs for the target provided by the predetermined business operator is extracted, it is possible to extract the information regarding the potential needs for the target provided by the predetermined business operator. I can not say.

The present application has been made in view of the above, and an object of the present application is to provide an information processing device, an information processing method, and an information processing program capable of providing useful information.

The information processing apparatus according to the present application receives a plurality of target information indicating a classification target, a reception unit that receives a specified number of clusters, which is the number of clusters when the plurality of target information is classified into clusters, and the plurality of target information. It is characterized by having a generation unit that generates designated cluster information about clusters generated by classifying the clusters into the specified number of clusters, and an output unit that outputs the designated cluster information generated by the generation unit.

According to one aspect of the embodiment, there is an effect that useful information can be obtained.

FIG. 1 is a diagram showing an example of information processing according to an embodiment. FIG. 2 is a diagram showing a configuration example of an information processing system according to an embodiment. FIG. 3 is a diagram showing a configuration example of the information processing device according to the embodiment. FIG. 4 is a diagram showing an example of the query information storage unit according to the embodiment. FIG. 5 is a diagram showing an example of a vector information storage unit according to the embodiment. FIG. 6 is a diagram showing an example of the cluster information storage unit according to the embodiment. FIG. 7 is a diagram showing an example of a model information storage unit according to the embodiment. FIG. 8 is a diagram showing a configuration example of the terminal device according to the embodiment. FIG. 9 is a flowchart showing an information processing procedure according to the embodiment. FIG. 10 is a diagram showing an example of the generation process of the first model according to the embodiment. FIG. 11 is a diagram showing an example of the generation process of the first model according to the embodiment. FIG. 12 is a diagram showing a configuration example of the generator according to the embodiment. FIG. 13 is a diagram showing an example of the query information storage unit according to the embodiment. FIG. 14 is a diagram showing an example of the vector information storage unit according to the embodiment. FIG. 15 is a diagram showing an example of a model information storage unit according to the embodiment. FIG. 16 is a diagram showing an example of the first model according to the embodiment. FIG. 17 is a flowchart showing a generation processing procedure of the first model according to the embodiment. FIG. 18 is a diagram showing an example of the hardware configuration of the computer that executes the program.

Hereinafter, the information processing apparatus, the information processing method, and the mode for carrying out the information processing program (hereinafter referred to as “the embodiment”) according to the present application will be described in detail with reference to the drawings. Note that this embodiment does not limit the information processing apparatus, information processing method, and information processing program according to the present application. Further, in each of the following embodiments, the same parts are designated by the same reference numerals, and duplicate description will be omitted.

[1. An example of information processing]
First, an example of information processing according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of information processing according to an embodiment. The information processing according to the embodiment is performed by the information processing device 100 shown in FIG. The information processing device 100 shown in FIG. 1 is a server device that provides a query analysis service to a client (company or the like). Here, the "query" refers to a phrase (character information) used by the user to make an inquiry or request to the database. For example, a "query" includes a search query, which is a phrase used by the user for a search. It should be noted that the "query" in the present invention may include words and phrases such as simple keywords and phrases that have nothing to do with how they are used by the user.

Prior to the description of FIG. 1, the configuration of the information processing system according to the embodiment will be described with reference to FIG. FIG. 2 is a diagram showing a configuration example of an information processing system according to an embodiment. As shown in FIG. 2, the information processing system 1 includes a terminal device 10, a search server 20, a generation device 50, and an information processing device 100. The terminal device 10, the search server 20, the generation device 50, and the information processing device 100 are connected to each other via a predetermined network N so as to be communicable by wire or wirelessly. The information processing system 1 shown in FIG. 2 includes an arbitrary number of terminal devices 10, an arbitrary number of search servers 20, an arbitrary number of generation devices 50, and an arbitrary number of information processing devices 100. May be good.

The terminal device 10 is an information processing device used by a user who is in charge of a client (company or the like). The terminal device 10 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a mobile phone, a PDA (Personal Digital Assistant), or the like. In the example shown in FIG. 1, the terminal device 10 is a notebook PC. In the following, the terminal device 10 may be equated with the user. That is, in the following, the user can be read as the terminal device 10.

The terminal device 10 determines a plurality of queries indicating the classification target and a designated number of clusters, which is the number of clusters when classifying the plurality of queries into clusters, according to the operation of the user U11 who is in charge of the client (company or the like). It is transmitted to the information processing device 100. Further, the terminal device 10 receives from the information processing device 100 the designated cluster information regarding the clusters generated by classifying the plurality of queries into clusters having a designated number of clusters. The terminal device 10 displays the received designated cluster information on the screen of the terminal device 10. In the example shown in FIG. 1, the terminal device 10 displays the partial content C12 displaying the received designated cluster information on the screen of the terminal device 10.

The search server 20 is a server device that provides a search service. For example, the search service provided by the search server 20 is a comprehensive search service capable of searching for any information. The search server 20 stores information about the search query entered by the user. Specifically, the search server 20 stores information regarding the user's search history. Further, the search server 20 transmits information about the search query input by the user to the generation device 50 in response to the request of the generation device 50.

The generation device 50 is a server device that generates the first model M1. The generation device 50 generates the first model M1 by executing a process described later. The first model M1 is a model that outputs a distributed representation of character information (for example, a search query) when character information is input. The distributed representation may be a vector. Here, the distributed representation of the character information output from the first model M1 includes feature information indicating the user's search intention when the character information is input as a search query. Further, the similarity between the distributed representation of the predetermined character information output from the first model M1 and the distributed representation of other character information is the search intention of the user when the predetermined character information is input as a search query. It means that the search intention of the user when other character information is input as a search query is similar. The details of the generation process of the first model by the generation device 50 will be described later.

The information processing device 100 is a server device that provides a query analysis service for a client such as a company to analyze a general user's search trend regarding a keyword (character information) indicating an analysis target that the client desires to analyze the market. Is. In the example shown in FIG. 1, the information processing apparatus 100 accepts a plurality of queries indicating a classification target and a designated number of clusters, which is the number of clusters when the plurality of queries are classified into clusters. Further, the information processing apparatus 100 generates designated cluster information regarding the clusters generated by classifying a plurality of queries into clusters having a designated number of clusters. Then, the information processing device 100 outputs the generated designated cluster information.

Generally, the query analysis service accepts keywords (character information) indicating an analysis target that the client wants to analyze the market from a client such as a company. Then, the query analysis service extracts information on queries similar to the specified keyword from a huge database of queries, and provides the client with information on the extracted queries. For example, the query analysis service converts the character string corresponding to the query and the keyword specified by the client into a distributed expression in order to grasp the similarity between the specified keyword and the query numerically. Then, the query analysis service calculates the similarity between the distributed expression obtained by converting the specified keyword and the distributed expression obtained by converting the character string corresponding to the query, and obtains information about the query similar to the specified keyword. Extract.

Here, since the database owned by the query analysis service has a huge number of queries, the number of distributed representations obtained by converting the character strings corresponding to the queries is also huge. Also, in general, the distributed representation obtained by converting a character string is generally a high-dimensional vector (for example, a vector having hundreds or thousands of dimensions). That is, if the device that provides the query analysis service simply extracts and provides the information on the analysis result of the query similar to the specified keyword, the client who receives the information on the analysis result obtains useful information. It's difficult. For example, when the information provided to the client is provided with a large number of high-dimensional distributed representations mapped to the distributed representation space, or a huge amount of data showing the similarity between the distributed representations is listed. When provided in a state, the amount of information is large and it is difficult for the client to grasp the analysis result at a glance. Therefore, it is difficult for the client to obtain useful information from the provided information.

Therefore, the information processing apparatus 100 according to the present invention accepts a plurality of target information indicating a classification target and a designated number of clusters, which is the number of clusters when the plurality of target information is classified into clusters. Further, the information processing apparatus 100 generates designated cluster information regarding the clusters generated by classifying the plurality of target information into clusters having a designated number of clusters. Then, the information processing device 100 outputs the generated designated cluster information. It is assumed that the information processing apparatus 100 according to the present invention has received, for example, 100 target information indicating a classification target and a designated number of clusters "5". In this case, the information processing apparatus 100 according to the present invention can divide 100 target information indicating a classification target into five clusters of target information having similar characteristics. That is, the information processing apparatus 100 according to the present invention can extract five cluster information, which can be said to be a summary of the features of the 100 target information, from the 100 target information. Then, the information processing apparatus 100 according to the present invention can provide the features of 100 target information in a summary of five cluster information. As described above, the information processing apparatus 100 according to the present invention can provide the features of a large number of target information indicating the classification target in a summary of a small amount of information. Therefore, the information processing apparatus 100 according to the present invention can obtain useful information.

From here, the flow of information processing will be described with reference to FIG. In FIG. 1, the information processing device 100 transmits the partial content C11 of the content C1 to the terminal device 10 in response to the request of the terminal device 10. Specifically, the information processing device 100 transmits the input field F11 capable of inputting the number of clusters, the input field F12 capable of inputting a plurality of queries, and the information input in the input fields to the information processing device 100. The partial content C11 including the button B11 is transmitted to the terminal device 10.

The terminal device 10 receives the partial content C11 of the content C1 from the information processing device 100. When the terminal device 10 receives the partial content C11, the terminal device 10 displays the received partial content C11 on the screen of the terminal device 10.

The user U11 of the terminal device 10 performs an operation of inputting the number of clusters in the input field F11 included in the partial content C11 displayed on the screen of the terminal device 10. In the example shown in FIG. 1, the user U11 performs an operation of inputting the number of clusters “3” (number of clusters CN) into the input field F11.

Further, the user U11 of the terminal device 10 performs an operation of inputting a plurality of queries into the input field F12 included in the partial content C11 displayed on the screen of the terminal device 10. In the example shown in FIG. 1, the user U11 performs an operation of inputting 12 queries Q1-1 to Q1-12 into the input field F12. Specifically, the user U11 performs an operation of inputting each character string separated by a delimiter into the input field F12 as one query. For example, the user U11, who is in charge of the automobile manufacturer # 1, has six queries "vehicle type T11" (query Q1-1) indicating the names of six types of automobiles that are products of the company (automobile manufacturer # 1). "Vehicle type T12" (query Q1-2), query "vehicle type T13" (query Q1-3), query "vehicle type T14" (query Q1-4), query "vehicle type T15" (query Q1-5), query "vehicle type" The operation of inputting "T16" (query Q1-6) into the input field F12 is performed. In addition, the user U11 uses six queries "vehicle type T21" (query Q1-7) and a query "vehicle type T22" (query Q1-) indicating the names of six types of vehicles that are products of competitor automobile manufacturer # 2. 8), query "vehicle type T23" (query Q1-9), query "vehicle type T24" (query Q1-10), query "vehicle type T25" (query Q1-11), query "vehicle type T26" (query Q1-12) Is input to the input field F12.

Subsequently, the user U11 of the terminal device 10 performs an operation of selecting the transmission button B11 included in the partial content C11 displayed on the screen of the terminal device 10. When the transmission button B11 is selected according to the operation of the user U11, the terminal device 10 has the number of clusters "3" input in the input field F11 and the 12 queries Q1-1 to Q1 input in the input field F12. -12 is transmitted to the information processing device 100.

The information processing device 100 receives the number of clusters "3" and 12 queries Q1-1 to Q1-12 from the user U11. Specifically, the information processing device 100 receives the number of clusters "3" and 12 queries Q1-1 to Q1-12 from the terminal device 10.

When the information processing device 100 receives the number of clusters "3" and 12 queries Q1-1 to Q1-12, the distributed representation QV1- of the queries Q1-1 to Q1-12 generated using the first model M1. Obtain 1 to QV1-12 (see FIG. 5). Subsequently, when the information processing apparatus 100 acquires the distributed expressions QV1-1 to QV1-12, the acquired distributed expressions QV1-1 to QV1-12 are classified into clusters having the number of clusters "3" by using the k-means method. To do. The information processing apparatus 100 uses any clustering method, not limited to the k-means method, as long as the acquired distributed representations QV1-1 to QV1-12 can be classified into clusters having the number of clusters "3". May be good.

Further, the information processing apparatus 100 classifies the distributed expressions QV1-1 to QV1-12 corresponding to the queries Q1-1 to Q1-12 into clusters having the number of clusters "3", and thereby each query corresponding to each distributed expression. Is classified into clusters with the number of clusters "3". In this way, the information processing apparatus 100 classifies each query corresponding to each distributed representation into clusters having the number of clusters "3", thereby generating cluster information regarding the cluster into which each query is classified.

For example, the information processing apparatus 100 classifies the distributed representation QV1-1, the distributed representation QV1-2, the distributed representation QV1-3, the distributed representation QV1-7, and the distributed representation QV1-8 into one cluster (cluster CL1). Since the information processing apparatus 100 has classified the distributed expression QV1-1 into the cluster CL1, the query Q1-1 corresponding to the distributed expression QV1-1 is classified into the cluster CL1. Further, since the information processing apparatus 100 classifies the distributed expression QV1-2 into the cluster CL1, the query Q1-2 corresponding to the distributed expression QV1-2 is classified into the cluster CL1. Similarly, since the information processing apparatus 100 classifies the distributed representation QV1-3, the distributed representation QV1-7, and the distributed representation QV1-8 into the cluster CL1, the queries Q1-3 and the distributed representation QV1 corresponding to the distributed representation QV1-3. Query Q1-7 corresponding to -7 and query Q1-8 corresponding to the distributed representation QV1-8 are classified into cluster CL1. In this way, the information processing apparatus 100 generates cluster information regarding the cluster CL1 in which the query Q1-1, the query Q1-2, the query Q1-3, the query Q1-7, and the query Q1-8 are classified.

Here, the distributed representation of the character information generated by using the first model M1 includes feature information indicating the user's search intention when the character information is input as a search query. For example, the distributed expression QV1-1 of the query Q1-1 includes feature information indicating the user's search intention when the character information "vehicle type T11" corresponding to the query Q1-1 is input as a search query. There is. Further, the distributed expression QV1-2 of the query Q1-2 includes feature information indicating the user's search intention when the character information "vehicle type T12" corresponding to the query Q1-2 is input as a search query. There is.

Further, the similarity between the distributed representation of the predetermined character information output from the first model M1 and the distributed representation of other character information is the search intention of the user when the predetermined character information is input as a search query. It means that the search intention of the user when other character information is input as a search query is similar. For example, the similarity between the distributed expression QV1-1 of the character information "vehicle type T11" and the distributed expression QV1-2 of the character information "vehicle type T12" is used when the character information "vehicle type T11" is input as a search query. It means that the search intention of the user and the search intention of the user when the character information "vehicle type T12" is input as a search query are similar.

Further, in general, data classified into the same cluster using a clustering method such as the k-means method have similar characteristics. For example, vectors classified into the same cluster using the k-means method have similar characteristics. Therefore, in the present invention, for example, the fact that the distributed representation QV1-1 and the distributed representation QV1-2 are classified into one cluster (cluster CL1) by using the k-means method corresponds to the distributed representation QV1-1. The user's search intention when the character information "vehicle type T11" is input as a search query, and the user's search intention when the character information "vehicle type T12" corresponding to the distributed expression QV1-2 is input as a search query. Means similar to. As described above, in the present invention, the character information (for example, the query) corresponding to the distributed representation classified into the same cluster has a similar search intention of the user when input as a search query. For example, the query Q1-1, the query Q1-2, the query Q1-3, the query Q1-7, and the query Q1-8 classified into the same cluster CL1 have the search intention of the user when they are input as a search query. Similar to each other. That is, the information processing device 100 and a plurality of queries can be classified into clusters having a specified number of clusters according to the user's search intention when each query is input as a search query.

Even for the same plurality of queries, the clusters classified according to the search intention and the normal clustering may be different. For example, assume that "vehicle type T11" and "vehicle type T14" are sports cars, "vehicle type T12" and "vehicle type T15" are family wagon vehicles, and "vehicle type T13" and "vehicle type T16" are light vehicles. In this case, in the case of normal clustering, the sports cars "vehicle type T11" and "vehicle type T14", the family wagon vehicles "vehicle type T12" and "vehicle type T15", and the light vehicles "vehicle type T13" and "vehicle type" Since the vehicle types of "T16" are the same, they may be classified into the same cluster. However, as in the present invention, in clustering according to the search intention, since the vehicle types are not necessarily the same, they are not always classified into the same cluster, and even different vehicle types are classified into the same cluster. May occur. For example, when the search intentions of the users when the sports car "vehicle type T11", the family wagon vehicle "vehicle type T12", and the light vehicle "vehicle type T13" are input as search queries are similar to each other. Is classified into the same cluster CL1 even if they are different vehicle types. In addition, when the search intentions of the users when the sports car "vehicle type T14", the family wagon vehicle "vehicle type T15", and the light vehicle "vehicle type T16" are input as search queries are similar to each other. Is classified into the same cluster CL2 even if they are different vehicle types. As described above, since the information processing apparatus 100 enables clustering according to the search intention, new knowledge based on the clustering according to the search intention can be obtained.

Further, the information processing apparatus 100 uses one cluster (cluster CL2) in which the distributed representation QV1-4, the distributed representation QV1-5, the distributed representation QV1-6, the distributed representation QV1-9, and the distributed representation QV1-10 are different from the cluster CL1. Classify into. Since the information processing apparatus 100 has classified the distributed expression QV1-4 into the cluster CL2, the query Q1-4 corresponding to the distributed expression QV1-4 is classified into the cluster CL2. Further, since the information processing apparatus 100 classifies the distributed expression QV1-5 into the cluster CL2, the query Q1-5 corresponding to the distributed expression QV1-5 is classified into the cluster CL2. Similarly, since the information processing apparatus 100 classifies the distributed representation QV1-6, the distributed representation QV1-9, and the distributed representation QV1-10 into the cluster CL2, the queries Q1-6 and the distributed representation QV1 corresponding to the distributed representation QV1-6 Queries Q1-9 corresponding to -9 and queries Q1-10 corresponding to the distributed representation QV1-10 are classified into cluster CL2. In this way, the information processing apparatus 100 generates cluster information regarding the cluster CL2 in which the query Q1-4, the query Q1-5, the query Q1-6, the query Q1-9, and the query Q1-10 are classified. Further, the query Q1-4, the query Q1-5, the query Q1-6, the query Q1-9, and the query Q1-10 classified into the same cluster CL2 have the search intention of the user when they are input as a search query. Similar to each other.

Further, the information processing apparatus 100 classifies the distributed representation QV1-11 and the distributed representation QV1-12 into one cluster (cluster CL3) different from the cluster CL1 and the cluster CL2. Since the information processing apparatus 100 has classified the distributed representation QV1-11 into the cluster CL3, the query Q1-11 corresponding to the distributed representation QV1-11 is classified into the cluster CL3. Further, since the information processing apparatus 100 classifies the distributed expression QV1-12 into the cluster CL3, the query Q1-12 corresponding to the distributed expression QV1-12 is classified into the cluster CL3. In this way, the information processing apparatus 100 generates cluster information regarding the cluster CL3 in which the queries Q1-11 and the queries Q1-12 are classified. Further, the queries Q1-11 and the queries Q1-12 classified into the same cluster CL3 have similar search intentions of the users when they are input as search queries.

Here, an example of interpretation of the clustering result by the information processing apparatus 100 will be described. For example, the cluster CL1 includes a query indicating the product name of the automobile manufacturer # 1 ("vehicle type T11", "vehicle type T12", "vehicle type T13") and a query indicating the product name of the automobile manufacturer # 2 ("vehicle type T21"). , "Vehicle type T22") are included. In addition, a query indicating the product name of the automobile manufacturer # 1 ("vehicle type T14", "vehicle type T15", "vehicle type T16") and a query indicating the product name of the automobile manufacturer # 2 ("vehicle type T23") are also sent to the cluster CL2. , "Vehicle type T24") are included. On the other hand, the cluster CL3 does not include a query indicating the product name of the automobile manufacturer # 1, but includes only a query indicating the product name of the automobile manufacturer # 2 ("vehicle type T25", "vehicle type T26"). There is. From these results, it can be seen that there is no product corresponding to the product searched with the search intention corresponding to the cluster CL3 in the product of the automobile manufacturer # 1. As a result, for example, the automobile manufacturer # 1 can obtain an analysis result that the product searched with the search intention corresponding to the cluster CL3 has a weakness in the market as compared with the automobile manufacturer # 2. In addition, the automobile manufacturer # 1 that has obtained the analysis result can use the analysis result for marketing policies such as promoting the development of products that are searched with the search intention corresponding to the cluster CL3. In this way, the information processing device 100 makes it possible to provide the client with information regarding market analysis that reflects the user's search intention. For example, the information processing apparatus 100 can provide knowledge about the weaknesses and strengths of the company (other companies) in the market that reflects the user's search intention.

Subsequently, when the information processing apparatus 100 generates the cluster information regarding the three clusters CL1 to CL3, the information processing apparatus 100 transmits the cluster information regarding the generated three clusters CL1 to CL3 to the terminal apparatus 10. When the terminal device 10 acquires the cluster information related to the three clusters CL1 to CL3, the terminal device 10 displays the cluster information related to the clusters CL1 to CL3 in each of the display areas F21 to F23 included in the partial content C12 of the content C1. Control the display.

For example, the information processing apparatus 100 controls the display of the screen so that the name "cluster 1" of the cluster CL1 is displayed above the display area F21 in which the cluster information regarding the cluster CL1 is displayed. Further, the information processing apparatus 100 controls the display of the screen so that the name "cluster 2" of the cluster CL2 is displayed above the display area F22 in which the cluster information regarding the cluster CL2 is displayed. Further, the information processing apparatus 100 controls the display of the screen so that the name "cluster 3" of the cluster CL3 is displayed above the display area F23 in which the cluster information regarding the cluster CL3 is displayed.

As described above, the information processing apparatus 100 receives a plurality of target information indicating the classification target and a designated number of clusters which is the number of clusters when the plurality of target information is classified into clusters. Further, the information processing apparatus 100 generates designated cluster information regarding the clusters generated by classifying the plurality of target information into clusters having a designated number of clusters. Then, the information processing device 100 outputs the generated designated cluster information. As a result, the information processing apparatus 100 can extract cluster information of a specified number of clusters, which can be said to be a summary of the features of a large number of target information, from a large number of target information indicating classification targets. Then, the information processing apparatus 100 can summarize the features of a large number of target information into cluster information of a specified number of clusters and provide the information. As described above, the information processing apparatus 100 can provide the features of a large number of target information indicating the classification target in a summary of a small amount of information. Therefore, the information processing device 100 can obtain useful information.

[2. Information processing device configuration]
Next, the configuration of the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram showing a configuration example of the information processing device 100 according to the embodiment. As shown in FIG. 3, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The information processing device 100 includes an input unit (for example, a keyboard, a mouse, etc.) that receives various operations from the administrator of the information processing device 100, and a display unit (for example, a liquid crystal display, etc.) for displaying various information. You may have.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. Then, the communication unit 110 is connected to the network by wire or wirelessly, and for example, information is transmitted / received between the terminal device 10, the search server 20, and the generation device 50.

(Memory unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk. As shown in FIG. 3, the storage unit 120 includes a query information storage unit 121, a vector information storage unit 122, a cluster information storage unit 123, and a model information storage unit 124.

(Query information storage unit 121)
The query information storage unit 121 stores various information related to the query received from the user. FIG. 4 shows an example of the query information storage unit according to the embodiment. In the example shown in FIG. 4, the query information storage unit 121 has items such as "user ID", "date and time", "specified number of clusters", "query", and "query ID".

The "user ID" indicates identification information for identifying a user. "Date and time" indicates the date and time when the query was received from the user. The "specified number of clusters" indicates the number of clusters specified by the user as the number of clusters when classifying a plurality of queries received from the user into clusters. "Query" indicates a query received from a user. Specifically, the "query ID" indicates identification information for identifying the query received from the user.

In the example shown in the first record of FIG. 4, the query (query Q1-1) identified by the query ID "Q1-1" corresponds to the query "vehicle type T11" shown in FIG. Further, the specified number of clusters "3" means that the number of clusters specified by the user as the number of clusters when classifying the 12 queries Q1-1 to Q1-12 received from the user into clusters is "3". Shown.

(Vector information storage unit 122)
The vector information storage unit 122 stores various information related to the vector, which is a distributed representation of the query. FIG. 5 shows an example of the vector information storage unit according to the embodiment. In the example shown in FIG. 5, the vector information storage unit 122 has items such as “vector ID”, “query ID”, and “vector information”.

The "vector ID" indicates identification information for identifying a vector which is a distributed representation of a query. The "query ID" indicates identification information for identifying the query corresponding to the vector. The "vector information" indicates an N-dimensional (for example, 128-dimensional) vector which is a distributed representation of the query.

In the example shown in the first record of FIG. 5, the vector (vector QV1-1) identified by the vector ID “QV1-1” corresponds to the vector QV1-1 which is the query Q1-1 distributed representation shown in FIG. To do. Further, the query (query Q1-1) identified by the query ID "Q1-1" indicates that the query corresponding to the vector QV1-1 is the query Q1-1. Further, the vector information "QVDT1-1" indicates an N-dimensional vector which is a distributed representation of the query Q1-1.

(Cluster Information Storage Unit 123)
The cluster information storage unit 123 stores various information about the cluster. FIG. 6 shows an example of the cluster information storage unit according to the embodiment. In the example shown in FIG. 6, the cluster information storage unit 123 stores a plurality of data tables for each cluster information generated by classifying a plurality of queries received from the user into a specified number of clusters received from the user. Consists of. In addition, each data table has items such as "cluster ID", "cluster name", and "query ID".

The "cluster ID" indicates identification information for identifying the cluster. "Cluster name" indicates the name of the cluster. The "query ID" indicates identification information for identifying the query.

In the example shown in the first record of FIG. 6, the cluster (cluster CL1) identified by the cluster ID “CL1” corresponds to the cluster CL1 shown in FIG. Further, the cluster name "cluster 1" indicates that the name of the cluster CL1 is cluster 1. Further, the cluster CL1 includes a query (query Q1-1) identified by the query ID "Q1-1", a query (query Q1-2) identified by the query ID "Q1-2", and a query ID "Q1-2". Query identified by "3" (query Q1-3), query identified by query ID "Q1-7" (query Q1-7), and query identified by query ID "Q1-8" (query Q1-8) ) Is classified.

(Model information storage unit 124)
The model information storage unit 124 stores various information related to the learning model generated by the generation device 50. FIG. 7 shows an example of the model information storage unit according to the embodiment. In the example shown in FIG. 7, the model information storage unit 124 has items such as "model ID" and "model data".

The "model ID" indicates identification information for identifying the learning model generated by the generation device 50. The "model data" indicates the model data of the learning model generated by the generation device 50. For example, "model data" stores data for converting a query into a distributed representation.

In the example shown in the first record of FIG. 7, the learning model identified by the model ID “M1” corresponds to the first model M1 shown in FIG. Further, the model data "MDT1" indicates model data (model data MDT1) of the first model M1 generated by the generation device 50.

The model data MDT1 includes an input layer into which a query is input, an output layer, a first element which is any layer from the input layer to the output layer and belongs to a layer other than the output layer, and the first element and the first element. Generated to output a distributed representation of the query input to the input layer from the output layer, including a second element whose value is calculated based on the weight of the element, and in response to the query input to the input layer. The device 50 may function.

Here, it is assumed that the model data MDT1 is realized by the regression model represented by "y = a1 * x1 + a2 * x2 + ... + ai * xi". In this case, the first element included in the model data MDT1 corresponds to input data (xi) such as x1 and x2. Further, the weight of the first element corresponds to the coefficient ai corresponding to xi. Here, the regression model can be regarded as a simple perceptron having an input layer and an output layer. When each model is regarded as a simple perceptron, the first element can be regarded as any node of the input layer, and the second element can be regarded as the node of the output layer.

Further, it is assumed that the model data MDT1 is realized by a neural network having one or a plurality of intermediate layers such as DNN (Deep Neural Network). In this case, the first element included in the model data MDT1 corresponds to either the node of the input layer or the intermediate layer. Further, the second element corresponds to a node in the next stage, which is a node to which a value is transmitted from a node corresponding to the first element. Further, the weight of the first element corresponds to a connection coefficient which is a weight considered for the value transmitted from the node corresponding to the first element to the node corresponding to the second element.

The generation device 50 calculates the variance representation using a model having an arbitrary structure such as the regression model and the neural network described above. Specifically, the model data MDT1 is set with a coefficient so as to output a distributed representation when a query is input. The generation device 50 calculates the distributed representation using such model data MDT1.

In the above example, the model data MDT1 is a model (hereinafter referred to as model X1) that outputs a distributed representation of the query when a query is input. However, the model data MDT1 according to the embodiment may be a model generated based on the result obtained by repeating the input / output of data to the model X1. For example, the model data MDT1 may be a model (hereinafter, referred to as model Y1) learned by inputting the distributed representation output by the model X1 when the query is input. Alternatively, the model data MDT1 may be a model trained to take a query as an input and output a value of the model Y1 as an output.

Further, when the generation device 50 performs the estimation process using GAN (Generative Adversarial Networks), the model data MDT1 may be a model forming a part of GAN.

(Control unit 130)
Returning to the description of FIG. 3, the control unit 130 is a controller, and is stored in a storage device inside the information processing device 100 by, for example, a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). This is realized by executing various programs (corresponding to an example of an information processing program) using the RAM as a work area. Further, the control unit 130 is a controller, and is realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 3, the control unit 130 includes a reception unit 131, an acquisition unit 132, a generation unit 133, and an output unit 134, and realizes or executes the information processing operation described below. The internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 3, and may be another configuration as long as it is a configuration for performing information processing described later.

(Reception Department 131)
The reception unit 131 receives a distribution request for the content C1 from the terminal device 10. The reception unit 131 receives a distribution request for the partial content C11 of the content C1 from the terminal device 10. Specifically, the reception unit 131 has an input field F11 capable of inputting the number of clusters, an input field F12 capable of inputting a plurality of queries, and a send button for transmitting the information input in the input fields to the information processing apparatus 100. Accepts a distribution request for partial content C11 including B11.

Subsequently, when the reception unit 131 receives the distribution request for the content C1, the reception unit 131 distributes the content C1 to the terminal device 10. When the reception unit 131 receives the distribution request for the partial content C11 of the content C1, the reception unit 131 distributes the partial content C11 of the content C1 to the terminal device 10. Specifically, the reception unit 131 has an input field F11 capable of inputting the number of clusters, an input field F12 capable of inputting a plurality of queries, and a send button for transmitting the information input in the input fields to the information processing apparatus 100. The partial content C11 including the B11 is delivered to the terminal device 10.

Further, the reception unit 131 receives a plurality of target information indicating the classification target and a designated number of clusters, which is the number of clusters when the plurality of target information is classified into clusters. Specifically, the reception unit 131 receives from the terminal device 10 a plurality of queries indicating the classification target and a designated number of clusters, which is the number of clusters when the plurality of queries are classified into clusters. Subsequently, when the reception unit 131 receives the plurality of queries and the specified number of clusters, the reception unit 131 stores the received plurality of queries and the designated number of clusters in the query information storage unit 121 in association with each other.

(Acquisition unit 132)
The acquisition unit 132 acquires various types of information. The acquisition unit 132 acquires various information from an external information processing device. The acquisition unit 132 acquires various information from other information processing devices such as the generation device 50.

Further, the acquisition unit 132 acquires various information from the storage unit 120. The acquisition unit 132 acquires various types of information from the query information storage unit 121, the vector information storage unit 122, the cluster information storage unit 123, and the model information storage unit 124.

Specifically, the acquisition unit 132 acquires the model. More specifically, the acquisition unit 132 receives the plurality of queries indicating the classification target by the reception unit 131 and the specified number of clusters, which is the number of clusters when the plurality of queries are classified into clusters, and the generation device 50. The first model M1 is acquired from. The acquisition unit 132 acquires the model data MDT1 of the first model M1 from the generation device 50. Subsequently, when the acquisition unit 132 acquires the first model M1 (model data MDT1), the acquisition unit 132 stores the acquired first model M1 (model data MDT1) in the model information storage unit 124.

(Generator 133)
The generation unit 133 generates various information. The generation unit 133 generates a distributed representation of the query. Specifically, the generation unit 133 generates a distributed representation of a plurality of queries received by the reception unit 131.

In addition, the generation unit 133 uses a learning model that learns the characteristics of the plurality of search queries, assuming that the plurality of search queries input by the same user within a predetermined time have similar characteristics. Generates a distributed representation corresponding to the character information that is each target information included in the target information. Specifically, the generation unit 133 uses the first model M1 that has learned the characteristics of the plurality of search queries, assuming that the plurality of search queries input by the same user within a predetermined time have similar characteristics. It is used to generate a distributed representation of a plurality of queries received by the reception unit 131.

Further, the generation unit 133 generates a distributed expression by using a learning model that outputs a distributed expression of the predetermined search query as output information when a predetermined search query is input as input information. Specifically, the generation unit 133 receives the input information by the reception unit 131 using the first model M1 that outputs the distributed representation of the predetermined search query as the output information when the predetermined search query is input. Generate a distributed representation of multiple queries.

In addition, the generation unit 133 uses a learning model that learns the characteristics of a plurality of search queries by learning so that the distributed expressions of a pair of search queries input consecutively within a predetermined time are similar. Generate a distributed representation. Specifically, the generation unit 133 learns the characteristics of a plurality of search queries by learning so that the distributed expressions of a pair of search queries input consecutively within a predetermined time are similar to each other. Using M1, a distributed representation of a plurality of queries received by the reception unit 131 is generated.

Further, the generation unit 133 learns that as a plurality of search queries input by the same user within a predetermined time, a plurality of search queries including a character string delimited by a predetermined delimiter have similar characteristics. By doing so, a distributed representation is generated using a learning model that has learned the characteristics of a plurality of search queries. Specifically, the generation unit 133 has a feature that a plurality of search queries including a character string separated by a predetermined delimiter are similar as a plurality of search queries input by the same user within a predetermined time. By learning as a thing, a distributed representation of a plurality of queries received by the reception unit 131 is generated by using the first model M1 that has learned the characteristics of the plurality of search queries.

In addition, the generation unit 133 generates a distributed expression by using the learning model in which the characteristics of the plurality of search queries are learned by learning that the plurality of randomly extracted search queries have different characteristics. .. Specifically, the generation unit 133 uses the first model M1 that has learned the characteristics of the plurality of search queries by learning that the plurality of randomly extracted search queries have different characteristics. Generates a distributed representation of a plurality of queries received by the reception unit 131.

Further, the generation unit 133 generates a distributed expression by using a learning model that has learned the characteristics of a plurality of search queries by learning so that the distributed expressions of a pair of randomly extracted search queries are different. .. Specifically, the generation unit 133 uses the first model M1 that has learned the characteristics of the plurality of search queries by learning so that the distributed expressions of the pair of randomly extracted search queries are different. Generates a distributed representation of a plurality of queries received by the reception unit 131.

More specifically, the generation unit 133 stores model information when a plurality of queries indicating classification targets and a specified number of clusters, which is the number of clusters when classifying the plurality of queries into clusters, are received by the reception unit 131. The first model M1 is acquired with reference to the part 124. Further, when the generation unit 133 receives a plurality of queries indicating classification targets by the reception unit 131 and a specified number of clusters which is the number of clusters when classifying the plurality of queries into clusters, the generation unit 133 refers to the query information storage unit 121. Then, a plurality of queries received by the reception unit 131 are acquired. In the example shown in FIG. 1, the generation unit 133 acquires the queries Q1-1 to Q1-12 received by the reception unit 131.

Subsequently, when the generation unit 133 acquires a plurality of queries, the generation unit 133 acquires one query from the plurality of queries. In the example shown in FIG. 1, when the generation unit 133 acquires the queries Q1-1 to Q1-12, the generation unit 133 acquires one query Q1-1 from the queries Q1-1 to Q1-12. Subsequently, the generation unit 133 inputs the acquired one query to the first model M1 as the input information of the first model M1. In the example shown in FIG. 1, the generation unit 133 inputs the acquired one query Q1-1 to the first model M1 as the input information of the first model M1. Further, the generation unit 133 outputs the distributed representation of the query input to the first model M1 as the output information of the first model M1 from the first model M1. In the example shown in FIG. 1, the generation unit 133 outputs the distributed representation QV1-1 of the query Q1-1 input to the first model M1 as the output information of the first model M1 from the first model M1. In this way, the generator 133 generates a distributed representation of the query. Similarly, the generation unit 133 generates a distributed representation of each query for all the acquired queries. In the example shown in FIG. 1, the generation unit 133 generates the distributed representations QV1-1 to QV1-12 of each query Q1-1 to Q1-12 for the acquired queries Q1-1 to Q1-12. Subsequently, when the generation unit 133 generates the distributed expression of each query, the generated unit 133 stores the distributed expression of each generated query in association with each query in the vector information storage unit 122.

In addition, the generation unit 133 generates a cluster. Specifically, the generation unit 133 generates a cluster generated by classifying a plurality of target information into clusters having a specified number of clusters. For example, the generation unit 133 generates a cluster generated by classifying a plurality of queries received by the reception unit 131 into clusters having a specified number of clusters.

In addition, the generation unit 133 includes a search intention when one target information included in a plurality of target information is input as a search query, and a search query when another target information included in the plurality of target information is input as a search query. Classify multiple target information into clusters with a specified number of clusters based on the similarity with the search intention of. Specifically, the generation unit 133 corresponds to the distributed expression corresponding to the character information which is one target information included in the plurality of target information and the character information which is other target information included in the plurality of target information. Classify multiple target information into clusters with a specified number of clusters based on the similarity with the distributed representation. More specifically, the generation unit 133 determines the degree of similarity between the distributed representation of one query generated using the first model M1 and the distributed representation of another query generated using the first model M1. Based on this, a plurality of queries received by the reception unit 131 are classified into clusters having a specified number of clusters.

In addition, the generation unit 133 generates cluster information regarding the cluster. Specifically, the generation unit 133 generates cluster information regarding the cluster in which each target information is classified by classifying a plurality of target information into clusters having a specified number of clusters. For example, the generation unit 133 generates designated cluster information regarding the cluster into which each query is classified by classifying the plurality of queries received by the reception unit 131 into clusters having a specified number of clusters.

In addition, the generation unit 133 includes a search intention when one target information included in the plurality of target information is input as a search query, and when another target information included in the plurality of target information is input as a search query. Generates designated cluster information based on the similarity to the search intent of. Specifically, the generation unit 133 corresponds to the distributed expression corresponding to the character information which is one target information included in the plurality of target information and the character information which is other target information included in the plurality of target information. Generates designated cluster information based on similarity to the distributed representation. More specifically, the generation unit 133 determines the degree of similarity between the distributed representation of one query generated using the first model M1 and the distributed representation of another query generated using the first model M1. Based on this, the designated cluster information regarding the cluster generated by classifying the plurality of queries received by the reception unit 131 into the clusters having the specified number of clusters is generated.

In the example shown in FIG. 1, when the reception unit 131 receives the number of clusters “3” and 12 queries Q1-1 to Q1-12, the generation unit 133 generates the query Q1 using the first model M1. Obtain the distributed representations QV1-1 to QV1-12 of -1 to Q1-12. Specifically, the generation unit 133 refers to the query information storage unit 121 and the vector information storage unit 122, and refers to the distributed representation QV1-1 of the queries Q1-1 to Q1-12 generated by using the first model M1. ~ QV1-12 is acquired.

Subsequently, when the generation unit 133 acquires the distributed expressions QV1-1 to QV1-12, the generated unit 133 classifies the acquired distributed expressions QV1-1 to QV1-12 into clusters having the number of clusters "3" using the k-means method. .. Note that the generation unit 133 can use any clustering method, not limited to the k-means method, as long as the acquired distributed representations QV1-1 to QV1-12 can be classified into clusters having the number of clusters "3". Good.

In addition, the generation unit 133 classifies the distributed expressions QV1-1 to QV1-12 corresponding to the queries Q1-1 to Q1-12 into clusters having the number of clusters "3", so that each query corresponding to each distributed expression is classified. Classify into clusters with the number of clusters "3". In this way, the generation unit 133 generates cluster information regarding the cluster to which each query is classified by classifying each query corresponding to each distributed representation into clusters having the number of clusters “3”.

For example, the generation unit 133 classifies the distributed representation QV1-1, the distributed representation QV1-2, the distributed representation QV1-3, the distributed representation QV1-7, and the distributed representation QV1-8 into one cluster (cluster CL1). Since the generation unit 133 has classified the distributed expression QV1-1 into the cluster CL1, the query Q1-1 corresponding to the distributed expression QV1-1 is classified into the cluster CL1. Further, since the generation unit 133 classifies the distributed expression QV1-2 into the cluster CL1, the query Q1-2 corresponding to the distributed expression QV1-2 is classified into the cluster CL1. Similarly, since the generation unit 133 classifies the distributed representation QV1-3, the distributed representation QV1-7, and the distributed representation QV1-8 into the cluster CL1, the query Q1-3 and the distributed representation QV1- corresponding to the distributed representation QV1-3. The query Q1-7 corresponding to 7 and the query Q1-8 corresponding to the distributed representation QV1-8 are classified into cluster CL1. In this way, the generation unit 133 generates cluster information regarding the cluster CL1 in which the query Q1-1, the query Q1-2, the query Q1-3, the query Q1-7, and the query Q1-8 are classified.

Further, the generation unit 133 puts the distributed representation QV1-4, the distributed representation QV1-5, the distributed representation QV1-6, the distributed representation QV1-9, and the distributed representation QV1-10 into one cluster (cluster CL2) different from the cluster CL1. Classify. Since the generation unit 133 has classified the distributed expression QV1-4 into the cluster CL2, the query Q1-4 corresponding to the distributed expression QV1-4 is classified into the cluster CL2. Further, since the generation unit 133 classifies the distributed expression QV1-5 into the cluster CL2, the query Q1-5 corresponding to the distributed expression QV1-5 is classified into the cluster CL2. Similarly, since the generation unit 133 classifies the distributed representation QV1-6, the distributed representation QV1-9, and the distributed representation QV1-10 into the cluster CL2, the query Q1-6 and the distributed representation QV1- corresponding to the distributed representation QV1-6. Query Q1-9 corresponding to 9 and query Q1-10 corresponding to the distributed representation QV1-10 are classified into cluster CL2. In this way, the generation unit 133 generates cluster information regarding the cluster CL2 in which the query Q1-4, the query Q1-5, the query Q1-6, the query Q1-9, and the query Q1-10 are classified.

Further, the generation unit 133 classifies the distributed representation QV1-11 and the distributed representation QV1-12 into one cluster (cluster CL3) different from the cluster CL1 and the cluster CL2. Since the generation unit 133 has classified the distributed representation QV1-11 into the cluster CL3, the query Q1-11 corresponding to the distributed representation QV1-11 is classified into the cluster CL3. Further, since the generation unit 133 classifies the distributed expression QV1-12 into the cluster CL3, the query Q1-12 corresponding to the distributed expression QV1-12 is classified into the cluster CL3. In this way, the generation unit 133 generates cluster information regarding the cluster CL3 in which the query Q1-11 and the query Q1-12 are classified.

In addition, the generation unit 133 generates information in which the target information classified into the clusters can be visually recognized as the designated cluster information for each cluster. For example, the generation unit 133 generates content in which the target information classified into the clusters can be visually recognized as the designated cluster information for each cluster.

In the example shown in FIG. 1, the generation unit 133 may generate the partial content C12 in which the name “cluster 1” of the cluster CL1 is displayed above the display area F21 in which the cluster information regarding the cluster CL1 is displayed. Further, the generation unit 133 may generate the partial content C12 in which the name “cluster 2” of the cluster CL2 is displayed above the display area F22 in which the cluster information regarding the cluster CL2 is displayed. Further, the generation unit 133 may generate the partial content C12 in which the name “cluster 3” of the cluster CL3 is displayed above the display area F23 in which the cluster information regarding the cluster CL3 is displayed.

(Output unit 134)
The output unit 134 outputs the designated cluster information generated by the generation unit 133. Specifically, the output unit 134 transmits the designated cluster information generated by the generation unit 133 to the terminal device 10.

[3. Terminal device configuration]
Next, the configuration of the terminal device 10 according to the embodiment will be described with reference to FIG. FIG. 8 is a diagram showing a configuration example of the terminal device 10 according to the embodiment. As shown in FIG. 8, the terminal device 10 includes a communication unit 11, an input unit 12, a display unit 13, a storage unit 14, and a control unit 15.

(Communication unit 11)
The communication unit 11 is realized by, for example, a NIC or the like. Then, the communication unit 11 is connected to the network N by wire or wirelessly, and transmits / receives information to / from the information processing device 100.

(Input unit 12, display unit 13)
The input unit 12 is an input device that receives various operations from the user. Various information is input to the input unit 12 via the display unit 13. For example, the input unit 12 is realized by a keyboard, a mouse, operation keys, or the like. The display unit 13 is a display device for displaying various information, that is, a screen. For example, the display unit 13 is realized by a liquid crystal display or the like. The display unit 13 displays the information stored in the storage unit 14. The display unit 13 displays the information received by the reception unit 151. The display unit 13 displays various information according to the control by the display control unit 152. When a touch panel is adopted for the terminal device 10, the input unit 12 and the display unit 13 are integrated. Further, in the following description, the display unit 13 may be described as a screen.

In the example of FIG. 1, the display unit 13 displays the content C1 received by the reception unit 151. The display unit 13 displays the partial content C11 of the content C1 received by the reception unit 151. Specifically, the display unit 13 inputs the input field F11 capable of inputting a plurality of first queries, the input field F12 capable of inputting a plurality of second queries, and the information processing device 100 for the queries input in the input fields. The partial content C11 including the transmission button B11 to be transmitted to is displayed.

Further, the display unit 13 displays the partial content C12 of the content C1 received by the reception unit 151. The display unit 13 displays information about the bar graphs G21 to G23 received by the reception unit 151. Specifically, the display unit 13 displays information about the bar graphs G21 to G23 in each of the display areas F21 to F23 included in the partial content C12 of the content C1 received by the reception unit 151.

(Memory unit 14)
The storage unit 14 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 14 stores various information used for displaying the information. The storage unit 14 stores the information received by the reception unit 151. In the example shown in FIG. 1, the storage unit 14 stores the content C1 received by the reception unit 151. Further, the storage unit 14 stores the partial content C11 of the content C1 received by the reception unit 151. Further, the storage unit 14 stores the partial content C12 of the content C1 received by the reception unit 151. Further, the storage unit 14 stores information regarding the bar graphs G21 to G23 received by the reception unit 151.

(Control unit 15)
In the control unit 15, various programs (for example, corresponding to an example of a display control program for displaying the content C1 or the like on the screen) stored in the storage device inside the terminal device 10 by the CPU, MPU, or the like use the RAM as a work area. It is realized by being executed. For example, these various programs correspond to application programs called web browsers. Further, the control unit 15 is realized by, for example, an integrated circuit such as an ASIC or FPGA.

As shown in FIG. 8, the control unit 15 includes a reception unit 151, a display control unit 152, a reception unit 153, and a transmission unit 154, and realizes or executes an information processing function or operation described below. To do. The internal configuration of the control unit 15 is not limited to the configuration shown in FIG. 8, and may be another configuration as long as it is a configuration for performing information processing described later.

(Receiver 151)
The receiving unit 151 receives various information. The receiving unit 151 receives various information from an external information processing device. The receiving unit 151 receives various information from other information processing devices such as the information processing device 100. In the example of FIG. 1, the receiving unit 151 receives the content C1 from the information processing device 100. Further, the receiving unit 151 receives the partial content C11 of the content C1 from the information processing device 100. Further, the receiving unit 151 receives the partial content C12 of the content C1 from the information processing device 100. Further, the receiving unit 151 receives the cluster information regarding the clusters CL1 to CL3 from the information processing device 100.

(Display control unit 152)
The display control unit 152 controls various displays. The display control unit 152 controls the display of the display unit 13. The display control unit 152 controls the display of the display unit 13 in response to the reception by the reception unit 151. The display control unit 152 controls the display of the display unit 13 based on the information received by the reception unit 151. The display control unit 152 controls the display of the display unit 13 based on the information received by the reception unit 153. The display control unit 152 controls the display of the display unit 13 in response to the reception by the reception unit 153. The display control unit 152 controls the display of the display unit 13 so that the content is displayed on the display unit 13.

In the example of FIG. 1, the display control unit 152 controls the display of the display unit 13 so that the content C1 is displayed. Further, the display control unit 152 controls the display of the display unit 13 so that the partial content C11 of the content C1 is displayed. Further, the display control unit 152 controls the display of the display unit 13 so that the partial content C12 of the content C1 is displayed. Further, the display control unit 152 controls the display of the display unit 13 so that the cluster information regarding the clusters CL1 to CL3 is displayed in each of the display areas F21 to F23 included in the partial content C12 of the content C1.

(Reception Department 153)
The reception unit 153 receives various information. For example, the reception unit 153 receives an input by the user via the input unit 12. The reception unit 153 accepts operations by the user. The reception unit 153 accepts the user's operation on the information displayed by the display unit 13. The reception unit 153 accepts character input by the user. The reception unit 153 accepts the input of the number of clusters by the user. The reception unit 153 accepts the input of the number of clusters by inputting characters into the input field F11 included in the partial content C11 of the content C1. The reception unit 153 accepts the number input by the user in the input field F11 as the number of clusters.

In addition, the reception unit 153 accepts input of a plurality of queries by the user. The reception unit 153 accepts the input of the query by inputting characters into the input field F12 included in the partial content C11 of the content C1. The reception unit 153 accepts the character string input by the user in the input field F12 as a query. Further, the reception unit 153 accepts each character string separated by a delimiter as each query.

In addition, the reception unit 153 accepts the user's selection operation of the transmission button B11. The reception unit 153 accepts the selection operation of the transmission button B11 received by the input unit 12.

In the example of FIG. 1, the reception unit 153 accepts the input of the number “3” which is the number of clusters. The reception unit 153 accepts the input of the number of clusters "3" by inputting characters into the input field F11 included in the partial content C11 of the content C1.

In addition, the reception unit 153 accepts inputs of 12 queries Q1-1 to Q1-12, which are 12 character strings separated by delimiters. The reception unit 153 accepts the input of 12 queries Q1-1 to Q1-12 by inputting characters into the input field F12 included in the partial content C11 of the content C1. The reception unit 153 accepts input of 12 character strings "vehicle type T11" to character strings "vehicle type T26", which are 12 character strings input by the user U11 in the input field F12.

In addition, the reception unit 153 accepts the selection operation of the transmission button B11 by the user U11. The reception unit 153 accepts the selection operation of the transmission button B11 received by the input unit 12. The reception unit 153 accepts the selection operation of the transmission button B11 received via the display unit 13.

(Transmission unit 154)
The transmission unit 154 transmits various information to an external information processing device. For example, the transmission unit 154 transmits various information to another information processing device such as the information processing device 100. The transmission unit 154 transmits the information stored in the storage unit 14. Further, the transmission unit 154 transmits various information based on information from another information processing device such as the information processing device 100. The transmission unit 154 transmits various types of information based on the information stored in the storage unit 14. The transmission unit 154 transmits the information received by the reception unit 153.

The transmission unit 154 transmits the number of clusters received by the reception unit 153 and a plurality of queries to the information processing device 100. Specifically, when the reception unit 153 accepts the selection operation of the transmission button B11, the transmission unit 154 transmits the number of clusters accepted by the reception unit 153 and a plurality of queries to the information processing device 100.

In the example of FIG. 1, when the reception unit 153 accepts the selection operation of the transmission button B11, the transmission unit 154 transmits the number of clusters "3" accepted by the reception unit 153 to the information processing device 100. The transmission unit 154 transmits the number of clusters “3”, which is a number received by the reception unit 153, to the information processing device 100.

Further, when the reception unit 153 accepts the selection operation of the transmission button B11, the transmission unit 154 transmits 12 queries Q1-1 to Q1-12 received by the reception unit 153 to the information processing device 100. The transmission unit 154 transmits 12 queries Q2-1 to Q2-3, which are 12 character strings received by the reception unit 153, to the information processing device 100.

[4. Information processing flow]
Next, the procedure of information processing according to the embodiment will be described with reference to FIG. FIG. 9 is a flowchart showing an information processing procedure according to the embodiment. In the example shown in FIG. 9, the information processing apparatus 100 determines whether or not a plurality of queries and a specified number of clusters have been received from the terminal apparatus 10 (step S101). When the information processing apparatus 100 does not accept a plurality of queries and a specified number of clusters (step S101; No), the information processing apparatus 100 waits until the plurality of queries and the specified number of clusters are accepted.

Subsequently, when the information processing apparatus 100 receives a plurality of queries and a specified number of clusters (step S101; Yes), the information processing apparatus 100 generates cluster information regarding the generated clusters by classifying the plurality of queries into the specified number of clusters (step S101; Yes). Step S102).

Subsequently, when the information processing apparatus 100 generates the cluster information, the information processing apparatus 100 outputs the generated cluster information to the terminal apparatus 10 (step S103).

[5. Modification example]
The information processing system 1 according to the above-described embodiment may be implemented in various different forms other than the above-described embodiment. Therefore, another embodiment of the information processing system 1 will be described below. The same parts as those in the embodiment are designated by the same reference numerals, and the description thereof will be omitted.

[5-1. Cluster information for clusters less than the specified number of clusters]
The information processing apparatus 100 generates cluster information about the clusters generated by classifying the target information of the number into clusters having a number smaller than the number of designated clusters. Further, the information processing apparatus 100 outputs the generated cluster information in a comparable manner with the designated cluster information.

Specifically, the generation unit 133 generates cluster information regarding the generated clusters by classifying the plurality of target information into clusters having a number smaller than the number of designated clusters. Subsequently, the output unit 134 outputs the cluster information generated by the generation unit 133 so as to be comparable to the designated cluster information. Specifically, the generation unit 133 generates the partial content C12 that displays the designated cluster information and the cluster information side by side so as to be comparable. For example, the generation unit 133 generates the designated cluster information regarding the three clusters CL1 to CL3 having the designated number of clusters “3” as shown in FIG. In addition, the generation unit 133 has two clusters CL1 (Q1-1, Q1-2, Q1-3, Q1-7, Q1-8, Q1-11) having a number of clusters "2" less than the designated number of clusters "3". And cluster CL2 (Q1-4, Q1-5, Q1-6, Q1-9, Q1-10, Q1-12) to generate cluster information. Subsequently, the generation unit 133 generates the partial content C12 that displays the designated cluster information and the cluster information side by side so as to be comparable. Subsequently, the output unit 134 outputs the partial content C12 generated by the generation unit.

Further, the generation unit 133 may generate information in which the character color of the query corresponding to the difference (difference) between the designated cluster information and the cluster information is changed. For example, the generation unit 133 colors the character color of the cluster CL3 (Q1-11, Q1-12) in the case of the designated number of clusters "3" to a color different from other queries (for example, red). Generate the information. Subsequently, the generation unit 133 generates the partial content C12 that displays the designated cluster information and the cluster information side by side so as to be comparable. Subsequently, the output unit 134 outputs the partial content C12 generated by the generation unit 133.

[6. First model generation process]
Next, the flow of the generation process of the first model will be described with reference to FIG. FIG. 10 is a diagram showing an example of the generation process of the first model according to the embodiment. In the example shown in FIG. 10, the generator 50 is a pair consisting of a search query Q11 "Roppongi pasta" and a search query Q12 "Roppongi Italian" continuously input by the same user U1 within a predetermined time. The search query of is extracted (step S11).

Subsequently, the generation device 50 inputs the extracted search query Q11 into the first model M1 and outputs a vector BQV11 which is a distributed representation of the search query Q11. Here, the vector BQV11 is a distributed representation of the search query Q11 just output from the output layer of the first model M1, and shows a distributed representation before giving feedback to the first model M1 (before learning). Further, the generation device 50 inputs the extracted search query Q12 into the first model M1 and outputs a vector BQV12 which is a distributed representation of the search query Q12. Here, the vector BQV12 is a distributed representation of the search query Q12 just output from the output layer of the first model M1, and shows a distributed representation before giving feedback to the first model M1 (before learning). In this way, the generation device 50 outputs the vector BQV11 which is the distributed representation of the search query Q11 and the vector BQV12 which is the distributed representation of the search query Q12 (step S12).

Subsequently, the generator 50 is a pair of searches including the search query Q11 (“Roppongi pasta”) and the search query Q12 (“Roppongi Italian”) that are continuously input by the same user U1 within a predetermined time. Since the query is presumed to be a search query entered with a predetermined search intention (for example, a search intention of "searching for a restaurant in a certain place"), the search query Q11 is assumed to have similar characteristics to each other. The first model M1 is trained so that the distributed representation (vector QV11) of the above and the distributed representation (vector QV12) of the search query Q12 paired with the search query Q11 are similar in the distributed representation space. For example, let Θ be the magnitude of the angle formed by the vector BQV11 which is the distributed representation of the search query Q11 before giving feedback to the first model M1 (before learning) and the vector BQV12 which is the distributed representation of the search query Q12. Further, let Φ be the size of the angle formed by the vector QV11 which is the distributed representation of the search query Q11 after giving feedback to the first model M1 (after learning) and the vector QV12 which is the distributed representation of the search query Q12. At this time, the generator 50 trains the first model M1 so that Φ is smaller than Θ. For example, the generator 50 calculates the value of the cosine similarity between the vector BQV11 and the vector BQV12. Further, the generation device 50 calculates the value of the cosine similarity between the vector QV11 and the vector QV12. Subsequently, the generator 50 uses the first model so that the cosine similarity values of the vector QV11 and the vector QV12 are larger than the cosine similarity values of the vector BQV11 and the vector BQV12 (so that the values approach 1). Learn M1. In this way, the generation device 50 trains the first model M1 so that the two vectors, which are a pair of distributed representations corresponding to the pair of search queries, are similar in the distributed representation space, so that the distributed representation is expressed from the search query. The first model M1 that outputs the above is generated (step S13). The generator 50 is not limited to the cosine similarity, and may calculate the similarity between the distributed representations based on any index as long as it is an index applicable as a distance scale between vectors. Further, the generation device 50 may train the first model M1 based on any index as long as it is an index applicable as a distance scale between vectors. For example, the generation device 50 calculates the value of a predetermined distance function such as the Euclidean distance between distributed expressions, the distance in a non-Euclidean space such as a hyperbolic space, the Manhattan distance, and the Mahalanobis distance. Subsequently, the generation device 50 may train the first model M1 so that the value of a predetermined distance function between the distributed representations (that is, the distance in the distributed representation space) becomes small.

Next, the flow of the generation process of the first model will be described in more detail with reference to FIG. In the description of FIG. 11, a part that overlaps with the description of FIG. 9 will be omitted as appropriate. FIG. 11 is a diagram showing a generation process of the first model according to the embodiment. In the example shown in FIG. 11, the distributed representation output by the first model M1 generated by the generation device 50 is mapped to the distributed representation space. The generation device 50 trains the first model M1 so that the distributed representation of a predetermined search query and the distributed representation of another search query paired with the predetermined search query are mapped close to each other on the distributed representation space. ..

In the example shown in the upper part of FIG. 11, the generation device 50 is a search query Q11 (“Roppongi pasta”), which is four search queries continuously input by the same user U1 within a predetermined time, and a search query. Extract Q12 (“Roppongi Italian”), search query Q13 (“Akasaka pasta”), and search query Q14 (“Azabu pasta”). The generation device 50 extracts four search queries in which the time interval in which each search query is input by the same user U1 is within a predetermined time. The generation device 50 extracts a plurality of search queries in which the time interval in which each search query pair described later is input by the same user U1 is within a predetermined time. When the search query 50 is arranged in the order in which the search queries are input, the generation device 50 extracts four search queries input in the order of the search query Q11, the search query Q12, the search query Q13, and the search query Q14. When the generation device 50 extracts four search queries, it is a pair of three search queries (search query Q11, search query Q12), with two search queries adjacent in chronological order as a pair of search queries. (Search query Q12, search query Q13) and (search query Q13, search query Q14) are extracted (step S21-1). The generation device 50 may extract a plurality of search queries in which all the search queries are input within a predetermined time by the same user U1. Then, the generation device 50 selects two search queries from the extracted plurality of search queries regardless of whether they are adjacent to each other in chronological order, and uses the two selected search queries as a pair of search queries. It may be extracted.

Subsequently, the generation device 50 inputs the extracted search query Q1k (k = 1, 2, 3, 4) into the first model M1, and distributes the search query Q1k (k = 1, 2, 3, 4). The representation vector BQV1k (k = 1, 2, 3, 4) is output. Here, the vector BQV1k (k = 1, 2, 3, 4) is a distributed representation of the search query Q1k (k = 1, 2, 3, 4) just output from the output layer of the first model M1. , The distributed representation before giving feedback to the first model M1 (before learning) is shown (step S22-1).

Subsequently, in the generator 50, a pair of search queries continuously input by the same user U1 within a predetermined time is subjected to a predetermined search intention (for example, "eating and drinking at a certain place (near Minato-ku, Tokyo)). Since it is presumed that the search query was entered with the search intent of "finding a store"), the distributed representation of the search query Q11 (vector QV11) and the pair with the search query Q11 are assumed to have similar characteristics to each other. The first model M1 is trained so that the distributed representation (vector QV12) of the search query Q12 is similar on the distributed representation space. Further, in the generation device 50, the first distributed representation of the search query Q12 (vector QV12) and the distributed representation of the search query Q13 paired with the search query Q12 (vector QV13) are similar in the distributed representation space. Train model M1. Further, in the generation device 50, the first distributed representation of the search query Q13 (vector QV13) and the distributed representation of the search query Q14 paired with the search query Q13 (vector QV14) are similar in the distributed representation space. Train model M1. In this way, the generation device 50 trains the first model M1 so that the two vectors, which are a pair of distributed representations corresponding to the pair of search queries, are similar in the distributed representation space, so that the distributed representation is expressed from the search query. The first model M1 that outputs the above is generated (step S23-1).

As a result of the information processing shown in the upper part of FIG. 11, the vector QV1k (k = 1, 2, 3, 4), which is a distributed representation of the search query Q1k (k = 1, 2, 3, 4), is close to the distributed representation space. It is shown that the location is mapped as cluster CL11. For example, the search query Q1k (k = 1, 2, 3, 4) is a search query searched by the user U1 with the search intention of "searching for a restaurant in a certain place (near Minato-ku, Tokyo)". It is presumed to be a set. That is, the search query Q1k (k = 1, 2, 3, 4) is a search query searched with the search intention of "searching for a restaurant in a certain place (near Minato-ku, Tokyo)". , It is presumed that the search query has similar characteristics to each other. Here, when a predetermined search query input with the search intention of "searching for a restaurant in a certain place (near Minato-ku, Tokyo)" is input to the first model, the generator 50 is placed at the position of the cluster CL11. It is possible to output a distributed representation that is mapped. As a result, for example, the generator 50 has a search intention of "searching for a restaurant in a certain place (near Minato-ku, Tokyo)" by extracting a search query corresponding to the distributed representation mapped to the position of the cluster CL11. Search queries can be extracted according to. Therefore, the generation device 50 can appropriately interpret the meaning of the search query.

In the example shown in the lower part of FIG. 11, the generator 50 is a search query Q21 (“refrigerator 400L”), which is three search queries continuously input by the same user U2 within a predetermined time, and a search query. Extract Q22 ("refrigerator medium size") and search query Q23 ("refrigerator medium size recommended"). When the generation device 50 arranges the search queries in the order in which they are input, the generation device 50 extracts three search queries input in the order of the search query Q21, the search query Q22, and the search query Q23. When the generator 50 extracts three search queries, it is a pair of two search queries (search query Q21, search query Q22), with two search queries adjacent in chronological order as a pair of search queries. (Search query Q22, search query Q23) is extracted (step S21-2).

Subsequently, the generation device 50 inputs the extracted search query Q2m (m = 1, 2, 3) into the first model M1, and a vector that is a distributed representation of the search query Q2m (m = 1, 2, 3). BQV2m (m = 1, 2, 3) is output. Here, the vector BQV2m (m = 1, 2, 3) is a distributed representation of the search query Q2m (m = 1, 2, 3) just output from the output layer of the first model M1, and is the first model. The distributed expression before giving feedback to M1 (before learning) is shown (step S22-2).

Subsequently, in the generation device 50, a pair of search queries continuously input by the same user U2 within a predetermined time has a predetermined search intention (for example, a search intention of "examining a medium-sized refrigerator"). Since it is presumed to be an input search query, the distributed representation of the search query Q21 (vector QV21) and the distributed representation of the search query Q22 paired with the search query Q21 (vector) are assumed to have similar characteristics to each other. QV22) and the first model M1 are trained so as to be similar in the distributed expression space. Further, in the generation device 50, the first distributed representation of the search query Q22 (vector QV22) and the distributed representation of the search query Q23 paired with the search query Q22 (vector QV23) are similar in the distributed representation space. Train model M1. In this way, the generation device 50 trains the first model M1 so that the two vectors, which are a pair of distributed representations corresponding to the pair of search queries, are similar in the distributed representation space, so that the distributed representation is expressed from the search query. The first model M1 that outputs the above is generated (step S23-2).

As a result of the information processing shown in the lower part of FIG. 11, the vector QV2m (m = 1, 2, 3), which is a distributed representation of the search query Q2m (m = 1, 2, 3), is located near the distributed representation space of the cluster CL21. It is shown that it is mapped as. For example, the search query Q2m (m = 1, 2, 3) is presumed to be a set of search queries searched by the user U2 with the search intention of "checking a medium-sized refrigerator". That is, Q2m (m = 1, 2, 3) is a search query having characteristics similar to each other in that it is a search query searched with the search intention of "checking a medium-sized refrigerator". Presumed. Here, the generator 50 outputs a distributed representation that is mapped to the position of the cluster CL21 when a predetermined search query input with the search intention of "checking a medium-sized refrigerator" is input to the first model. can do. As a result, for example, the generator 50 extracts the search query corresponding to the distributed representation mapped to the position of the cluster CL21, thereby extracting the search query according to the search intention of "checking the medium-sized refrigerator". Can be done. Therefore, the generation device 50 can appropriately interpret the meaning of the search query.

Further, the generation device 50 according to the present invention is a search query having different characteristics from each other in that a plurality of randomly extracted search queries are search queries searched under different search intentions. Assuming that, the first model M1 is trained. Specifically, the generation device 50 maps the distributed representation of a predetermined search query and the distributed representation of a search query randomly extracted independently of the predetermined search query far away in the distributed representation space. The first model M1 is trained. In the example shown in FIG. 11, it is assumed that the generation device 50 randomly extracts the search query regardless of the search query Q11, and the search query Q21 is extracted. In this case, in the generation device 50, the distributed representation of the search query Q11 (vector QV11) and the distributed representation of the search query Q21 randomly extracted regardless of the search query Q11 (vector QV21) are far apart in the distributed representation space. The first model M1 is trained so as to be mapped to. As a result, the vector QV1k, which is a distributed expression of the search query Q1k (k = 1, 2, 3, 4) searched with the search intention of "searching for a restaurant in a certain place (near Minato-ku, Tokyo)". It is a distributed representation of cluster CL11 including (k = 1, 2, 3, 4) and search query Q2m (m = 1, 2, 3) searched with the search intention of "checking a medium-sized refrigerator". The cluster CL21 containing the vector QV2m (m = 1, 2, 3) is mapped far away on the distributed representation space. That is, the generator 50 according to the present invention trains the first model M1 so that the distributed expressions of a plurality of randomly extracted search queries are different, so that the distributed expressions of the search queries having different search intentions are distributed. It is possible to output to a distant position in space.

As a result of mapping the distributed representation output by the first model M1 generated by the generation device 50 to the distributed representation space, in addition to the cluster CL11 and the cluster CL21 described above, the same user within a predetermined time Cluster CL12 and cluster CL22, which are a set of distributed expressions of a plurality of input search queries, are generated.

As described above, the generator 50 acquires the search query entered by the user. Further, the generation device 50 learns from the acquired search queries that a plurality of search queries input by the same user within a predetermined time have similar characteristics, so that the generated search queries can be used as predetermined search queries. Generate a first model that predicts the feature information of the search query. That is, the generation device 50 according to the present invention is similar to each other in that a plurality of search queries continuously input within a predetermined time are search queries searched under a predetermined search intention. The first model is trained by regarding it as a search query having characteristics. Specifically, the generation device 50 trains the first model so that the distributed representations of a plurality of search queries input by the same user within a predetermined time are similar to each other, so that the first model is trained from the predetermined search query. Generate the first model that outputs the distributed representation including the feature information of the search query. That is, the generation device 50 according to the present invention trains the first model M1 so that the distributed representations of a plurality of search queries continuously input within a predetermined time are similar, so that the first model M1 is trained under a predetermined search intention. The distributed representation of the search query searched in is made possible to be output to a close position on the distributed representation space. This enables the generation device 50 to output (interpret) the meaning (search intention) of the search query according to the context of the user who input the search query. Therefore, the generation device 50 can appropriately interpret the meaning of the search query.

Further, the generation device 50 extracts the search query corresponding to the distributed expression mapped in the vicinity of the distributed expression including the feature information of the predetermined search query, so that the predetermined search query can be searched according to the search intention. Search queries can be extracted. That is, the generation device 50 makes it possible to analyze the search trend of the user in consideration of the search intention and context of the user who input the search query. Therefore, the generation device 50 can improve the analysis accuracy of the user's search trend. Further, the first model M1 generated by the generation device 50 can be made to function as a part of the search system. Alternatively, the generation device 50 is a distributed representation of the search query output by the first model M1 as input information to another system (for example, a search engine) that uses the feature information of the search query predicted by the first model M1. Can also be provided. As a result, the search system can select the content output as the search result based on the feature information of the search query predicted by the first model M1. That is, the search system can select the content output as the search result in consideration of the search intention and context of the user who entered the search query. Further, the search system calculates the similarity between the distributed representation of the character information included in the content output as the search result and the distributed representation of the search query based on the characteristic information of the search query predicted by the first model M1. It will be possible. Then, the search system can determine the display order of the contents output as the search result based on the calculated similarity. That is, the search system can determine the display order of the contents output as the search result in consideration of the search intention and context of the user who input the search query. Therefore, the generation device 50 can improve the usability in the search service.

[7. Generation device configuration]
Next, the configuration of the generation device 50 according to the embodiment will be described with reference to FIG. FIG. 12 is a diagram showing a configuration example of the generator 50 according to the embodiment. As shown in FIG. 12, the generation device 50 includes a communication unit 51, a storage unit 53, and a control unit 52. The generation device 50 has an input unit (for example, a keyboard, a mouse, etc.) that receives various operations from the administrator of the generation device 50, and a display unit (for example, a liquid crystal display, etc.) for displaying various information. You may.

(Communication unit 51)
The communication unit 51 is realized by, for example, a NIC (Network Interface Card) or the like. Then, the communication unit 51 is connected to the network by wire or wirelessly, and transmits / receives information between the terminal device 10 and the search server 20, for example.

(Memory unit 53)
The storage unit 53 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk. As shown in FIG. 12, the storage unit 53 includes a query information storage unit 531, a vector information storage unit 532, and a model information storage unit 533.

(Query information storage unit 531)
The query information storage unit 531 stores various information related to the search query input by the user. FIG. 13 shows an example of the query information storage unit according to the embodiment. In the example shown in FIG. 13, the query information storage unit 531 has items such as "user ID", "date and time", "search query", and "search query ID".

The "user ID" indicates identification information for identifying the user who entered the search query. "Date and time" indicates the date and time when the search server received the search query from the user. The "search query" indicates a search query entered by the user. The "search query ID" indicates identification information for identifying the search query entered by the user.

In the example shown in the first record of FIG. 13, the search query (search query Q11) identified by the search query ID “Q11” corresponds to the search query Q11 shown in FIG. Further, the user ID "U1" indicates that the user who entered the search query Q11 is a user (user U1) identified by the user ID "U1". Further, the date and time "2018/9/1 PM 17:00" indicates that the date and time when the search server receives the search query Q11 from the user U1 is 17:00 pm on September 1, 2018. Further, the search query "Roppongi pasta" indicates the search query Q11 input by the user U1. Specifically, the search query "Roppongi pasta" indicates that the characters "Roppongi" indicating the place name and the characters "pasta" indicating the type of food are separated by a space which is a delimiter.

(Vector information storage unit 532)
The vector information storage unit 532 stores various information related to the vector, which is a distributed representation of the search query. FIG. 14 shows an example of the vector information storage unit according to the embodiment. In the example shown in FIG. 14, the vector information storage unit 532 has items such as "vector ID", "search query ID", and "vector information".

The "vector ID" indicates identification information for identifying a vector which is a distributed representation of a search query. The "search query ID" indicates identification information for identifying the search query corresponding to the vector. "Vector information" indicates an N-dimensional vector which is a distributed representation of a search query. The vector, which is a distributed representation of the search query, is, for example, a 128-dimensional vector.

In the example shown in the first record of FIG. 14, the vector (vector QV11) identified by the vector ID “QV11” corresponds to the vector QV11 which is a distributed representation of the search query Q11 shown in FIG. Further, the search query (search query Q11) identified by the search query ID "Q11" indicates that the search query corresponding to the vector QV11 is the search query Q11. Further, the vector information "QVDT11" indicates an N-dimensional vector which is a distributed representation of the search query Q11.

(Model information storage unit 533)
The model information storage unit 533 stores various information related to the learning model generated by the generation device 50. FIG. 15 shows an example of the model information storage unit according to the embodiment. In the example shown in FIG. 15, the model information storage unit 533 has items such as "model ID" and "model data".

The "model ID" indicates identification information for identifying the learning model generated by the generation device 50. The "model data" indicates the model data of the learning model generated by the generation device 50. For example, "model data" stores data for converting a search query into a distributed representation.

In the example shown in the first record of FIG. 15, the learning model identified by the model ID “M1” corresponds to the first model M1 shown in FIG. Further, the model data "MDT1" indicates model data (model data MDT1) of the first model M1 generated by the generation device 50.

The model data MDT1 includes an input layer into which a search query is input, an output layer, a first element which is any layer from the input layer to the output layer and belongs to a layer other than the output layer, and the first element and the first element. A second element whose value is calculated based on the weight of one element and a second element are included, and a distributed representation of the search query input to the input layer is output from the output layer according to the search query input to the input layer. As such, the generator 50 may function.

Here, it is assumed that the model data MDT1 is realized by the regression model represented by "y = a1 * x1 + a2 * x2 + ... + ai * xi". In this case, the first element included in the model data MDT1 corresponds to input data (xi) such as x1 and x2. Further, the weight of the first element corresponds to the coefficient ai corresponding to xi. Here, the regression model can be regarded as a simple perceptron having an input layer and an output layer. When each model is regarded as a simple perceptron, the first element can be regarded as any node of the input layer, and the second element can be regarded as the node of the output layer.

Further, it is assumed that the model data MDT1 is realized by a neural network having one or a plurality of intermediate layers such as DNN (Deep Neural Network). In this case, the first element included in the model data MDT1 corresponds to either the node of the input layer or the intermediate layer. Further, the second element corresponds to a node in the next stage, which is a node to which a value is transmitted from a node corresponding to the first element. Further, the weight of the first element corresponds to a connection coefficient which is a weight considered for the value transmitted from the node corresponding to the first element to the node corresponding to the second element.

The generation device 50 calculates the variance representation using a model having an arbitrary structure such as the regression model and the neural network described above. Specifically, the model data MDT1 is set with a coefficient so as to output a distributed representation when a search query is input. The generation device 50 calculates the distributed representation using such model data MDT1.

In the above example, the model data MDT1 is a model (hereinafter referred to as model X1) that outputs a distributed representation of the search query when the search query is input. However, the model data MDT1 according to the embodiment may be a model generated based on the result obtained by repeating the input / output of data to the model X1. For example, the model data MDT1 may be a model (hereinafter, referred to as model Y1) learned by inputting the distributed representation output by the model X1 when the search query is input. Alternatively, the model data MDT1 may be a model trained to input a search query and output the output value of the model Y1.

Further, when the generation device 50 performs the estimation process using GAN (Generative Adversarial Networks), the model data MDT1 may be a model forming a part of GAN.

(Control unit 52)
Returning to the description of FIG. 12, the control unit 52 is a controller, and is stored in a storage device inside the generation device 50 by, for example, a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). It is realized by executing various programs (corresponding to an example of a generation program) using the RAM as a work area. Further, the control unit 52 is a controller, and is realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

In addition, the control unit 52 sends the search query input to the input layer to each layer other than the output layer by information processing according to the first model M1 (model data MDT1) stored in the model information storage unit 533. The computer is made to function so as to output a distributed representation from the output layer by performing an operation based on the first element and the weight of the first element with each element to which it belongs as the first element.

Further, the control unit 52 sends the search query input to the input layer to each layer other than the output layer by information processing according to the second model M2 (model data MDT2) stored in the model information storage unit 533. By performing an operation based on the first element and the weight of the first element with each element to which it belongs as the first element, the computer is made to function so that the probability that the search query belongs to each category is output from the output layer.

As shown in FIG. 12, the control unit 52 includes an acquisition unit 521, an extraction unit 522, and a generation unit 523, and realizes or executes the information processing operation described below. The internal configuration of the control unit 52 is not limited to the configuration shown in FIG. 12, and may be any other configuration as long as it performs information processing described later.

(Acquisition unit 521)
The acquisition unit 521 acquires various information. Specifically, the acquisition unit 521 acquires the search query input by the user from the search server 20. When the acquisition unit 521 acquires the search query input by the user, the acquisition unit 521 stores the acquired search query in the query information storage unit 531.

In addition, the acquisition unit 521 acquires vector information regarding a vector which is a distributed representation of the search query. When the acquisition unit 521 acquires the vector information, the acquisition unit 521 stores the acquired vector information in the vector information storage unit 532.

In addition, the acquisition unit 521 acquires information about the first model. Specifically, the acquisition unit 521 acquires information about the first model generated by the generation unit 523. When the acquisition unit 521 acquires the information regarding the first model, the acquisition unit 521 stores the acquired information regarding the first model in the model information storage unit 533.

(Extractor 522)
The extraction unit 522 extracts various information. Specifically, the extraction unit 522 extracts a plurality of search queries input by the same user within a predetermined time from the search queries acquired by the acquisition unit 521. For example, the extraction unit 522 extracts a plurality of search queries in which the time interval in which each search query is input by the same user is within a predetermined time. Subsequently, the extraction unit 522 extracts a pair of search queries continuously input by the same user within a predetermined time from among a plurality of search queries input by the same user within a predetermined time. For example, the extraction unit 522 extracts a plurality of search queries in which the time interval in which each search query pair is input by the same user is within a predetermined time. For example, the extraction unit 522 is a search query Q11 (“Roppongi pasta”), which is four search queries continuously input by the same user U1 within a predetermined time among the search queries acquired by the acquisition unit 521. ”), Search query Q12 (“Roppongi Italian”), search query Q13 (“Akasaka pasta”), and search query Q14 (“Azabu pasta”) are extracted. When the search queries are arranged in the order in which they are input, the extraction unit 522 extracts four search queries input in the order of search query Q11, search query Q12, search query Q13, and search query Q14. Subsequently, when the extraction unit 522 extracts four search queries, it is a pair of three search queries (search query Q11, search query), with two search queries adjacent in chronological order as a pair of search queries. Q12), (search query Q12, search query Q13), (search query Q13, search query Q14) are extracted. The extraction unit 522 may extract a plurality of search queries in which all the search queries are input by the same user within a predetermined time. Then, the extraction unit 522 selects two search queries from the extracted plurality of search queries regardless of whether they are adjacent to each other in chronological order, and sets the two selected search queries as a pair of search queries. It may be extracted.

In addition, the extraction unit 522 extracts a predetermined search query and other search queries unrelated to the predetermined search query from the search queries acquired by the acquisition unit 521. For example, the extraction unit 522 extracts a predetermined search query from the search queries acquired by the acquisition unit 521. Subsequently, the extraction unit 522 randomly extracts another search query from the search queries acquired by the acquisition unit 521, regardless of the predetermined search query.

(Generation unit 523)
The generation unit 523 generates various information. Specifically, the generation unit 523 learns that among the search queries acquired by the acquisition unit 521, a plurality of search queries input by the same user within a predetermined time have similar characteristics. , Generate a learning model that predicts the feature information of a predetermined search query from a predetermined search query. Specifically, the generation unit 523 trains the learning model so that the distributed expressions of a plurality of search queries input by the same user within a predetermined time are similar, thereby performing a predetermined search from the predetermined search query. Generate a learning model that predicts the characteristic information of the query. For example, the generation unit 523 generates a learning model by learning so that the distributed representations of a pair of search queries input consecutively within a predetermined time are similar. For example, the generation unit 523 calculates the value of the similarity of the distributed representation before learning the pair of search queries. In addition, the generation unit 523 calculates the value of the similarity of the distributed representation after learning the pair of search queries. Subsequently, the generation unit 523 trains the learning model so that the value of the similarity of the distributed expression after learning is larger than the value of the similarity of the distributed expression before learning. In this way, the generation unit 523 outputs the distributed expression from the search query by training the learning model so that the two vectors, which are the pair of distributed expressions corresponding to the pair of search queries, are similar in the distributed expression space. Generate a learning model to do. More specifically, the generation unit 523 generates a learning model that outputs a distributed expression from a search query by using the DSSM technology that uses LSTM, which is a kind of RNN, for the distributed expression generation. For example, the generation unit 523 assumes that the pair of search queries input by the same user within a predetermined time have similar characteristics as the correct answer data of the learning model, and the distributed representation of the predetermined search query and the predetermined Learn so that the distributed representations of other search queries that are paired with the search query are close together in the distributed representation space. Further, when the first model is generated, the generation unit 523 stores the generated first model (model data MDT1) in the model information storage unit 533 in association with the identification information that identifies the first model.

[8. Example of the first model]
Next, an example of the first model generated by the generation device 50 will be described with reference to FIG. FIG. 16 is a diagram showing an example of the first model according to the embodiment. In the example shown in FIG. 16, the first model M1 generated by the generation device 50 is composed of three layers of LSTM RNNs. In the example shown in FIG. 16, the extraction unit 522 is a pair consisting of a search query Q11 "Roppongi pasta" and a search query Q12 "Roppongi Italian" continuously input by the same user U1 within a predetermined time. Extract search queries for. The generation unit 523 inputs the search query Q11 extracted by the extraction unit 522 to the input layer of the first model M1 (step S41).

Subsequently, the generation unit 523 outputs a 256-dimensional vector BQV11 which is a distributed representation of the search query Q11 from the output layer of the first model M1. Further, the generation unit 523 inputs the search query Q12 extracted by the extraction unit 522 to the input layer of the first model M1. Subsequently, the generation unit 523 outputs a 256-dimensional vector BQV12 which is a distributed representation of the search query Q12 from the output layer of the first model M1 (step S42).

Subsequently, the generation unit 523 generates the first model M1 that outputs the distributed expression from the search query by learning so that the distributed expressions of the two consecutively input search queries are similar (step S43). .. For example, let Θ be the magnitude of the angle formed by the vector BQV11 which is the distributed representation of the search query Q11 before giving feedback to the first model M1 (before learning) and the vector BQV12 which is the distributed representation of the search query Q12. Further, let Φ be the size of the angle formed by the vector QV11 which is the distributed representation of the search query Q11 after giving feedback to the first model M1 (after learning) and the vector QV12 which is the distributed representation of the search query Q12. At this time, the generation unit 523 trains the first model M1 so that Φ is smaller than Θ. For example, the generation unit 523 calculates the value of the cosine similarity between the vector BQV11 and the vector BQV12. Further, the generation unit 523 calculates the value of the cosine similarity between the vector QV11 and the vector QV12. Subsequently, the generation unit 523 prepares the learning model M1 so that the value of the cosine similarity between the vector QV11 and the vector QV12 is larger than the value of the cosine similarity between the vector BQV11 and the vector BQV12 (so that the value approaches 1). To learn. In this way, the generation unit 523 trains the first model M1 so that the two vectors, which are a pair of distributed representations corresponding to the pair of search queries, are similar in the distributed representation space, so that the distributed representation is expressed from the search query. Generates the first model M1 that outputs. The generation unit 523 may calculate the similarity between the variance representations based on any index as long as it is an index applicable as a distance scale between vectors, not limited to the cosine similarity. Further, the generation unit 523 may train the learning model M1 based on any index as long as it is an index applicable as a distance scale between vectors. For example, the generation unit 523 calculates the value of a predetermined distance function such as the Euclidean distance between distributed expressions, the distance in a non-Euclidean space such as hyperbolic space, the Manhattan distance, and the Mahalanobis distance. Subsequently, the generation unit 523 may train the learning model M1 so that the value of a predetermined distance function between the distributed expressions (that is, the distance in the distributed expression space) becomes small.

Further, the generation unit 523 learns that, as a plurality of search queries input by the same user within a predetermined time, a plurality of search queries including character information delimited by a predetermined delimiter have similar characteristics. By doing so, the first model is generated. For example, the generation unit 523 includes a search query "Roppongi pasta" in which the characters "Roppongi" indicating the place name and the characters "pasta" indicating the type of food are separated by a space, and "Roppongi" indicating the place name. The first model is generated by learning that the search query "Roppongi Italian", which is separated by a space delimiter from the character "Italian" indicating the type of food, has similar characteristics.

Further, the generation unit 523 generates the first model by learning that the plurality of randomly extracted search queries among the search queries acquired by the acquisition unit 521 have different characteristics. Specifically, the generation unit 523 generates the first model by learning so that the distributed representations of a pair of randomly extracted search queries among the search queries acquired by the acquisition unit 521 are different. .. For example, in the generation unit 523, the distributed representation of the predetermined search query extracted by the extraction unit 522 and the distributed representation of the search query randomly extracted regardless of the predetermined search query are far apart in the distributed representation space. The first model M1 is trained so as to be mapped.

[9. Flow of generation processing of the first model]
Next, the procedure of the generation process of the first model according to the embodiment will be described with reference to FIG. FIG. 17 is a flowchart showing a generation processing procedure of the first model according to the embodiment.

In the example shown in FIG. 17, the generation device 50 acquires the search query input by the user (step S1001).

Subsequently, the generation device 50 extracts a plurality of search queries input by the same user within a predetermined time (step S1002).

Subsequently, the generation device 50 generates a first model that predicts the characteristic information of a predetermined search query from the predetermined search query by learning that the plurality of extracted search queries have similar characteristics (step). S1003).

[10. effect〕
As described above, the information processing apparatus 100 according to the embodiment includes a reception unit 131, a generation unit 133, and an output unit 134. The reception unit 131 receives a plurality of target information indicating the classification target and a designated number of clusters, which is the number of clusters when the plurality of target information is classified into clusters. The generation unit 133 generates the designated cluster information regarding the generated clusters by classifying the plurality of target information into clusters having a designated number of clusters. The output unit 134 outputs the designated cluster information generated by the generation unit 133.

As a result, the information processing apparatus 100 can extract cluster information of a specified number of clusters, which can be said to be a summary of the features of a large number of target information, from a large number of target information indicating classification targets. Then, the information processing apparatus 100 can summarize the features of a large number of target information into cluster information of a specified number of clusters and provide the information. As described above, the information processing apparatus 100 can provide the features of a large number of target information indicating the classification target in a summary of a small amount of information. Therefore, the information processing device 100 can obtain useful information.

In addition, the generation unit 133 includes a search intention when one target information included in the plurality of target information is input as a search query, and when another target information included in the plurality of target information is input as a search query. Generates designated cluster information based on the similarity to the search intent of.

As a result, the information processing apparatus 100 can provide the designated cluster information regarding the clusters of the designated number of clusters classified based on the similarity of the search intention when the target information is input as the search query.

Further, the generation unit 133 includes a distributed expression corresponding to the character information which is one target information included in the plurality of target information and a distributed expression corresponding to the character information which is other target information included in the plurality of target information. Generates designated cluster information based on the similarity of.

As a result, the information processing apparatus 100 can provide the classification result of the cluster based on the similarity of the search intentions between the queries in a state where it can be objectively grasped numerically.

In addition, the generation unit 133 generates information in which the target information classified into the clusters can be visually recognized as the designated cluster information for each cluster.

As a result, the information processing apparatus 100 can provide an analysis result regarding the similarity between a large number of high-dimensional distributed expressions in a state in which it is easy to grasp at a glance.

In addition, the generation unit 133 generates cluster information regarding the generated clusters by classifying the plurality of target information into clusters having a number smaller than the number of designated clusters. The output unit 134 outputs the cluster information generated by the generation unit 133 so as to be comparable to the designated cluster information.

As a result, the information processing apparatus 100 can provide the cluster information about the cluster generated by classifying the plurality of target information into clusters smaller than the number of designated clusters in a state where the designated cluster information can be compared.

In addition, the generation unit 133 includes a search intention when one target information included in a plurality of target information is input as a search query, and a search query when another target information included in the plurality of target information is input as a search query. Classify multiple target information into clusters with a specified number of clusters based on the similarity with the search intention of.

As a result, the information processing apparatus 100 can generate a specified number of clusters based on the similarity of the search intention when the target information is input as the search query.

Further, the generation unit 133 includes a distributed expression corresponding to the character information which is one target information included in the plurality of target information and a distributed expression corresponding to the character information which is other target information included in the plurality of target information. Classify multiple target information into clusters with a specified number of clusters based on the similarity of.

As a result, the information processing apparatus 100 can provide the classification result of the cluster based on the similarity of the search intentions between the queries in a state where it can be objectively grasped numerically.

In addition, the generation unit 133 uses a learning model that learns the characteristics of the plurality of search queries, assuming that the plurality of search queries input by the same user within a predetermined time have similar characteristics. Generates a distributed representation corresponding to the character information that is each target information included in the target information.

As a result, the information processing apparatus 100 can generate a distributed representation in which the character information which is the target information reflects the search intention manually input as the search query.

Further, the generation unit 133 generates a distributed expression by using a learning model that outputs a distributed expression of the predetermined search query as output information when a predetermined search query is input as input information.

As a result, the information processing apparatus 100 can generate a distributed expression that reflects the search intention manually input by using the character information as the target information as a search query.

In addition, the generation unit 133 uses a learning model that learns the characteristics of a plurality of search queries by learning so that the distributed expressions of a pair of search queries input consecutively within a predetermined time are similar. Generate a distributed representation.

As a result, the information processing apparatus 100 can generate a distributed representation in which the character information which is the target information reflects the search intention manually input as the search query.

Further, the generation unit 133 learns that as a plurality of search queries input by the same user within a predetermined time, a plurality of search queries including a character string delimited by a predetermined delimiter have similar characteristics. By doing so, a distributed representation is generated using a learning model that has learned the characteristics of a plurality of search queries.

As a result, the information processing apparatus 100 can generate a distributed representation in which the character information which is the target information reflects the search intention manually input as the search query.

In addition, the generation unit 133 generates a distributed expression by using the learning model in which the characteristics of the plurality of search queries are learned by learning that the plurality of randomly extracted search queries have different characteristics. ..

As a result, the information processing apparatus 100 can generate a distributed representation in which the character information which is the target information reflects the search intention manually input as the search query.

Further, the generation unit 133 generates a distributed expression by using a learning model that has learned the characteristics of a plurality of search queries by learning so that the distributed expressions of a pair of randomly extracted search queries are different. ..

As a result, the information processing apparatus 100 can generate a distributed representation in which the character information which is the target information reflects the search intention manually input as the search query.

[11. Hardware configuration]
Further, the information processing device 100 or the generation device 50 or the terminal device 10 according to the above-described embodiment is realized by, for example, a computer 1000 having a configuration as shown in FIG. FIG. 18 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device 100, the generation device 50, or the terminal device 10. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400, and controls each part. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started, a program that depends on the hardware of the computer 1000, and the like.

The HDD 1400 stores a program executed by the CPU 1100, data used by such a program, and the like. The communication interface 1500 receives data from another device via a predetermined communication network and sends it to the CPU 1100, and transmits the data generated by the CPU 1100 to the other device via the predetermined communication network.

The CPU 1100 controls an output device such as a display or a printer, and an input device such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. Further, the CPU 1100 outputs the generated data to the output device via the input / output interface 1600.

The media interface 1700 reads a program or data stored in the recording medium 1800 and provides the program or data to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or 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. And so on.

For example, when the computer 1000 functions as the information processing device 100, the generation device 50, or the terminal device 10, the CPU 1100 of the computer 1000 controls the control unit 130 or the control unit 52 or the control unit 52 by executing the program loaded on the RAM 1200. The function of the part 15 is realized. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, these programs may be acquired from another device via a predetermined communication network.

Although some of the embodiments of the present application have been described in detail with reference to the drawings, these are examples, and various modifications are made based on the knowledge of those skilled in the art, including the embodiments described in the disclosure column of the invention. It is possible to practice the present invention in other improved forms.

[12. Others]
Further, among the processes described in the above-described embodiments and modifications, all or part of the processes described as being automatically performed can be manually performed, or are described as being manually performed. It is also possible to automatically perform all or part of the processed processing by a known method. In addition, the processing procedure, specific name, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of the device is functionally or physically distributed / physically in any unit according to various loads and usage conditions. It can be integrated and configured.

In addition, the above-described embodiments and modifications can be appropriately combined as long as the processing contents do not contradict each other.

In addition, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the generation unit can be read as a generation means or a generation circuit.

1 Information processing system 10 Terminal device 20 Search server 50 Generation device 100 Information processing device 110 Communication unit 120 Storage unit 121 Query information storage unit 122 Vector information storage unit 123 Cluster information storage unit 124 Model information storage unit 130 Control unit 131 Reception unit 132 Acquisition unit 133 Generation unit 134 Output unit

Claims (15)

A reception unit that accepts a plurality of target information indicating a classification target and a specified number of clusters, which is the number of clusters when the plurality of target information is classified into clusters.
A generator that generates designated cluster information related to a cluster generated by classifying the plurality of target information into clusters having the specified number of clusters, and a generator.
An output unit that outputs the specified cluster information generated by the generation unit, and
An information processing device characterized by being equipped with. The generator
The search intention when one target information included in the plurality of target information is input as a search query and the search intention when other target information included in the plurality of target information is input as a search query. The information processing apparatus according to claim 1, wherein the designated cluster information is generated based on the similarity. The generator
Based on the degree of similarity between the distributed expression corresponding to the character information which is one target information included in the plurality of target information and the distributed expression corresponding to the character information which is other target information included in the plurality of target information. The information processing apparatus according to claim 1 or 2, wherein the designated cluster information is generated. The generator
The information processing apparatus according to any one of claims 1 to 3, wherein, as the designated cluster information, information that can visually recognize the target information classified into the cluster is generated for each cluster. The generator
Cluster information related to the cluster generated by classifying the plurality of target information into a number of clusters smaller than the specified number of clusters is generated.
The output unit
The information processing apparatus according to any one of claims 1 to 4, wherein the cluster information generated by the generation unit is output so as to be comparable to the designated cluster information. The generator
The search intention when one target information included in the plurality of target information is input as a search query and the search intention when other target information included in the plurality of target information is input as a search query. The information processing apparatus according to any one of claims 1 to 5, wherein the plurality of target information is classified into clusters having a specified number of clusters based on the similarity. The generator
Based on the degree of similarity between the distributed expression corresponding to the character information which is one target information included in the plurality of target information and the distributed expression corresponding to the character information which is other target information included in the plurality of target information. The information processing apparatus according to any one of claims 1 to 6, wherein the plurality of target information is classified into clusters having the specified number of clusters. The generator
Assuming that a plurality of search queries input by the same user within a predetermined time have similar characteristics, the plurality of target information is included in the plurality of target information by using a learning model in which the characteristics of the plurality of search queries are learned. The information processing apparatus according to any one of claims 1 to 7, wherein a distributed expression corresponding to character information which is each target information is generated. The generator
The eighth aspect of claim 8 is characterized in that when a predetermined search query is input as input information, the distributed expression is generated by using a learning model that outputs a distributed expression of the predetermined search query as output information. Information processing equipment. The generator
By learning so that the distributed expressions of a pair of search queries input consecutively within the predetermined time are similar, the distributed expressions are generated using a learning model that has learned the characteristics of the plurality of search queries. The information processing apparatus according to claim 8 or 9. The generator
As a plurality of search queries input by the same user within a predetermined time, the plurality of search queries including a character string separated by a predetermined delimiter are learned as having similar characteristics. The information processing apparatus according to any one of claims 8 to 10, wherein the distributed representation is generated by using a learning model that has learned the characteristics of a search query. The generator
It is characterized in that the distributed representation is generated by using a learning model that has learned the characteristics of the plurality of search queries by learning that a plurality of randomly extracted search queries have different characteristics. The information processing apparatus according to any one of claims 8 to 11. The generator
By learning so that the distributed expressions of a pair of randomly extracted search queries are different, the distributed expressions are generated by using a learning model that has learned the characteristics of the plurality of search queries. The information processing apparatus according to any one of claims 8 to 12. Information processing method executed by a computer
A reception process that accepts a plurality of target information indicating a classification target and a designated number of clusters, which is the number of clusters when the plurality of target information is classified into clusters.
A generation process for generating designated cluster information regarding a cluster generated by classifying the plurality of target information into clusters having the specified number of clusters, and a generation step.
An output process that outputs the designated cluster information generated by the generation process, and
An information processing method characterized by including. A reception procedure for accepting a plurality of target information indicating a classification target and a specified number of clusters, which is the number of clusters when classifying the plurality of target information into clusters.
A generation procedure for generating designated cluster information regarding a cluster generated by classifying the plurality of target information into clusters having the specified number of clusters, and a generation procedure.
An output procedure that outputs the specified cluster information generated by the above generation procedure, and
An information processing program characterized by having a computer execute.

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