JP6584613B1 - Information processing apparatus, information processing method, and information processing program - Google Patents

Abstract

An object of the present invention is to appropriately interpret the meaning of a search query. An information processing apparatus according to the present application includes an acquisition unit and a generation unit. The acquisition unit acquires a search query input by the user. The generation unit learns that a plurality of search queries input by the same user within a predetermined period of time from among the search queries acquired by the acquisition unit have similar characteristics. A learning model that predicts feature information of the search query is generated. [Selection] Figure 1

Description

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

  Conventionally, a technique for interpreting the meaning of a search query is known. Specifically, information related to the search query used by the target user is classified into the first cluster based on the similarity between vectors corresponding to the information related to each search query used by the target user, and each search query of other users is classified. Information related to search queries used by other users is classified into the second cluster based on the similarity between vectors corresponding to the information related to. And the technique which extracts the feature cluster which is a cluster which shows the action characteristic to an object user from the 1st cluster based on the difference between the 1st cluster and the 2nd cluster is proposed.

JP 2018-60469 A

  However, in the above-described conventional technology, it is not always possible to appropriately interpret the meaning of the search query. Specifically, the meaning of a word may vary depending on the context in which the word is used. Therefore, even if the character string used in the context is extracted separately from the context, it is difficult to interpret the original meaning of the character string (that is, the meaning of the character string used in the context). There is. In view of the above, the above-described conventional technology only considers the similarity of the search query as a character string, and thus it is difficult to interpret the original meaning of the search query. For this reason, it is difficult to properly interpret the meaning of the search query in the above-described conventional technology in consideration of the context of the user who has input the search query. Therefore, in the above-described conventional technology, it is not always possible to appropriately interpret the meaning of the search query.

  In general, in a search service, it is important to understand the meaning (content) of a search query and control the behavior of the search system according to the meaning (content) of the search query. However, since the context in which the search query is used varies widely, the meaning of the search query varies greatly. For this reason, it is difficult to generate learning data by manually creating learning data and appropriately analyzing the meaning of a wide variety of search queries.

  The present application has been made in view of the above, and an object thereof is to provide an information processing apparatus, an information processing method, and an information processing program capable of appropriately interpreting the meaning of a search query.

  An information processing apparatus according to the present application includes: an acquisition unit that acquires a search query input by a user; and a plurality of search queries that are input within a predetermined time by the same user among the search queries acquired by the acquisition unit And a generation unit that generates a learning model that predicts characteristic information of the predetermined search query from a predetermined search query by learning as having similar characteristics.

  According to one aspect of the embodiment, there is an effect that the meaning of the search query can be appropriately interpreted.

FIG. 1 is a diagram illustrating an example of information processing according to the embodiment. FIG. 2 is a diagram illustrating an example of information processing according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the information processing system according to the embodiment. FIG. 4 is a diagram illustrating a configuration example of the information processing apparatus according to the embodiment. FIG. 5 is a diagram illustrating an example of a query information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of a vector information storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of a model information storage unit according to the embodiment. FIG. 8 is a diagram illustrating an example of a learning model according to the embodiment. FIG. 9 is a flowchart illustrating an information processing procedure according to the embodiment. FIG. 10 is a hardware configuration diagram illustrating an example of a computer that implements the functions of the information processing apparatus.

  Hereinafter, a mode for carrying out an information processing apparatus, an information processing method, and an information processing program according to the present application (hereinafter referred to as “embodiment”) will be described in detail with reference to the drawings. Note that the information processing apparatus, the information processing method, and the information processing program according to the present application are not limited by this embodiment. In the following embodiments, the same parts are denoted by the same reference numerals, and redundant description is omitted.

[1. Example of information processing)
First, an example of information processing according to the embodiment will be described with reference to FIGS. 1 and 2. 1 and 2 are diagrams illustrating an example of information processing according to the embodiment. The information processing illustrated in FIGS. 1 and 2 is performed by the user terminal 10, the search server 50, and the information processing apparatus 100.

[Configuration of information processing system]
Prior to the description of FIGS. 1 and 2, the configuration of the information processing system 1 will be described with reference to FIG. FIG. 3 is a diagram illustrating a configuration example of the information processing system according to the embodiment. As illustrated in FIG. 3, the information processing system 1 includes a user terminal 10, a search server 50, and an information processing apparatus 100. The user terminal 10, the search server 50, and the information processing apparatus 100 are connected via a predetermined network N so as to be communicable by wire or wireless. Note that the information processing system 1 illustrated in FIG. 3 may include an arbitrary number of user terminals 10, an arbitrary number of search servers 50, and an arbitrary number of information processing apparatuses 100.

  The user terminal 10 is an information processing apparatus used by a user who uses a search service. The user terminal 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. Hereinafter, the user terminal 10 may be identified with the user. That is, in the following, the user can be read as the user terminal 10.

  In the following, the user specified by the user ID “U1” may be referred to as “user U1”. Thus, in the following, when “user U * (* is an arbitrary numerical value)” is described, it indicates that the user is a user specified by the user ID “U *”. For example, when “user U2” is described, the user is a user specified by the user ID “U2”.

  Moreover, below, the user terminal 10 is demonstrated as user terminal 10-1, 10-2 according to the user who uses the user terminal 10. For example, the user terminal 10-1 is the user terminal 10 used by the user U1. For example, the user terminal 10-2 is the user terminal 10 used by the user U2. Hereinafter, the user terminals 10-1 and 10-2 are referred to as user terminals 10 when they are described without particular distinction.

  The user terminal 10 transmits a search query input by the user to the search server 50. Specifically, the user terminal 10 acquires a search page including a search box for inputting a search query from the search server 50 in accordance with an operation by the user. Subsequently, when an operation for transmitting a search query is performed following an operation in which a user inputs characters in the search box, the user terminal 10 uses the characters input in the search box via the search page as a search query. It transmits to the search server 50. For example, if the user terminal 10 performs an operation of pressing a search query transmission button or an enter key after an operation of inputting characters into a search box by the user, the user terminal 10 performs a search via a search page. The characters entered in the search box are transmitted to the search server 50 as a search query.

  The search server 50 is a server device that provides a search service. Specifically, when receiving a search query from the user terminal 10, the search server 50 selects content corresponding to the received search query and output as a search result. Subsequently, the search server 50 delivers a search result page including the selected content to the user terminal 10. Here, the content distributed by the search server 50 is not limited to the web page displayed by the web browser. For example, the content distributed by the search server 50 may be content displayed by a dedicated application installed in the user terminal 10. The content distributed by the search server 50 includes music content, images (including moving images as well as still images), and text content (including news articles and articles posted on SNS (Social Networking Service)). Any content such as content combining an image and text, game content, and the like may be used.

  Further, the search server 50 stores information related to the search query input by the user. Specifically, the search server 50 stores information related to the user search history. For example, when receiving a search query from the user terminal 10, the search server 50 registers the received search query, the user ID for identifying the user who is the transmission source of the search query, and the transmission date and time of the search query in association with each other. . The search server 50 transmits information related to the search query input by the user to the information processing apparatus 100 in response to a request from the information processing apparatus 100.

  The information processing apparatus 100 learns that a plurality of search queries input by the same user within a predetermined time among the search queries input by the user have similar characteristics, so that It is a server device that generates a learning model that predicts feature information of a predetermined search query. Specifically, the information processing apparatus 100 learns a learning model so that distributed expressions of a plurality of search queries input within a predetermined time by the same user are similar, so that a predetermined search query can generate a predetermined search query. A learning model for predicting feature information of a search query is generated. Here, the information processing apparatus 100 learns the entire character string input in the search box for each search performed by the user as a search query input by the user. For example, when a search query including a plurality of character strings such as “Roppongi Pasta” is input to the search box in one search by the user U1, the information processing apparatus 100 searches for “Roppongi Pasta” as a whole. Learn as a query. In addition, the information processing apparatus 100 allows a plurality of search queries whose intervals are within a predetermined time (for example, within 2 minutes) to be input by the same user for a predetermined time. It learns as a plurality of search queries entered in. That is, the information processing apparatus 100 learns that a plurality of search queries input in a short time by the same user have similar characteristics. Note that the information processing apparatus 100 determines that when a predetermined search query is input and another search query is input after a predetermined time has elapsed (for example, after two minutes or more), It is learned as having different characteristics from other search queries.

  Generally, when a searcher performs a search, the searcher intends as a result of performing a search multiple times using different search queries, rather than reaching the searcher's intent information in a single search. There are more cases where information is reached. That is, since it is considered that the user performs a search a plurality of times with a certain intention, it is assumed that a search query continuously input within a predetermined time has a close search intention. Therefore, the information processing apparatus 100 according to the present invention causes the learning model to be learned on the assumption that a plurality of search queries input continuously by the same user within a predetermined time have similar characteristics. Specifically, the information processing apparatus 100 acquires information about the search query input by the user from the search server 50. Subsequently, the information processing apparatus 100 extracts a plurality of search queries input by the same user within a predetermined time from the search queries acquired from the search server 50. Subsequently, the information processing apparatus 100 generates a learning model that predicts feature information of a predetermined search query from the predetermined search query by learning that the extracted search queries have similar characteristics. For example, the information processing apparatus 100 learns a learning model so that distributed expressions of a plurality of extracted search queries are similar, thereby obtaining a distributed expression (vector) including feature information of a predetermined search query from a predetermined search query. Generate an output learning model.

  More specifically, the information processing apparatus 100 uses a technology of DSSM (Deep Structured Sematic Model) that uses LSTM (Long Short-Term Memory), which is a kind of RNN (Recurrent Neural Network), for distributed expression generation, A learning model that outputs a distributed expression (vector) from the search query is generated. For example, the information processing apparatus 100 assumes that a pair of search queries input by the same user within a predetermined time as the correct answer data of the learning model has similar characteristics, and a distributed expression (vector) of the predetermined search query. And a distributed expression (vector) of another search query that is paired with a predetermined search query is learned so as to exist in the vicinity in the distributed expression space. Note that learning so that two vectors are close to each other in the distributed representation space can be rephrased as learning so that the two vectors are similar in the distributed representation space. In this way, the information processing apparatus 100 generates a learning model using the DSSM technology that uses LSTM for distributed expression generation. Therefore, the learning model generated by the information processing apparatus 100 is distributed from any character string. (Vector) can be generated. That is, the information processing apparatus 100 is not limited to a distributed representation (vector) of a known character string (for example, a search query learned at the time of learning), but also an unknown character string (for example, a search query that has never been learned at the time of learning). Can also generate a distributed representation (vector).

  From here, the flow of information processing will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of information processing according to the embodiment. In the example illustrated in FIG. 1, the information processing apparatus 100 includes a pair of search queries Q11 “Roppongi pasta” and search queries Q12 “Roppongi Italian” that are continuously input by the same user U1 within a predetermined time. The search query is extracted (step S11).

  Subsequently, the information processing apparatus 100 inputs the extracted search query Q11 to the learning model M1, and outputs a vector BQV11 that is a distributed representation of the search query Q11. Here, the vector BQV11 is a distributed expression of the search query Q11 just output from the output layer of the learning model M1, and indicates a distributed expression before applying feedback to the learning model M1 (before learning). Further, the information processing apparatus 100 inputs the extracted search query Q12 into the learning model M1, and outputs a vector BQV12 that is a distributed representation of the search query Q12. Here, the vector BQV12 is a distributed expression of the search query Q12 just output from the output layer of the learning model M1, and indicates a distributed expression before applying feedback to the learning model M1 (before learning). In this way, the information processing apparatus 100 outputs the vector BQV11 that is a distributed representation of the search query Q11 and the vector BQV12 that is a distributed representation of the search query Q12 (step S12).

  Subsequently, the information processing apparatus 100 performs a pair of searches including a search query Q11 (“Roppongi pasta”) and a 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 input with a predetermined search intention (for example, a search intention “search for a restaurant in a certain place”), it is assumed that the query has characteristics similar to each other. The learning model M1 is trained so that the distributed representation (vector QV11) of the search query Q11 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, the angle between the vector BQV11, which is a distributed representation of the search query Q11 before applying feedback to the learning model M1 (before learning), and the vector BQV12, which is a distributed representation of the search query Q12, is Θ. Also, let Φ be the angle between the vector QV11, which is a distributed representation of the search query Q11 after feedback is applied to the learning model M1 (after learning), and the vector QV12, which is a distributed representation of the search query Q12. At this time, the information processing apparatus 100 learns the learning model M1 so that Φ is smaller than Θ. For example, the information processing apparatus 100 calculates the value of the cosine similarity between the vector BQV11 and the vector BQV12. Further, the information processing apparatus 100 calculates the value of the cosine similarity between the vector QV11 and the vector QV12. Subsequently, the information processing apparatus 100 determines the learning model so that the value of the cosine similarity between the vectors QV11 and QV12 is larger than the value of the cosine similarity between the vectors BQV11 and BQV12 (so that the value approaches 1). Let M1 learn. Thus, the information processing apparatus 100 learns the learning model M1 so that two vectors, which are a pair of distributed expressions corresponding to a pair of search queries, are similar on the distributed expression space, so that the distributed expressions are obtained from the search query. A learning model M1 that outputs (vector) is generated (step S13). Note that the information processing apparatus 100 calculates the similarity between the distributed representations (vectors) based on any index as long as the index is applicable not only as the cosine similarity but also as a distance measure between vectors. Also good. Further, the information processing apparatus 100 may learn the learning model M1 based on any index as long as it is an index applicable as a distance measure between vectors. For example, the information processing apparatus 100 calculates a value of a predetermined distance function such as a Euclidean distance between distributed expressions (vectors), a distance in a non-Euclidean space such as a hyperbolic space, a Manhattan distance, a Mahalanobis distance, or the like. Subsequently, the information processing apparatus 100 may learn the learning model M1 so that the value of a predetermined distance function between the distributed representations (vectors) (that is, the distance in the distributed representation space) becomes small.

  Next, the flow of information processing will be described in more detail with reference to FIG. In the description of FIG. 2, portions overlapping with those of FIG. 1 are omitted as appropriate. FIG. 2 is a diagram illustrating an example of information processing according to the embodiment. In the example illustrated in FIG. 2, the distributed representation (vector) output by the learning model M1 generated by the information processing apparatus 100 is mapped to the distributed representation space. The information processing apparatus 100 performs training of the learning model M1 so that the distributed expression of the predetermined search query and the distributed expression of the other search query that is paired with the predetermined search query are mapped in the vicinity in the distributed expression space. .

  In the example illustrated in the upper part of FIG. 2, the information processing apparatus 100 includes a search query Q11 (“Roppongi Pasta”), which is four search queries that are continuously input by the same user U1 within a predetermined time, and a search query. Q12 (“Roppongi Italian”), search query Q13 (“Akasaka pasta”), and search query Q14 (“Azabu pasta”) are extracted. The information processing apparatus 100 extracts four search queries in which the time interval when each search query is input by the same user U1 is within a predetermined time. The information processing apparatus 100 extracts a plurality of search queries in which a time interval within which a pair of search queries described later is input by the same user U1 is within a predetermined time. When arranged in the order in which the search queries are input, the information processing apparatus 100 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 information processing apparatus 100 extracts four search queries, two search queries adjacent in time series are used as a pair of search queries, and a pair of search queries (search query Q11, search query Q12). , (Search query Q12, search query Q13), (search query Q13, search query Q14) are extracted (step S21-1). The information processing apparatus 100 may extract a plurality of search queries in which all search queries are input within a predetermined time by the same user U1. Then, the information processing apparatus 100 selects two search queries from the plurality of extracted search queries regardless of whether or not they are adjacent in time series, and selects the two search queries as a pair of search queries. May be extracted as

  Subsequently, the information processing apparatus 100 inputs the extracted search query Q1k (k = 1, 2, 3, 4) to the learning model M1, and distributes the search query Q1k (k = 1, 2, 3, 4). A vector BQV1k (k = 1, 2, 3, 4) as an expression is output. Here, the vector BQV1k (k = 1, 2, 3, 4) is a distributed expression of the search query Q1k (k = 1, 2, 3, 4) just output from the output layer of the learning model M1, A distributed representation before applying feedback to the learning model M1 (before learning) is shown (step S22-1).

  Subsequently, the information processing apparatus 100 determines that a pair of search queries continuously input within a predetermined time by the same user U1 is a predetermined search intention (for example, “Food and drink at a certain place (near Minato-ku, Tokyo)”. Since the search query is presumed to be input in the search intention “search for store”, the distributed expression (vector QV11) of the search query Q11, the search query Q11 and the pair are assumed to have similar characteristics. The learning model M1 is learned so that the distributed expression (vector QV12) of the search query Q12 is similar in the distributed expression space. Further, the information processing apparatus 100 learns that the distributed expression (vector QV12) of the search query Q12 and the distributed expression (vector QV13) of the search query Q13 that is paired with the search query Q12 are similar in the distributed expression space. The model M1 is learned. Further, the information processing apparatus 100 learns that the distributed representation (vector QV13) of the search query Q13 and the distributed representation (vector QV14) of the search query Q14 paired with the search query Q13 are similar in the distributed representation space. The model M1 is learned. Thus, the information processing apparatus 100 learns the learning model M1 so that two vectors, which are a pair of distributed expressions corresponding to a pair of search queries, are similar on the distributed expression space, so that the distributed expressions are obtained from the search query. A learning model M1 that outputs (vector) is generated (step S23-1).

  As a result of the information processing shown in the upper part of FIG. 2, the vector QV1k (k = 1, 2, 3, 4), which is a distributed expression of the search query Q1k (k = 1, 2, 3, 4), is close to the distributed expression space. A state of mapping as a cluster CL11 at the position is shown. For example, the search query Q1k (k = 1, 2, 3, 4) is a set of search queries searched by the user U1 with a search intention of “searching for a restaurant in a certain place (near Minato-ku, Tokyo)”. It is estimated that. That is, the search query Q1k (k = 1, 2, 3, 4) is a search query searched under a search intention of “finding a restaurant in a certain place (near Tokyo Minato-ku)”. , It is presumed that the search queries have characteristics similar to each other. Here, when a predetermined search query input with a search intention of “searching for a restaurant in a certain place (near Minato-ku, Tokyo)” is input to the learning model, the information processing apparatus 100 moves to the position of the cluster CL11. A distributed representation that can be mapped can be output. Thereby, for example, the information processing apparatus 100 extracts a search query corresponding to the distributed expression mapped to the position of the cluster CL11, thereby searching for “search for a restaurant in a certain place (near Minato-ku, Tokyo)”. A search query according to the intention can be extracted. Therefore, the information processing apparatus 100 can appropriately interpret the meaning of the search query.

  In the example illustrated in the lower part of FIG. 2, the information processing apparatus 100 includes a search query Q21 (“refrigerator 400L”), which is three search queries that are continuously input by the same user U2 within a predetermined time, and a search query. Q22 ("refrigerator medium size") and search query Q23 ("refrigerator medium size recommendation") are extracted. When arranged in the order in which the search queries are input, the information processing apparatus 100 extracts three search queries that are input in the order of the search query Q21, the search query Q22, and the search query Q23. When the information processing apparatus 100 extracts three search queries, two search queries that are adjacent in time series are used as a pair of search queries (search query Q21, search query Q22). , (Search query Q22, search query Q23) are extracted (step S21-2).

  Subsequently, the information processing apparatus 100 inputs the extracted search query Q2m (m = 1, 2, 3) to the learning model M1, and a vector that is a distributed expression 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 expression of the search query Q2m (m = 1, 2, 3) just output from the output layer of the learning model M1, and is stored in the learning model M1. A distributed expression before applying feedback (before learning) is shown (step S22-2).

  Subsequently, in the information processing apparatus 100, a pair of search queries continuously input by the same user U2 within a predetermined time is a predetermined search intention (for example, a search intention of “examine medium size refrigerator”). Since it is presumed that the search query is input, it is assumed that the search query Q21 has similar characteristics to each other, and a distributed representation (vector QV21) of the search query Q21 and a distributed representation (vector) of the search query Q22 paired with the search query Q21. QV22) is trained so that the learning model M1 is similar in the distributed expression space. Further, the information processing apparatus 100 learns so that the distributed representation (vector QV22) of the search query Q22 and the distributed representation (vector QV23) of the search query Q23 paired with the search query Q22 are similar in the distributed representation space. The model M1 is learned. Thus, the information processing apparatus 100 learns the learning model M1 so that two vectors, which are a pair of distributed expressions corresponding to a pair of search queries, are similar on the distributed expression space, so that the distributed expressions are obtained from the search query. A learning model M1 that outputs (vector) is generated (step S23-2).

  As a result of the information processing shown in the lower part of FIG. 2, the vector QV2m (m = 1, 2, 3), which is a distributed expression of the search query Q2m (m = 1, 2, 3), is located at a position close to the distributed expression space. As shown in FIG. 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 “examine medium size 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 under the search intention of “examine medium size refrigerator”. Presumed. Here, the information processing apparatus 100 outputs a distributed expression that is mapped to the position of the cluster CL21 when a predetermined search query input with a search intention of “examine medium size refrigerator” is input to the learning model. can do. Thereby, for example, the information processing apparatus 100 extracts a search query corresponding to a search intention of “examine medium size refrigerator” by extracting a search query corresponding to the distributed expression mapped to the position of the cluster CL21. be able to. Therefore, the information processing apparatus 100 can appropriately interpret the meaning of the search query.

  In addition, the information processing apparatus 100 according to the present invention is a search query having characteristics that are different from each other in that the plurality of randomly extracted search queries are search queries that are searched under different search intentions. The learning model M1 is learned by assuming that there is. Specifically, the information processing apparatus 100 maps a distributed expression of a predetermined search query and a distributed expression of a search query that is randomly extracted regardless of the predetermined search query in the distributed expression space. Thus, the learning model M1 is trained. In the example illustrated in FIG. 2, it is assumed that the information processing apparatus 100 randomly extracts a search query regardless of the search query Q11, and then extracts the search query Q21. In this case, the information processing apparatus 100 determines that the distributed expression (vector QV11) of the search query Q11 and the distributed expression (vector QV21) of the search query Q21 randomly extracted regardless of the search query Q11 are in the distributed expression space. Training of the learning model M1 is performed so that it is mapped far away. As a result, a vector QV1k which is a distributed representation of the search query Q1k (k = 1, 2, 3, 4) searched under the search intention of “searching for a restaurant in a certain place (near Minato-ku, Tokyo)” This is a distributed representation of a cluster CL11 including (k = 1, 2, 3, 4) and a search query Q2m (m = 1, 2, 3) searched with the search intention of “examine medium size refrigerator”. The cluster CL21 including the vector QV2m (m = 1, 2, 3) is mapped far away in the distributed representation space. That is, the information processing apparatus 100 according to the present invention causes the learning model M1 to learn so that the distributed expressions of a plurality of randomly extracted search queries are different, thereby distributing the distributed expressions of search queries having different search intentions. It is possible to output to a far position in space.

  Note that, as a result of the distributed representation (vector) output by the learning model M1 generated by the information processing apparatus 100 being mapped to the distributed representation space, in addition to the above-described cluster CL11 and cluster CL21, the same user can perform a predetermined expression. A cluster CL12 and a cluster CL22, which are sets of distributed expressions (vectors) of a plurality of search queries input in time, are generated.

  As described above, the information processing apparatus 100 acquires a search query input by a user. In addition, the information processing apparatus 100 learns that a plurality of search queries input by the same user within a predetermined time from the acquired search queries have similar characteristics, so that a predetermined search query can be used. A learning model for predicting feature information of the search query is generated. That is, the information processing apparatus 100 according to the present invention is similar to each other in that a plurality of search queries input continuously within a predetermined time are search queries searched under a predetermined search intention. The learning model is learned by assuming that the search query has the characteristic of Specifically, the information processing apparatus 100 learns a learning model so that distributed expressions of a plurality of search queries input within a predetermined time by the same user are similar, so that a predetermined search query can generate a predetermined search query. A learning model that outputs a distributed expression including feature information of a search query is generated. That is, the information processing apparatus 100 according to the present invention learns the learning model M1 so that the distributed expressions of a plurality of search queries that are continuously input within a predetermined time are similar, thereby achieving a predetermined search intention. The distributed expression of the search query retrieved in (1) can be output at a position close to the distributed expression space. As a result, the information processing apparatus 100 can output (interpret) the meaning (search intention) of the search query according to the context of the user who has input the search query. Therefore, the information processing apparatus 100 can appropriately interpret the meaning of the search query. Further, the information processing apparatus 100 extracts a search query corresponding to the distributed expression mapped in the vicinity of the distributed expression including the feature information of the predetermined search query, thereby responding to the search intention for searching the predetermined search query. Search query can be extracted. That is, the information processing apparatus 100 can analyze a user's search trend in consideration of the search intention and context of the user who has input the search query. Therefore, the information processing apparatus 100 can improve the analysis accuracy of the user's search trend. The learning model M1 generated by the information processing apparatus 100 can also function as a part of the search system. Alternatively, the information processing apparatus 100 uses a distributed expression of the search query output by the learning 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 learning model M1. It can also be provided. As a result, the search system can select content output as a search result based on the feature information of the search query predicted by the learning 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 has input the search query. Further, the search system can calculate the similarity between the distributed representation of the character string included in the content output as the search result and the distributed representation of the search query, based on the feature information of the search query predicted by the learning model M1. become. The search system can determine the display order of the content output as the search result based on the calculated similarity. That is, the search system can determine the display order of contents output as search results in consideration of the search intention and context of the user who has input the search query. Therefore, the information processing apparatus 100 can improve usability in the search service.

[2. Configuration of information processing apparatus]
Next, the configuration of the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 4 is a diagram illustrating a configuration example of the information processing apparatus 100 according to the embodiment. As illustrated in FIG. 4, the information processing apparatus 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The information processing apparatus 100 includes an input unit (for example, a keyboard and a mouse) that receives various operations from an administrator of the information processing apparatus 100 and a display unit (for example, a liquid crystal display) for displaying various types of information. You may have.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to the network by wire or wirelessly, and transmits and receives information between the user terminal 10 and the search server 50, for example.

(Storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As illustrated in FIG. 4, the storage unit 120 includes a query information storage unit 121, a vector information storage unit 122, and a model information storage unit 123.

(Query information storage unit 121)
The query information storage unit 121 stores various types of information related to the search query input by the user. FIG. 5 shows an example of the query information storage unit according to the embodiment. In the example illustrated in FIG. 5, the query information storage unit 121 includes items such as “user ID”, “date and time”, “search query”, and “search query ID”.

  “User ID” indicates identification information for identifying a user who has input a search query. “Date and time” indicates the date and time when the search server received a search query from the user. “Search query” indicates a search query input by the user. “Search query ID” indicates identification information for identifying a search query input by a user.

  In the example shown in the first record in FIG. 5, the search query (search query Q11) identified by the search query ID “Q11” corresponds to the search query Q11 shown in FIG. The user ID “U1” indicates that the user who has input the search query Q11 is a user (user U1) identified by the user ID “U1”. Also, the date and time “2018/9/1 PM 17:00” indicates that the date and time when the search server received the search query Q11 from the user U1 was 17:00 on September 1, 2018. The search query “Roppongi pasta” indicates the search query Q11 input by the user U1. Specifically, the search query “Roppongi Pasta” indicates that the character string is a character string in which “Roppongi” indicating a place name and “Pasta” indicating a food type are separated by a space as a delimiter.

(Vector information storage unit 122)
The vector information storage unit 122 stores various types of information regarding vectors that are distributed representations of search queries. FIG. 6 shows an example of a vector information storage unit according to the embodiment. In the example illustrated in FIG. 6, the vector information storage unit 122 includes items such as “vector ID”, “search query ID”, and “vector information”.

  The “vector ID” indicates identification information for identifying a vector that is a distributed expression of the search query. “Search query ID” indicates identification information for identifying a search query corresponding to a vector. “Vector information” indicates an N-dimensional vector which is a distributed expression of the search query. The vector that is a distributed expression of the search query is, for example, a 128-dimensional vector.

  In the example shown in the first record of FIG. 6, the vector (vector QV11) identified by the vector ID “QV11” corresponds to the vector QV11 that is a distributed representation of the search query Q11 shown in FIG. 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. The vector information “QVDT11” indicates an N-dimensional vector that is a distributed expression of the search query Q11.

(Model information storage unit 123)
The model information storage unit 123 stores various types of information regarding the learning model generated by the information processing apparatus 100. FIG. 7 shows an example of the model information storage unit according to the embodiment. In the example illustrated in FIG. 7, the model information storage unit 123 includes items such as “model ID” and “model data”.

  “Model ID” indicates identification information for identifying a learning model generated by the information processing apparatus 100. “Model data” indicates model data of a learning model generated by the information processing apparatus 100. For example, “model data” stores data for converting a search query into a distributed expression.

  In the example shown in the first record of FIG. 7, the learning model (learning model M1) identified by the model ID “M1” corresponds to the learning model M1 shown in FIG. The model data “MDT1” indicates model data (model data MDT1) of the learning model M1 generated by the information processing apparatus 100.

  The model data MDT1 includes an input layer to which a search query is input, an output layer, a first element belonging to any layer from the input layer to the output layer and other than the output layer, the first element, and the first element A second element whose value is calculated based on the weight of one element, and outputs a distributed representation of the search query input to the input layer from the output layer in response to the search query input to the input layer As such, the information processing apparatus 100 may function.

  Here, it is assumed that the model data MDT1 is realized by a 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 or x2. 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 correspond to any node of the input layer, and the second element can be regarded as a 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 any node of the input layer or the intermediate layer. The second element corresponds to the next node, which is a node to which a value is transmitted from the node corresponding to the first element. The weight of the first element corresponds to a connection coefficient that is a weight considered for a value transmitted from a node corresponding to the first element to a node corresponding to the second element.

  The information processing apparatus 100 calculates a distributed expression using a model having an arbitrary structure such as the above-described regression model or neural network. Specifically, in the model data MDT1, a coefficient is set so that a distributed expression is output when a search query is input. The information processing apparatus 100 calculates a distributed representation using such model data MDT1.

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

  When the information processing apparatus 100 performs an estimation process using GAN (Generative Adversarial Networks), the model data MDT1 may be a model that constitutes a part of the GAN.

(Control unit 130)
Returning to the description of FIG. 4, the control unit 130 is a controller, and is stored in a storage device inside the information processing apparatus 100 by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. The various programs (corresponding to an example of the generation program) are executed by using the RAM as a work area. The control unit 130 is a controller, and is realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  Further, the control unit 130 belongs to each layer other than the output layer with respect to the search query input to the input layer by information processing according to the learning model M1 (model data MDT1) stored in the model information storage unit 123. By using each element as a first element and performing an operation based on the first element and the weight of the first element, the computer is caused to function so as to output a distributed representation from the output layer.

  As illustrated in FIG. 4, the control unit 130 includes an acquisition unit 131, an extraction unit 132, and a generation unit 133, and realizes or executes the information processing operation described below. Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 4, and may be another configuration as long as the information processing described below is performed.

(Acquisition part 131)
The acquisition unit 131 acquires various information. Specifically, the acquisition unit 131 acquires a search query input by the user from the search server 50. When the acquisition unit 131 acquires a search query input by the user, the acquisition unit 131 stores the acquired search query in the query information storage unit 121.

(Extractor 132)
The extraction unit 132 extracts various information. Specifically, the extraction unit 132 extracts a plurality of search queries input by the same user within a predetermined time from the search queries acquired by the acquisition unit 131. For example, the extraction unit 132 extracts a plurality of search queries in which the time interval when each search query is input by the same user is within a predetermined time. Subsequently, the extraction unit 132 extracts a pair of search queries that are continuously input by the same user within a predetermined time from a plurality of search queries input by the same user within a predetermined time. For example, the extraction unit 132 extracts a plurality of search queries in which a time interval when a pair of search queries is input by the same user is within a predetermined time. For example, the extraction unit 132 includes, among the search queries acquired by the acquisition unit 131, a search query Q11 (“Roppongi Pasta”) that is four search queries that are continuously input by the same user U1 within a predetermined time. ), Search query Q12 (“Roppongi Italian”), search query Q13 (“Akasaka pasta”), and search query Q14 (“Azabu pasta”). The extraction unit 132 extracts four search queries that are input in the order of the search query Q11, the search query Q12, the search query Q13, and the search query Q14 when arranged in the order in which the search queries are input. Subsequently, when extracting four search queries, the extraction unit 132 uses a pair of search queries as two search queries adjacent to each other in time series (search query Q11, search query). Q12), (search query Q12, search query Q13), and (search query Q13, search query Q14) are extracted. Note that the extraction unit 132 may extract a plurality of search queries in which all search queries are input by the same user within a predetermined time. Then, the extraction unit 132 selects two search queries from the plurality of extracted search queries regardless of whether or not they are adjacent in time series, and uses the selected two search queries as a pair of search queries. It may be extracted.

  The extraction unit 132 also extracts a predetermined search query and other search queries unrelated to the predetermined search query from the search queries acquired by the acquisition unit 131. For example, the extraction unit 132 extracts a predetermined search query from the search queries acquired by the acquisition unit 131. Subsequently, the extraction unit 132 randomly extracts another search query from the search queries acquired by the acquisition unit 131 regardless of the predetermined search query.

(Generator 133)
The generation unit 133 generates various information. Specifically, the generation unit 133 learns that among the search queries acquired by the acquisition unit 131, a plurality of search queries input by the same user within a predetermined time have similar characteristics. A learning model for predicting feature information of a predetermined search query from the predetermined search query is generated. Specifically, the generation unit 133 learns a learning model so that distributed expressions of a plurality of search queries input within a predetermined time by the same user are similar, thereby performing a predetermined search from a predetermined search query. A learning model that predicts query feature information is generated. For example, the generation unit 133 generates a learning model by learning so that the distributed expressions of a pair of search queries input in succession within a predetermined time are similar. For example, the generation unit 133 calculates a similarity value of a distributed expression (vector) before learning a pair of search queries. In addition, the generation unit 133 calculates the similarity value of the distributed expression (vector) after learning the pair of search queries. Subsequently, the generation unit 133 causes the learning model to learn so that the similarity value of the distributed representation (vector) after learning is larger than the similarity value of the distributed representation (vector) before learning. As described above, the generation unit 133 learns the learning model so that two vectors, which are a pair of distributed expressions corresponding to a pair of search queries, are similar in the distributed expression space, so that the distributed expressions (vectors) are obtained from the search query. ) Is generated. More specifically, the generation unit 133 generates a learning model that outputs a distributed expression (vector) from a search query, using a DSSM technique that uses LSTM, which is a kind of RNN, for distributed expression generation. For example, the generation unit 133 assumes that a pair of search queries input by the same user within a predetermined time as the correct answer data of the learning model has similar characteristics, and a distributed expression (vector) of the predetermined search query and Then, learning is performed so that a distributed expression (vector) of another search query paired with a predetermined search query exists in the vicinity in the distributed expression space.

[Example of learning model]
Here, an example of a learning model generated by the information processing apparatus 100 will be described with reference to FIG. FIG. 8 is a diagram illustrating an example of a learning model according to the embodiment. In the example illustrated in FIG. 8, the learning model M1 generated by the information processing apparatus 100 is configured with three layers of LSTM RNNs. In the example illustrated in FIG. 8, the extraction unit 132 includes a pair of search queries Q11 “Roppongi pasta” and search queries Q12 “Roppongi Italian” that are continuously input by the same user U1 within a predetermined time. Extract search queries. The generation unit 133 inputs the search query Q11 extracted by the extraction unit 132 to the input layer of the learning model M1 (Step S31).

  Subsequently, the generation unit 133 outputs a 256-dimensional vector BQV11 that is a distributed representation of the search query Q11 from the output layer of the learning model M1. In addition, the generation unit 133 inputs the search query Q12 extracted by the extraction unit 132 to the input layer of the learning model M1. Subsequently, the generation unit 133 outputs a 256-dimensional vector BQV12 that is a distributed representation of the search query Q12 from the output layer of the learning model M1 (step S32).

  Subsequently, the generation unit 133 generates a learning model that outputs a distributed expression (vector) from the search query by learning so that the distributed expressions (vectors) of two search queries input in succession are similar to each other. (Step S33). For example, the angle between the vector BQV11, which is a distributed representation of the search query Q11 before applying feedback to the learning model M1 (before learning), and the vector BQV12, which is a distributed representation of the search query Q12, is Θ. Also, let Φ be the angle between the vector QV11, which is a distributed representation of the search query Q11 after feedback is applied to the learning model M1 (after learning), and the vector QV12, which is a distributed representation of the search query Q12. At this time, the generation unit 133 causes the learning model M1 to be learned so that Φ is smaller than Θ. For example, the generation unit 133 calculates the value of the cosine similarity between the vector BQV11 and the vector BQV12. In addition, the generation unit 133 calculates the value of the cosine similarity between the vector QV11 and the vector QV12. Subsequently, the generation unit 133 causes the learning model M1 so that the value of the cosine similarity between the vectors QV11 and QV12 is larger than the value of the cosine similarity between the vectors BQV11 and BQV12 (so that the value approaches 1). To learn. As described above, the generation unit 133 learns the learning model M1 so that two vectors, which are a pair of distributed expressions corresponding to a pair of search queries, are similar on the distributed expression space, so that the distributed expressions ( A learning model M1 that outputs (vector) is generated. Note that the generation unit 133 is not limited to the cosine similarity, and can calculate the similarity between the distributed representations (vectors) based on any index as long as it is an index applicable as a distance measure between vectors. Good. Further, the generation unit 133 may learn the learning model M1 based on any index as long as it is an index applicable as a distance measure between vectors. For example, the generation unit 133 calculates a value of a predetermined distance function such as a Euclidean distance between distributed expressions (vectors), a distance in a non-Euclidean space such as a hyperbolic space, a Manhattan distance, a Mahalanobis distance, or the like. Subsequently, the generation unit 133 may learn the learning model M1 so that the value of a predetermined distance function between the distributed representations (vectors) (that is, the distance in the distributed representation space) becomes small.

  Further, the generation unit 133 learns that a plurality of search queries including character strings separated by a predetermined delimiter have similar characteristics as a plurality of search queries input by the same user within a predetermined time. By doing so, a learning model is generated. For example, the generation unit 133 includes a search query “Roppongi pasta” in which “Roppongi” indicating a place name and “pasta” indicating a food type are separated by a space as a delimiter, and “Roppongi” indicating a place name. A learning model is generated by learning that the search query “Roppongi Italian” in which the character “Italian” indicating the type of dish is separated by a space as a delimiter has similar characteristics.

  In addition, the generation unit 133 generates a learning model by learning that the search queries acquired by the acquisition unit 131 have different characteristics from a plurality of randomly extracted search queries. Specifically, the generation unit 133 generates a learning model by learning so that the distributed expressions of a pair of search queries extracted at random among the search queries acquired by the acquisition unit 131 are different. For example, the generation unit 133 may distant the distributed representation of the predetermined search query extracted by the extraction unit 132 and the distributed representation of the search query randomly extracted regardless of the predetermined search query in the distributed expression space. Training of the learning model M1 is performed so as to be mapped.

[3. Information processing flow)
Next, an information processing procedure according to the embodiment will be described with reference to FIG. FIG. 9 is a flowchart illustrating an information processing procedure according to the embodiment. In the example illustrated in FIG. 9, the information processing apparatus 100 acquires a search query input by the user (step S101).

  Subsequently, the information processing apparatus 100 extracts a plurality of search queries input by the same user within a predetermined time (step S102).

  Subsequently, the information processing apparatus 100 learns that the extracted search query has similar characteristics, thereby generating a learning model that outputs a distributed expression (vector) from the predetermined search query (step S103).

[4. effect〕
As described above, the information processing apparatus 100 according to the embodiment includes the acquisition unit 131 and the generation unit 133. The acquisition unit 131 acquires a search query input by the user. The generation unit 133 learns that, among the search queries acquired by the acquisition unit 131, a plurality of search queries input by the same user within a predetermined time have similar characteristics, so that the predetermined search query A learning model for predicting feature information of a predetermined search query is generated.

  Generally, since it is considered that a user performs a search a plurality of times with a certain intention, it is assumed that a search query continuously input within a predetermined time has a close search intention. Therefore, the information processing apparatus 100 according to the present invention is similar to each other in that a plurality of search queries input continuously within a predetermined time are search queries searched under a predetermined search intention. The learning model M1 is learned by assuming that the search query has the characteristic of Specifically, the information processing apparatus 100 according to the present invention causes the learning model M1 to learn so that the distributed expressions of the search queries that are continuously input within a predetermined time are similar, thereby searching for a search with a close search intention. The distributed expression of the query can be output at a position close to the distributed expression space. As a result, the information processing apparatus 100 can output (interpret) the meaning (search intention) of the search query according to the context of the user who has input the search query. Therefore, the information processing apparatus 100 can appropriately interpret the meaning of the search query. Further, the information processing apparatus 100 extracts a search query corresponding to the distributed expression mapped in the vicinity of the distributed expression including the feature information of the predetermined search query, thereby responding to the search intention for searching the predetermined search query. Search query can be extracted. That is, the information processing apparatus 100 can analyze a user's search trend based on the context of the user who has input the search query. Therefore, the information processing apparatus 100 can improve the analysis accuracy of the user's search trend. The learning model M1 generated by the information processing apparatus 100 can also function as a part of the search system. Alternatively, the information processing apparatus 100 uses a distributed expression of the search query output by the learning 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 learning model M1. It can also be provided. As a result, the search system can select content output as a search result based on the feature information of the search query predicted by the learning 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 has input the search query. Further, the search system can calculate the similarity between the distributed representation of the character string included in the content output as the search result and the distributed representation of the search query, based on the feature information of the search query predicted by the learning model M1. become. The search system can determine the display order of the content output as the search result based on the calculated similarity. That is, the search system can determine the display order of contents output as search results in consideration of the search intention and context of the user who has input the search query. Therefore, the information processing apparatus 100 can improve usability in the search service.

  In addition, the generation unit 133 generates a learning model by learning so that the distributed expressions of a pair of search queries input in succession within a predetermined time are similar.

  In general, two search queries input by the same user in a short time are considered to have the same search intention or close search intention even if they are not the same. That is, it is considered that the search intentions of a pair of search queries that are continuously input within a predetermined time period have the same search intention or the search intentions are similar even if they are not the same. That is, the information processing apparatus 100 can extract a search query according to a user's search intention more accurately by learning so that the distributed expressions of a pair of search queries that are continuously input within a predetermined time are similar. It can be.

  Further, the generation unit 133 learns that a plurality of search queries including character strings separated by a predetermined delimiter have similar characteristics as a plurality of search queries input by the same user within a predetermined time. By doing so, a learning model is generated.

  Generally, a search query including a plurality of character strings is considered to have a clearer search intention than a search query including a single character string. In other words, the information processing apparatus 100 can more accurately extract a search query according to a user's search intention by learning using a search query including a character string delimited by a predetermined delimiter. .

  In addition, the generation unit 133 generates a learning model by learning that the search queries acquired by the acquisition unit 131 have different characteristics from a plurality of randomly extracted search queries. In addition, the generation unit 133 generates a learning model by learning so that the distributed expressions of a pair of search queries extracted at random among the search queries acquired by the acquisition unit 131 are different.

  Generally, a plurality of search queries extracted at random are search queries that are input independently of each other, and therefore, the search intention is different or the search intention is considered to be far away. Therefore, the information processing apparatus 100 according to the present invention is a search query having characteristics that are different from each other in that the plurality of search queries extracted at random are search queries that are searched under different search intentions. The learning model M1 is learned by assuming that there is. Specifically, the information processing apparatus 100 according to the present invention causes the learning model M1 to learn so that the distributed expressions of a plurality of randomly extracted search queries are different, thereby distributing the distributed expressions of search queries having different search intentions. Can be output at a far position in the distributed expression space. Thus, the learning model can learn incorrect answer data that is a pair of search queries with a distant search intention in addition to correct answer data that is a pair of search queries with a close search intention. That is, the information processing apparatus 100 can improve the accuracy of the learning model. Therefore, the information processing apparatus 100 can appropriately interpret the meaning of the search query.

[5. Hardware configuration)
Further, the information processing apparatus 100 according to the embodiment described above is realized by a computer 1000 having a configuration as shown in FIG. 9, for example. FIG. 9 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing apparatus 100. The computer 1000 includes a CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and 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 unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program depending on the hardware of the computer 1000, and the like.

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

  The CPU 1100 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. In addition, 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 it to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a 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. Etc.

  For example, when the computer 1000 functions as the information processing apparatus 100 according to the embodiment, the CPU 1100 of the computer 1000 executes a program or data loaded on the RAM 1200 (for example, model data MDT1 of the learning model M1). The function of the control unit 130 is realized. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800. As another example, these programs or data (for example, model data of the learning model M1) from other devices via a predetermined communication network. MDT1) may be acquired.

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

[6. Others]
In addition, among the processes described in the above-described embodiments and modifications, all or a part of the processes described as being automatically performed can be manually performed, or are described as being performed manually. All or part of the processing can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the document and drawings can be arbitrarily changed unless otherwise specified. For example, the various types of information illustrated in each drawing is not limited to the illustrated information.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

  In addition, the above-described embodiments and modifications can be combined as appropriate within a range that does not contradict processing contents.

  In addition, the “section (module, unit)” described above can be read as “means” or “circuit”. For example, the generation unit can be read as generation means or a generation circuit.

DESCRIPTION OF SYMBOLS 1 Information processing system 10 User terminal 50 Search server 100 Information processing apparatus 121 Query information storage part 122 Vector information storage part 123 Model information storage part 131 Acquisition part 132 Extraction part 133 Generation part

Claims (7)

An acquisition unit for acquiring a search query input by a user during a search;
In the search query obtained by the obtaining unit, a distributed representation of the search query entered within a predetermined time by the same user, distributed representation of the search query corresponding to each search performed by the user Learning models that output a distributed representation of the predetermined search query from a predetermined search query by learning by sequentially processing the search queries in the order in which the search queries are input as having similar characteristics to each other An information processing apparatus comprising: a generation unit that generates The generator is
The information processing apparatus according to claim 1, wherein the learning model is generated by learning so that distributed expressions of a pair of search queries input in succession within the predetermined time are similar to each other . The generator is
As distributed representation of search query entered within a predetermined time by the same user is learned as having a characteristic that a string delimited by predetermined delimiters distributed representation of including search queries similar to each other The information processing apparatus according to claim 1, wherein the learning model is generated. The generator is
The learning model is generated by learning the search queries acquired by the acquisition unit as having different characteristics from each other in a distributed expression of a plurality of search queries extracted at random. Item 4. The information processing apparatus according to any one of Items 1 to 3. The generator is
The learning model is generated by learning so that distributed expressions of a pair of search queries extracted at random among the search queries acquired by the acquisition unit are different from each other . 5. The information processing apparatus according to any one of 4. An information processing method executed by a computer,
An acquisition process for acquiring a search query input by a user during a search;
In the search query obtained by the obtaining step, a distributed representation of the search query entered within a predetermined time by the same user, distributed representation of the search query corresponding to each search performed by the user Learning models that output a distributed representation of the predetermined search query from a predetermined search query by learning by sequentially processing the search queries in the order in which the search queries are input as having similar characteristics to each other An information processing method comprising: a generation step of generating An acquisition procedure for acquiring a search query entered by a user during a search,
In the search query acquired by the acquisition procedure, a distributed representation of the search query entered within a predetermined time by the same user, distributed representation of the search query corresponding to each search performed by the user Learning models that output a distributed representation of the predetermined search query from a predetermined search query by learning by sequentially processing the search queries in the order in which the search queries are input as having similar characteristics to each other An information processing program for causing a computer to execute a generation procedure for generating a message.

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