JP7079867B1 - Generator, generation method and generation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To further grasp the relationship between a group of users associated with a predetermined event and another event.
A generation device according to the present application has an acquisition unit that acquires history information indicating the history of a search query input by a group of users associated with a predetermined event, and a search unit that acquires history information acquired by the acquisition unit. A classification unit that classifies queries into one of a plurality of predetermined categories, and each search query classified into a predetermined category by the classification unit based on historical information, and the degree of association between a predetermined event and the predetermined category. It is characterized by having a calculation unit for calculating the above and a generation unit for generating relevance information indicating the relevance degree of each search query calculated by the calculation unit for each of a plurality of predetermined categories.
[Selection diagram] Fig. 1

Description

The present invention relates to a generator, a generator and a generator.

Conventionally, a technique for providing various information to a user via the Internet is known. As an example of such a technique, there is known a technique in which a search keyword frequently input among the search keywords input by a user who browses an advertisement is used as a keyword for a search-linked advertisement.

Japanese Unexamined Patent Publication No. 2014-006757

However, with the above-mentioned technique, it may not be possible to grasp the relationship between the user group associated with a predetermined event and other events.

For example, in the above-mentioned technique, a user who has viewed the advertisement of the advertiser of the target advertisement is only identified from the log data, and the search keyword input by the user is only extracted, and the user associated with a predetermined event is associated with the user. It may not be possible to say that the group has a further relationship with other events.

The present application has been made in view of the above, and an object thereof is to grasp the relationship between a user group associated with a predetermined event and another event.

The generation device according to the present application has a plurality of acquisition units that acquire history information indicating the history of search queries input by a group of users associated with a predetermined event, and a plurality of search queries indicated by the history information acquired by the acquisition unit. The degree of relevance between the classification unit that classifies into any of the predetermined categories, each search query classified into the predetermined category by the classification unit based on the history information, the predetermined event, and the predetermined category. It is characterized by having a calculation unit for calculating the above and a generation unit for generating relevance information indicating the relevance degree of each search query calculated by the calculation unit for each of a plurality of the predetermined categories.

According to one aspect of the embodiment, there is an effect that the user group associated with a predetermined event can further grasp the relationship with other events.

FIG. 1 is a diagram showing an example of a generation process according to an embodiment. FIG. 2 is a diagram showing a configuration example of the information processing apparatus according to the embodiment. FIG. 3 is a diagram showing an example of a history information database according to an embodiment. FIG. 4 is a diagram showing an example of a relevance information database according to an embodiment. FIG. 5 is a flowchart showing an example of the procedure of the generation process according to the embodiment. FIG. 6 is a hardware configuration diagram showing an example of a computer that realizes the function of the information providing device.

Hereinafter, a generation device, a generation method, and a mode for implementing the generation program (hereinafter referred to as “embodiment”) according to the present application will be described with reference to the drawings. It should be noted that this embodiment does not limit the generation device, generation method and generation program according to the present application. Further, in the following embodiments, the same parts are designated by the same reference numerals, and duplicate explanations are omitted.

[1. Embodiment]
The generation process realized by the generation apparatus and the like of this embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of a generation process according to an embodiment. In FIG. 1, it is assumed that the information processing device 10 which is an example of the generation device according to the present application realizes the generation process and the like according to the embodiment.

As shown in FIG. 1, the generation system 1 according to the embodiment includes an information processing device 10 and a terminal device 100. The information processing device 10 and the terminal device 100 are connected to each other via a network N (see, for example, FIG. 2) so as to be able to communicate with each other by wire or wirelessly. The network N is, for example, a WAN (Wide Area Network) such as the Internet. The generation system 1 shown in FIG. 1 may include a plurality of information processing devices 10 and a plurality of terminal devices 100.

The information processing device 10 shown in FIG. 1 is an information processing device that performs generation processing, and is realized by, for example, a server device, a cloud system, or the like. For example, the information processing apparatus 10 acquires information indicating the history of a search query input by a user in a predetermined search service, and based on the acquired information, a user group associated with a predetermined event and the use thereof. Generates information that is relevant to the search query entered by the group of people. To give a specific example, the information processing apparatus 10 includes a group of users whose attributes estimated based on the usage history of various services provided by the own apparatus or another server apparatus satisfy a predetermined condition, and the said user group. Generates information that shows the relevance to the search query entered by the user group in various services.

The services provided by the information processing device 10 and other server devices include, for example, a search service, a shopping service, a payment service, a route search service, a map providing service, a travel service, a restaurant introduction service, and a weather forecast service. , Schedule management service, news provision service, auction service, video content distribution service, financial transaction (stock transaction, etc.) service and the like.

The terminal device 100 shown in FIG. 1 is an information processing device used by a user. The terminal device 100 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. In the example shown in FIG. 1, the terminal device 100 is a smartphone used by a user.

Hereinafter, an example of the generation process performed by the information processing apparatus 10 will be described with reference to FIG. 1. In the following description, the terminal device 100 will be described as the terminal devices 100-1 to 100-N (N is an arbitrary natural number) depending on the user who uses the terminal device 100. For example, the terminal device 100-1 is a terminal device 100 used by a user (user U1) identified by the user ID "UID # 1". Further, in the following, when the terminal devices 100-1 to 100-N will be described without particular distinction, they will be referred to as the terminal device 100. Further, in the following description, the terminal device 100 may be equated with the user. That is, in the following, the user U1 can be read as the terminal device 100-1.

First, the information processing apparatus 10 acquires user information (history information) about the user (step S1). For example, the information processing apparatus 10 transmits user information including identification information (user ID) for identifying a user, usage history in various services, search history in various services, and the like, terminal devices 100-1, 100-. 2. Obtained from a predetermined server device or the like.

Subsequently, the information processing apparatus 10 extracts a user group (seed user) associated with a predetermined event (for example, a predetermined action or a predetermined attribute) based on the user information (step S2). In the example of FIG. 1, the information processing apparatus 10 extracts the user group G1 whose attribute is presumed to be "mother" based on the user information.

Subsequently, the information processing apparatus 10 extracts each search query input by each user belonging to the user group G1 (step S3). For example, in the information processing apparatus 10, users GU1, GU2, ... Belonging to the user group G1 input in various services within a predetermined period (for example, the latest week, one month, one year, etc.). Extract each search query (search history).

Subsequently, the information processing apparatus 10 classifies the search query extracted in step S3 into any of the categories related to the predetermined event (step S4). For example, the information processing apparatus 10 classifies search queries extracted into any of the categories "child-rearing", "smart device", "cooking", ..., Etc., which are previously associated with the attribute "mother". To give a specific example, in the example of FIG. 1, the information processing apparatus 10 classifies the search query group D # 1 (search query # 11, # 21, # 31, ...) In the category "child-rearing". Then, the search query group D # 2 (search query # 12, # 22, # 32, ..., etc.) is classified into the category "smart device", and the search query group D # 3 (search query #) is classified into the category "cooking". 13, # 23, # 33, ..., etc.) are classified.

The information processing apparatus 10 may realize the classification process of the search query by language recognition using a model generated by appropriately using various conventional techniques related to machine learning. Here, the model generation may be performed using a technique related to machine learning such as supervised learning or unsupervised learning. To give a specific example, model generation may be performed using a technique related to machine learning such as SVM (Support Vector Machine), such as DNN (Deep Neural Network), RNN (Recurrent Neural Network), and CNN. It may be performed by appropriately using various deep learning techniques such as (Convolutional Neural Network). It should be noted that the description regarding the generation of the above model is an example, and the generation of the model may be performed by a learning method appropriately selected according to the information that can be acquired and the like.

Subsequently, the information processing apparatus 10 calculates the degree of association between each search query classified into categories and a predetermined event and category (step S5). For example, the information processing apparatus 10 has a relationship between each search query classified into any category such as "child-rearing", "smart device", "cooking", ..., The attribute "mother", and the category. Is calculated. To give a specific example, when calculating the degree of association between each search query classified into the category "child-rearing" and the attribute "mother" and the category "child-rearing", the information processing apparatus 10 uses the search query group D #. For each search query included in 1, the number of times the user included in the user group G1 entered (or the number of users included in the user group G1 who entered the search query) was counted, and the counted number of times was counted. The higher the value (positive value), the higher the degree of relevance is calculated. Further, the information processing apparatus 10 refers to a user group (hereinafter, may be referred to as a "comparative user group") that does not belong to the user group G1 among the users indicated by the user information acquired in step S1. Among the search queries that are randomly selected and input by the comparison user group, the search queries that are not input by the user group G1 and are not classified into the category "child-rearing" are specified as non-input queries. Then, the information processing apparatus 10 counts the number of times the specified non-input query is input by the contrasting user group (or the number of users included in the contrasting user group who input the non-input query), and the counted number of times. The higher the number, the lower the relevance of the value (negative value) is calculated. The information processing apparatus 10 also performs the same processing as described above for each search query classified into other categories.

Subsequently, the information processing apparatus 10 generates relevance information (model) indicating the relevance of each search query for each category (step S6). For example, the information processing apparatus 10 has a search query included in the search query group D # 1, a positive relevance calculated for the search query, and a non-input query (a search query input by a comparison user group). , A search query that is not entered by the user group G1 and is not classified in the category "child-rearing") and a model showing the negative relevance calculated for the non-input query, with the attribute "mother" and the category "child-rearing". It is generated as model M # 1 corresponding to. Similarly, the information processing apparatus 10 generates a model M # 2 corresponding to the attribute “mother” and the category “smart device”, a model M # 3 corresponding to the attribute “mother” and the category “cooking”, and the like. do.

The information processing apparatus 10 may generate models M # 1, M # 2, M # 3, ..., Etc. by appropriately using various conventional techniques related to machine learning. For example, when the model M # 1 is generated using a technique related to machine learning, the information processing apparatus 10 learns to calculate a higher degree of relevance when a search query included in the search query group D # 1 is input. And learn to calculate a higher degree of relevance when entering a non-input query.

Subsequently, the information processing apparatus 10 identifies a user to whom the information is provided based on the generated relevance information, and provides the information (step S7). For example, the information processing apparatus 10 may, for example, provide user information including identification information for identifying a user and search history in various services to terminal devices 100-3, 100-4, ...・ ・ Obtain from the specified server device, etc. Then, the information processing apparatus 10 has attributes based on the search history of the users U3, U4, ..., Etc. within a predetermined period, and the models M # 1, M # 2, M # 3, ..., Etc. It identifies a user who is associated with "mother" and is associated with any of the categories "child-rearing", "smart device", "cooking", ..., And provides predetermined information.

To give a specific example, for the search query indicated by the search history of the user U3, when the total of each degree of association indicated by the model M # 1 is equal to or greater than a predetermined threshold value (for example, a positive value), the information processing apparatus. Reference numeral 10 identifies the attribute of the user U3 as "mother raising a child" and provides information on child-rearing support for mothers (women) and information on child-rearing products (nursing products, etc.) for women. Further, for the search query indicated by the search history of the user U4, the total of the relevance degrees indicated by the model M # 1 is equal to or higher than a predetermined threshold value, and the relevance degree indicated by the model M # 3 for the search query. When the total of is less than or equal to a predetermined threshold (for example, a negative value), the information processing apparatus 10 identifies the attribute of the user U4 as "a mother who is raising a child who is not interested in cooking" and is intended for baby food and children. Provide information about food or meal delivery services.

As described above, the information processing apparatus 10 according to the embodiment has a search query classified into a predetermined category among search queries input by a group of users associated with a predetermined event, a predetermined event, and a predetermined event. Calculate the degree of relevance of categories and generate a model for each category based on the degree of relevance. As a result, the information processing apparatus 10 according to the embodiment can grasp the relationship between the user group associated with the predetermined event and the other event. Further, the information processing apparatus 10 according to the embodiment can specify more detailed attributes for the user by using the plurality of models generated as described above.

[2. Information processing device configuration]
Next, the configuration of the information processing apparatus 10 will be described with reference to FIG. FIG. 2 is a diagram showing a configuration example of the information processing apparatus according to the embodiment. As shown in FIG. 2, the information processing apparatus 10 includes a communication unit 20, a storage unit 30, and a control unit 40.

(About communication unit 20)
The communication unit 20 is realized by, for example, a NIC (Network Interface Card) or the like. Then, the communication unit 20 is connected to the network N by wire or wirelessly, and transmits / receives information to / from the terminal device 100 and the like.

(About the storage unit 30)
The storage unit 30 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. 2, the storage unit 30 has a history information database 31 and a relevance information database 32.

(About history information database 31)
The history information database 31 stores various information related to the history of search queries entered by the user and the usage history of various services. Here, an example of the information stored in the history information database 31 will be described with reference to FIG. FIG. 3 is a diagram showing an example of a history information database according to an embodiment. In the example of FIG. 3, the history information database 31 has items such as "user ID", "search history", and "usage history".

The "user ID" indicates identification information for identifying a user. The "search history" indicates the history of the search query entered by the user. "Usage history" indicates the usage history of users in various services.

That is, FIG. 3 shows an example in which the search history of the user identified by the user ID “UID # 1” is “search history # 1” and the usage history of the service is “usage history # 1”.

(About the relevance information database 32)
The relevance information database 32 stores various information related to the relevance information generated based on the information stored in the history information database 31. Here, an example of the information stored in the relevance information database 32 will be described with reference to FIG. FIG. 4 is a diagram showing an example of a relevance information database according to an embodiment. In the example of FIG. 4, the relevance information database 32 has items such as "event", "category", and "relevance information".

"Event" indicates an event used when extracting a user group (seed user). "Category" indicates the category associated with the event. The "relevance information" indicates information indicating the relevance between the search query entered by the user and a predetermined event and category.

That is, FIG. 4 shows an example in which the relevance information indicating the relevance degree between the search query input by the user and the event "event # 1" and the category "category # 1" is "relevance degree information # 11". ..

(About control unit 40)
The control unit 40 is a controller, and for example, various programs stored in a storage device inside the information processing device 10 by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like have a RAM as a work area. It is realized by executing as. Further, the control unit 40 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. 2, the control unit 40 according to the embodiment includes an acquisition unit 41, a classification unit 42, a calculation unit 43, a generation unit 44, a specific unit 45, a determination unit 46, and a provision unit 47. To realize or execute the functions and actions of information processing described below.

(About acquisition unit 41)
The acquisition unit 41 acquires history information indicating the history of the search query input by the user group associated with the predetermined event. For example, in the example of FIG. 1, the acquisition unit 41 acquires user information (history information) including usage history in various services of the user, search history in various services, and stores the user information (history information) in the history information database 31.

(About classification unit 42)
The classification unit 42 classifies the search query indicated by the history information acquired by the acquisition unit 41 into one of a plurality of predetermined categories. For example, in the example of FIG. 1, the classification unit 42 refers to the history information database 31, and "child-rearing" each search query entered by each user belonging to the user group G1 whose attribute is presumed to be "mother". , "Smart device", "Cooking", ..., etc.

Further, the classification unit 42 may classify the search query into any of a plurality of predetermined categories that are related to the predetermined event. For example, in the example of FIG. 1, the classification unit 42 classifies each search query entered by each user belonging to the user group G1 into one of the categories previously associated with the attribute “mother”.

Further, the classification unit 42 may classify the search query into any of a plurality of predetermined categories having a hierarchical structure. For example, the classification unit 42 classifies the category "fashion" into one of the categories showing the subordinate concept of "fashion" (for example, men's fashion, ladies' fashion, etc.).

The classification unit 42 may classify one search query into a plurality of categories. For example, by taking FIG. 1 as an example, the classification unit 42 may classify one search query as a search query belonging to the categories of "child-rearing" and "smart device".

(About calculation unit 43)
Based on the history information, the calculation unit 43 calculates the degree of relevance between each search query classified into a predetermined category by the classification unit 42, a predetermined event, and the predetermined category. For example, in the example of FIG. 1, when calculating the degree of association between each search query classified into the category “child-rearing” and the attribute “mother” and the category “child-rearing”, the calculation unit 43 may use the search query group D # 1. For each search query included in, the number of times entered by the user included in the user group G1 is counted, and the higher the number of times counted, the higher the relevance degree is calculated. Further, the calculation unit 43 specifies as a non-input query a search query that is not input by the user group G1 and is not classified into the category "child-rearing" among the search queries input by the comparison user group. Then, the calculation unit 43 counts the number of times the comparison user group inputs the specified non-input query, and calculates the degree of relevance having a lower value as the number of times counted increases.

(About the generator 44)
The generation unit 44 generates relevance information indicating the relevance degree of each search query calculated by the calculation unit 43 for each of a plurality of predetermined categories. For example, in the example of FIG. 1, the generation unit 44 calculates the search query included in the search query group D # 1, the positive relevance calculated for the search query, and the non-input query and the non-input query. A model showing the negative relevance is generated as model M # 1 corresponding to the attribute "mother" and the category "child-rearing", and stored in the relevance information database 32.

(About the specific part 45)
The identification unit 45 identifies a user who is associated with a predetermined event and is associated with a predetermined category based on the search query input by the user and the relevance information. For example, in the example of FIG. 1, the specifying unit 45 refers to the history information database 31 and the relevance information database 32, and for the search query indicated by the search history, the total of the relevance indicated by the model M # 1 is a predetermined threshold value. The above user U3 is specified.

Further, even if the specific unit 45 identifies a user who is associated with the first category included in the plurality of predetermined categories and is not associated with the second category included in the plurality of predetermined categories. good. For example, in the example of FIG. 1, the specific unit 45 has a search query indicated by a search history in which the total of the relevance degrees indicated by the model M # 1 is equal to or more than a predetermined threshold value, and the search query is a model M. A user U4 whose sum of each degree of association indicated by # 3 is equal to or less than a predetermined threshold value is specified.

Here, in addition to the above example, there may be a request to specify a user having detailed characteristics by combining the specific results of the user by each model. Therefore, the specific unit 45 may specify a user associated with the first category included in the plurality of predetermined categories and associated with the second category included in the plurality of predetermined categories. .. For example, to explain using FIG. 1 as an example, the specific unit 45 describes the search query indicated by the search history in which the total of the relevance degrees indicated by the model M # 1 is equal to or more than a predetermined threshold value. , Identify a user whose sum of each degree of relevance indicated by model M # 2 is equal to or greater than a predetermined threshold.

Further, the specifying unit 45 may specify a user associated with either a first category included in a plurality of predetermined categories or a second category included in a plurality of predetermined categories. For example, when FIG. 1 is taken as an example, the specific unit 45 describes whether the total of the relevance degrees shown by the model M # 1 is equal to or more than a predetermined threshold value for the search query indicated by the search history, or the model M for the search query. Identify users whose sum of each degree of association indicated by # 2 is equal to or greater than a predetermined threshold.

Further, the specific unit 45 may specify a user who is not associated with the first category included in the plurality of predetermined categories and is associated with a category other than the first category among the plurality of predetermined categories. good. For example, to explain using FIG. 1 as an example, in the search query indicated by the search history, the total of the relevance degrees indicated by the model M # 1 is equal to or less than a predetermined threshold value, and the search query is described. A user whose sum of the relevance degrees indicated by any of the models M # 2, M # 3, ... Is equal to or higher than a predetermined threshold value is specified.

Further, the specific unit 45 is not associated with the first category and the second category included in the plurality of predetermined categories, and is other than the first category and the second category among the plurality of predetermined categories. A user associated with any of the categories may be specified. For example, to explain using FIG. 1 as an example, in the specific unit 45, the total of the relevance degrees shown by the model M # 1 is equal to or less than a predetermined threshold value for the search query indicated by the search history, and the model M is used for the search query. Utilization in which the total of each degree of association indicated by # 2 is equal to or less than a predetermined threshold value, and the total of each degree of association indicated by any of models M # 3, ... For the search query is equal to or greater than a predetermined threshold value. Identify the person.

Further, the specific unit 45 is associated with either the first category or the second category included in the plurality of predetermined categories, and is not associated with both the first category and the second category. The user may be specified. For example, when FIG. 1 is taken as an example, the specific unit 45 describes the total of the relevance degrees shown by the model M # 1 or the total of the relevance degrees shown by the model M # 2 for the search query indicated by the search history. Any of them is equal to or more than a predetermined threshold, and for the search query, both the total of the relevance indicated by the model M # 1 and the total of the relevance indicated by the model M # 2 are equal to or higher than the predetermined threshold. Identify the users who do not become.

(About the judgment unit 46)
The determination unit 46 determines the degree to which the user is associated with a predetermined category based on the relevance indicated by the relevance information and the search query input by the user. For example, to explain using FIG. 1 as an example, the determination unit 46 determines that the search query indicated by the search history is a user who has a higher degree of interest in cooking as the total of each degree of relevance indicated by model M # 1 is higher. However, the lower the total, the lower the degree of interest in cooking.

(About the provider 47)
The providing unit 47 provides information related to a predetermined category to the user specified by the specific unit 45. For example, in the example of FIG. 1, the providing unit 47 identifies the attribute of the user U3 associated with the category “child-rearing” as “mother during child-rearing”, and provides information on child-rearing support for mothers and child-rearing for women. Provide information about supplies.

Further, the providing unit 47 may provide the user with information according to the correspondence with a plurality of predetermined categories. For example, in the example of FIG. 1, the providing unit 47 identifies the attribute of the user U4, which is associated with the category “child-rearing” and is not associated with the category “cooking”, as “a mother raising a child who is not interested in cooking”. And provide information on baby food and food delivery services for children or meals.

Further, the providing unit 47 may provide the user with information related to the category associated with the user according to the degree of determination by the determination unit 46. For example, the providing unit 47 provides information related to the category to a user who is determined to have a high degree of association with the category.

[3. Generation process flow]
A procedure for generating the information processing apparatus 10 according to the embodiment will be described with reference to FIG. FIG. 5 is a flowchart showing an example of the procedure of the generation process according to the embodiment.

As shown in FIG. 5, the information processing apparatus 10 acquires history information indicating the history of the search query input by the user group associated with a predetermined event (step S101). Subsequently, the information processing apparatus 10 classifies the search query indicated by the history information into any of a plurality of predetermined categories (step S102). Subsequently, the information processing apparatus 10 calculates the degree of association between each search query classified into a predetermined category, a predetermined event, and the predetermined category based on the history information (step S103). Subsequently, the information processing apparatus 10 generates relevance information indicating the relevance degree of each calculated search query for each predetermined category (step S104), and ends the process.

[4. Modification example]
The above embodiment shows an example, and various modifications and applications are possible.

[4-1. About calculation of relevance]
In the above-described embodiment, when the calculation unit 43 calculates the degree of relevance regarding the attribute "mother" and the category "child-rearing", the user group not belonging to the user group G1 is set as the contrast user group, and the contrast user group is used. Among the input search queries, an example is shown in which the relevance is calculated by using a search query that is not input by the user group G1 and is not classified into the category "child-rearing" as a non-input query. However, the function of the calculation unit 43 is not limited to such an example, and the degree of relevance may be calculated by any method. For example, to explain using FIG. 1 as an example, when calculating the degree of relevance regarding the attribute “mother” and the category “child-rearing”, the calculation unit 43 uses the user group G1 among the search queries input by the contrast user group. A query that has not been entered may be regarded as a non-input query and the degree of relevance may be calculated. Further, the calculation unit 43 may set the comparison user group as a user group in which a search query that does not belong to the category "child-rearing" including the user belonging to the user group G1 is input and calculates the degree of relevance.

[4-2. About processing mode]
Of the processes described in the above embodiments, all or part of the processes described as being automatically performed can be performed manually, and conversely, all the processes described as being performed manually. Alternatively, a part can be automatically performed by a known method. In addition, the processing procedure, specific name, and information including various data and parameters shown in the above text and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown in the figure.

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 them may be functionally or physically distributed / physically in any unit according to various loads and usage conditions. Can be integrated and configured.

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

[5. effect〕
As described above, the information processing apparatus 10 according to the embodiment includes an acquisition unit 41, a classification unit 42, a calculation unit 43, a generation unit 44, a specific unit 45, a determination unit 46, and a provision unit 47. Has. The acquisition unit 41 acquires history information indicating the history of the search query input by the user group associated with the predetermined event. The classification unit 42 classifies the search query indicated by the history information acquired by the acquisition unit 41 into one of a plurality of predetermined categories. In addition, the classification unit 42 classifies the search query into any of a plurality of predetermined categories having a predetermined event. Further, the classification unit 42 classifies the search query into any of a plurality of predetermined categories having a hierarchical structure. Based on the history information, the calculation unit 43 calculates the degree of relevance between each search query classified into a predetermined category by the classification unit 42, a predetermined event, and the predetermined category. The generation unit 44 generates relevance information indicating the relevance degree of each search query calculated by the calculation unit 43 for each of a plurality of predetermined categories. The identification unit 45 identifies a user who is associated with a predetermined event and is associated with a predetermined category based on the search query input by the user and the relevance information. The determination unit 46 determines the degree to which the user is associated with a predetermined category based on the relevance indicated by the relevance information and the search query input by the user. The providing unit 47 provides information related to a predetermined category to the user specified by the specific unit 45. Further, the providing unit 47 provides the user with information related to the category associated with the user according to the degree of determination by the determination unit 46.

As a result, the information processing apparatus 10 according to the embodiment has a search query classified into a predetermined category among the search queries input by the user group associated with the predetermined event, and the predetermined event and the predetermined category. Since the degree of relevance can be calculated and the degree of relevance information for each category can be generated based on the degree of relevance, it is appropriate to grasp the relationship between the user group associated with a predetermined event and other events. Can provide information.

Further, in the information processing apparatus 10 according to the embodiment, for example, the specific unit 45 is associated with a first category included in a plurality of predetermined categories, and is included in a plurality of predetermined categories. Identify the user associated with. Further, the specifying unit 45 identifies a user associated with either a first category included in a plurality of predetermined categories or a second category included in a plurality of predetermined categories. Further, the specifying unit 45 identifies a user who is not associated with the first category included in the plurality of predetermined categories but is associated with a category other than the first category among the plurality of predetermined categories. Further, the specific unit 45 is not associated with the first category and the second category included in the plurality of predetermined categories, and is other than the first category and the second category among the plurality of predetermined categories. Identify the users associated with any of the categories. Then, the providing unit 47 provides the user with information according to the correspondence with the plurality of predetermined categories.

As a result, the information processing apparatus 10 according to the embodiment can identify a user having detailed characteristics by combining the identification results of the user by each model, and therefore, the effect of appealing the information provided to the user. Can be enhanced.

[6. Hardware configuration]
Further, the information processing apparatus 10 according to each of the above-described embodiments is realized by, for example, a computer 1000 having a configuration as shown in FIG. Hereinafter, the information processing apparatus 10 will be described as an example. FIG. 6 is a hardware configuration diagram showing an example of a computer that realizes the function of the information providing device. The computer 1000 has 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 depending 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 the communication network 500 (corresponding to the network N of the embodiment) and sends the data to the CPU 1100, and the communication interface 1500 receives data generated by the CPU 1100 via the communication network 500 to another device. Send to the device.

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 data generated via the input / output interface 1600 to the output device.

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 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 apparatus 10, the CPU 1100 of the computer 1000 realizes the function of the control unit 40 by executing a program loaded on the RAM 1200. Further, each data in the storage device of the information processing device 10 is stored in the HDD 1400. 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.

[7. others〕
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 carry out the present invention in other modified forms.

Further, the configuration of the above-mentioned information processing apparatus 10 can be flexibly changed, such as by calling an external platform or the like by API (Application Programming Interface), network computing, or the like depending on the function.

Further, the "part" described in the claims can be read as "means" or "circuit". For example, the acquisition unit can be read as an acquisition means or an acquisition circuit.

10 Information processing device 20 Communication unit 30 Storage unit 31 History information database 32 Relevance information database 40 Control unit 41 Acquisition unit 42 Classification unit 43 Calculation unit 44 Generation unit 45 Specific unit 46 Judgment unit 47 Providing unit 100 Terminal device

Claims (11)

An acquisition unit that acquires history information indicating the history of search queries entered by a group of users associated with a predetermined event, and an acquisition unit.
A classification unit that classifies the search query indicated by the history information acquired by the acquisition unit into one of a plurality of predetermined categories, and a classification unit.
Based on the history information, each search query classified into a predetermined category by the classification unit, a calculation unit for calculating the degree of association between the predetermined event and the predetermined category, and a calculation unit.
A generation unit that generates relevance information indicating the relevance degree of each search query calculated by the calculation unit for each of the plurality of predetermined categories, and a generation unit.
A specific unit that identifies a user associated with the predetermined event and associated with the predetermined category based on the search query entered by the user and the relevance information.
With a providing unit that provides information related to the predetermined category to the user specified by the specific unit.
A generator characterized by having. The classification unit
The generator according to claim 1, wherein the search query is classified into one of a plurality of the predetermined categories having the relation to the predetermined event. The classification unit
The generator according to claim 1 or 2, wherein the search query is classified into any of the plurality of predetermined categories having a hierarchical structure. The specific part is
A claim comprising identifying a user associated with a first category included in a plurality of the predetermined categories and associated with a second category included in the plurality of the predetermined categories. The generator according to any one of 1 to 3 . The specific part is
1. Claim 1 , wherein the user is associated with any one of the first category included in the plurality of predetermined categories or the second category included in the plurality of predetermined categories. The generator according to any one of 4 to 4 . The specific part is
A claim characterized by identifying a user who is not associated with a first category included in a plurality of the predetermined categories but is associated with a category other than the first category among the plurality of the predetermined categories. The generator according to any one of Items 1 to 5 . The specific part is
It is not associated with the first category and the second category included in the plurality of the predetermined categories, and is in any of the categories other than the first category and the second category among the plurality of the predetermined categories. The generator according to any one of claims 1 to 6 , wherein the user to be associated with the user is specified. The providing part
The generator according to any one of claims 1 to 7 , wherein the user is provided with information according to a correspondence relationship with the plurality of predetermined categories. Further, it has a determination unit for determining the degree to which the user is associated with the predetermined category based on the relevance degree indicated by the relevance information and the search query input by the user.
The providing part
The invention according to any one of claims 1 to 8 , wherein the information related to the category associated with the user is provided to the user according to the degree of determination by the determination unit. Generator. It ’s a computer-run generation method.
An acquisition process for acquiring history information indicating the history of search queries entered by a group of users associated with a predetermined event, and an acquisition process.
A classification process for classifying the search query indicated by the history information acquired by the acquisition process into one of a plurality of predetermined categories, and a classification process.
Based on the history information, each search query classified into a predetermined category by the classification step, a calculation step of calculating the degree of association between the predetermined event and the predetermined category, and a calculation step.
A generation step of generating relevance information indicating the relevance degree of each search query calculated by the calculation step for each of the plurality of predetermined categories, and a generation step.
A specific process for identifying a user who is associated with the predetermined event and is associated with the predetermined category based on the search query entered by the user and the relevance information.
With the providing process of providing information related to the predetermined category to the user specified by the specific process.
A generation method characterized by including. An acquisition procedure for acquiring history information indicating the history of search queries entered by a group of users associated with a predetermined event, and an acquisition procedure.
A classification procedure for classifying the search query indicated by the history information acquired by the acquisition procedure into one of a plurality of predetermined categories, and a classification procedure.
Based on the history information, each search query classified into a predetermined category by the classification procedure, a calculation procedure for calculating the degree of association between the predetermined event and the predetermined category, and a calculation procedure.
A generation procedure for generating relevance information indicating the relevance degree of each search query calculated by the calculation procedure for each of the plurality of predetermined categories, and a generation procedure.
A specific procedure for identifying a user who is associated with the predetermined event and is associated with the predetermined category based on the search query entered by the user and the relevance information.
With the provision procedure for providing information related to the predetermined category to the user specified by the specific procedure.
A generator to make a computer run.

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