JP2021149468A - Information processing apparatus, information processing method, and program - Google Patents

Abstract

To improve the accuracy of determining similarity between events.SOLUTION: An information processing apparatus includes an acquisition unit and a generation unit. The acquisition unit acquires information on a user having association with an event on a network. The generation unit generates characteristic information indicating characteristics of an event on the basis of the information acquired by the acquisition unit. The information processing apparatus further includes a specifying unit. The specifying unit specifies another event which is similar to an event to be determined, on the basis of similarity of characteristic information generated for each event.SELECTED DRAWING: Figure 1

Description

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

Conventionally, a technique for estimating the similarity between a plurality of objects has been known. As an example of such a technique, a technique for identifying a plurality of facilities that are similar to each other based on the similarity of the movement routes of users is known.

Japanese Patent Publication No. 2014-532353

However, with the above-mentioned techniques, it may not be possible to properly determine the similarity between events held in a region or facility.

For example, the above-mentioned technology merely identifies facilities with similar movement routes of users, and cannot be said to identify events with similar needs and tendencies of participating users, for example.

The present application has been made in view of the above, and an object of the present application is to improve the accuracy of determining similarity between events by appropriately identifying events.

The information processing apparatus according to the present application generates characteristic information indicating the characteristics of the event based on the acquisition unit that acquires information on the user's network related to the event and the information acquired by the acquisition unit. It is characterized by having a generating unit.

According to one aspect of the embodiment, there is an effect that the accuracy of determining the similarity between events can be improved.

FIG. 1 is a diagram showing an example of information processing according to an embodiment. FIG. 2 is a diagram showing a configuration example of the information processing device according to the embodiment. FIG. 3 is a diagram showing an example of an event database according to the embodiment. FIG. 4 is a diagram showing an example of a user database according to the embodiment. FIG. 5 is a diagram showing an example of a venue database according to the embodiment. FIG. 6 is a flowchart showing an information processing procedure according to the embodiment. FIG. 7 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device.

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

[1. An example of information processing]
First, an example of information processing according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of information processing according to an embodiment. In the example of FIG. 1, the information processing system 1 has an information processing device 10, a terminal device 100, and a log server 200. These various devices are connected so as to be communicable by wire or wirelessly via a network N (for example, the Internet). The information processing system 1 shown in FIG. 1 may include an arbitrary number of information processing devices 10, an arbitrary number of terminal devices 100, and an arbitrary number of log servers 200.

The information processing device 10 is an information processing device that provides information indicating a similar event similar to a designated event designated by the user. In the example shown in FIG. 1, the information processing device 10 receives an event designation from the user. Further, the information processing device 10 identifies a similar event similar to the designated event designated by the user. Then, the information processing device 10 provides the user with information indicating the specified similar event.

Specifically, the information processing device 10 is used for event attraction / holding planning and the like. For example, suppose that the business operator A1 successfully attracts and holds event # 1. In addition, since the business operator A1 has succeeded in attracting and holding the event # 1, it is assumed that the business operator A1 has a plan to attract and hold another event similar to the successful event # 1. In such a case, the information processing apparatus 10 receives the designation of event # 1 from the business operator A1. Further, the information processing device 10 identifies a similar event similar to the event # 1 designated by the business operator A1. Then, the information processing device 10 provides the user with information indicating the specified similar event. For example, the information processing device 10 identifies a plurality of similar events similar to event # 1. Then, the information processing device 10 provides the business operator A1 with a list showing the specified plurality of similar events.

The terminal device 100 is an information processing device used by the user. The terminal device 100 is, 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. The terminal device 100 transmits information regarding a designated event designated by the user to the information processing device 10 according to the operation of the user. Further, the terminal device 100 receives information about a similar event similar to the designated event from the information processing device 10. Then, the terminal device 100 displays the information regarding the received similar event on the screen of the terminal device 100. In the following, the terminal device 100 may be equated with the user. That is, in the following, the user can be read as the terminal device 100.

Further, in the following, the user specified by the user ID "U1" may be referred to as "user U1". As described above, in the following, when "user U * (* is an arbitrary numerical value)" is described, it means 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".

Further, in the following, the terminal device 100 will be described as the terminal devices 101 and 102 according to the user who uses the terminal device 100. For example, the terminal device 101 is a terminal device 100 used by the user U1. Further, for example, the terminal device 102 is a terminal device 100 used by the user U2. Further, in the following, when the terminal devices 101 and 102 will be described without particular distinction, they will be referred to as the terminal device 100.

The log server 200 is an information processing device that acquires various log data. Specifically, the log server 200 acquires log data indicating the behavior of the user on the network, which is related to each event. More specifically, the log server 200 acquires log data indicating the behavior of the users who participated in each event on the network.

From here, the flow of information processing will be described with reference to FIG. FIG. 1 is a diagram showing an example of information processing according to an embodiment. In the example shown in FIG. 1, the log server 200 acquires various log data (step S1). Specifically, the log server 200 acquires log data indicating the behavior of the users who participated in each event on the network. For example, the log server 200 acquires a position history, which is a history of the user's position, from the terminal device 100. Further, the log server 200 acquires the search history, which is the history of the search query input by the user, from the search server. Further, the log server 200 acquires a browsing history, which is a history of the contents browsed by the user, from the content server. Further, the log server 200 acquires a purchase history, which is a purchase history by the user, from the electronic commerce server. Further, the log server 200 acquires the posting history, which is the history of posting by the user, from the posting server or the SNS (Social Networking Service) server that provides the word-of-mouth posting service. The log server 200 can acquire various types of information in addition to the above-mentioned information. For example, the log server 200 acquires information on user attributes such as the user's age, gender, family structure, and financial status.

In addition, the log server 200 acquires information on the network regarding the venue where each event is held. For example, the log server 200 may include the name, location, average price (average customer unit price), and form (for example, meeting room / multipurpose hall / stadium / arena / outdoor stage) of the venue where each event is held. Obtain information on the large-scale hall / medium-scale hall / small-scale hall, etc.) and the surrounding environment (for example, nearby transportation / accommodation facilities / restaurants / sightseeing spots, etc.) from the terminal device of each venue. .. Further, the log server 200 can acquire various kinds of information in addition to the above-mentioned information. For example, the log server 200 acquires information about the venue such as the number of people accommodated in the venue, business hours, smoking in the venue, eating and drinking, toilets, roofed places, and parking lots.

In addition, the log server 200 acquires information on the network regarding each event. For example, the log server 200 acquires information about an event, which is information posted by a user who participated in each event, based on the acquired posting history. In addition, the log server 200 can be used for each event category, performers / competitors / groups, programs / competitions / exhibits, organizers, venues, venues, participation fees, holding times, holding periods, time zones, and the temperature of the day. , Get information on the network about each event such as weather. The categories of each event are, for example, categories such as sports competitions / entertainment / music / exhibitions / exhibitions / festivals, competitions such as the Olympics / World Cup / high school baseball / marathon, or These are categories such as soccer / baseball / tennis / basketball. In addition, entrance examinations and joint briefings are also included in the event.

The information processing device 10 acquires various log data from the log server 200 (step S2). Specifically, the information processing device 10 acquires information on the network related to each event from the log server 200. More specifically, the information processing device 10 acquires log data on the network indicating the behavior of the user who participated in each event from the log server 200. For example, the information processing device 10 acquires a position history, which is a history of the user's position, from the log server 200. Further, the information processing apparatus 10 acquires the search history, which is the history of the search query input by the user, from the log server 200. Further, the information processing device 10 acquires a browsing history, which is a history of the contents browsed by the user, from the log server 200. Further, the information processing apparatus 10 acquires a purchase history, which is a purchase history by the user, from the log server 200. Further, the information processing device 10 acquires the posting history, which is the history of posting by the user, from the log server 200. The information processing device 10 acquires various types of information in addition to the above-mentioned information. For example, the information processing device 10 acquires information on user attributes such as the user's age, gender, family structure, and financial status from the log server 200.

The information processing device 10 acquires information on the network regarding the venue where each event is held from the log server 200. For example, the information processing apparatus 10 also acquires information on the name, location, average price (average customer unit price), form, and surrounding environment of the venue where each event is held from the log server 200. The information processing device 10 acquires various types of information in addition to the above-mentioned information. For example, the information processing device 10 provides information about the venue such as the number of people accommodated in the venue, business hours, smoking in the venue, eating and drinking, toilets, roofed places, and parking lots from the log server 200. get.

The information processing device 10 acquires information on the network regarding each event from the log server 200. For example, the information processing device 10 acquires information about an event, which is information posted by a user who participated in each event, based on the posting history acquired from the log server 200. The information processing device 10 includes the category of each event, performers / competitors / groups, performances / competitions / exhibits, organizer, venue, venue, participation fee, holding time, holding period, time zone, temperature of the day, etc. Information on the network regarding each event such as weather is acquired from the log server 200.

Subsequently, when the information processing apparatus 10 acquires various log data, it identifies a user who is related to each event, and acquires the log data of the specified user (step S3). Specifically, the information processing device 10 identifies a user who has participated in each event based on the user's position history. Subsequently, the information processing device 10 acquires the log data of each user specified as the user who participated in each event.

For example, the information processing device 10 identifies the user # 1, the user # 2, the user # 3, ..., Who are the users who participated in the event # 1, based on the position history of the user. Subsequently, the information processing device 10 acquires the log data of the user # 1, the user # 2, the user # 3, ... Who are the users who participated in the event # 1. For example, the information processing device 10 acquires the search history of the user # 1, the browsing history of the user # 1, the purchase history of the user # 1, and so on as the log data of the user # 1. Further, the information processing device 10 acquires the search history of the user # 2, the browsing history of the user # 2, the purchase history of the user # 2, and so on as the log data of the user # 2. In this way, the information processing device 10 acquires the log data of each user who participated in the event # 1.

Subsequently, the information processing apparatus 10 calculates a multidimensional quantity (for example, a vector) indicating the context of each event based on the acquired log data and information about the venue where each event is held (step S4). .. Specifically, the information processing apparatus 10 converts the information on the network into a multidimensional quantity by using a model that converts the information into a multidimensional quantity based on the similarity between the information on the network. More specifically, the information processing apparatus 10 uses a model that converts the user's log data into a multidimensional amount based on the similarity of the acquired user's log data, and converts the user's log data into a vector. Convert. Further, the information processing device 10 holds each event using a model that converts information about the venue where each event is held into a multidimensional quantity based on the similarity of information about the venue where each event is held. Convert information about the venue into a vector. Subsequently, the information processing apparatus 10 calculates a multidimensional quantity indicating the context of each event based on the converted vector.

At this time, the information processing apparatus 10 uses a learning method such as a vector representation method (for example, Word2Vec) or a similar method to make the model learn the similarity between information on the network, and uses the trained model. , Information on the network may be converted into multidimensional quantities. Multidimensional quantities may be represented by distributed representation (word embedding).

Any type of model can be adopted as the model. For example, the information processing device 10 may use an SVM (Support Vector Machine) or a DNN (Deep Neural Network) as a model. Here, the DNN may be a CNN (Convolutional Neural Network) or an RNN (Recurrent Neural Network). Further, the RNN may be an LSTM (Long short-term memory) or the like. That is, any type of model can be adopted as the model. Further, the model may be a model realized by combining a plurality of models, for example, a model in which a CNN and an RNN are combined. In this way, the information processing apparatus 10 converts the information on the network into a multidimensional quantity by using a model having an arbitrary structure such as a regression model or a neural network.

For example, the information processing device 10 uses the model # 1 that converts the user's log data into a multidimensional amount based on the similarity of the acquired user's log data, and the user # 1 who participates in the event # 1. Convert the log data of to vector # 1.

Further, the information processing device 10 estimates the user's context based on the acquired user's log data. For example, the information processing device 10 estimates the behavioral context of a user's event as the user's context. In addition, the information processing device 10 estimates information about the user's interests as the user's context. In addition, the information processing device 10 calculates a score indicating the characteristics of the user based on the acquired log data of the user.

For example, the information processing device 10 estimates the context of the user # 1 who participated in the event # 1 based on the acquired log data of the user # 1. Further, the information processing apparatus 10 calculates a score indicating the characteristics of the user # 1 based on the acquired log data of the user # 1. Subsequently, the information processing device 10 converts the user's context and the score indicating the user's characteristics into a multidimensional quantity based on the similarity between the estimated user's context and the calculated score indicating the user's characteristics. Using the model # 2, the score indicating the context of the user # 1 who participated in the event # 1 and the characteristics of the user # 1 is converted into the vector # 2.

Further, the information processing apparatus 10 uses model # 3 for converting the venue where the event is held into a multidimensional quantity based on the similarity of the venue where the event is held, and vectorizes the venue where the event # 1 is held. Convert to # 3. Further, the information processing device 10 uses a model # 4 that converts the average usage amount of the venue where the event is held into a multidimensional amount based on the similarity of the average usage amount of the venue where the event is held, and uses the event # 4. Convert the average usage amount of the venue where 1 is held to vector # 4.

Subsequently, the information processing apparatus 10 calculates a vector # A1 indicating the context of the event # 1 based on the converted vectors # 1, vector # 2, vector # 3, vector # 4, .... Specifically, the information processing apparatus 10 converts a set of vectors into a multidimensional quantity by using a model that converts the set of vectors into a multidimensional quantity based on the similarity of the pair of transformed vectors. For example, the information processing apparatus 10 transforms a set of vectors (vector # 1, vector # 2, vector # 3, vector # 4, ...) Using a model that converts the set of vectors into a multidimensional quantity. The vector # A1 is calculated as the vector # A1 indicating the context of the event # 1.

Further, the information processing device 10 receives an event designation from the user U2 (step S5). Here, the user U2 is, for example, the person in charge of the business operator A1 who plans to attract and hold another event similar to the event in which the store has been successfully opened. Specifically, the information processing device 10 acquires information indicating a designated event designated by the user U2 from the terminal device 102.

Subsequently, when the information processing apparatus 10 acquires the information indicating the designated event, the information processing apparatus 10 identifies another event whose vector indicating the designated event and the context are similar (step S6). Specifically, the information processing device 10 specifies a vector #Q indicating the context of the designated event. Subsequently, the information processing apparatus 10 calculates the similarity between the specified vector #Q and the vector indicating the context of another event. Subsequently, the information processing apparatus 10 identifies a vector whose calculated similarity exceeds a predetermined threshold value. For example, the information processing apparatus 10 specifies a vector # A1 indicating the context of the event # 1, a vector # A2 indicating the context of the event # 2, ... As a vector whose calculated similarity exceeds a predetermined threshold value. Subsequently, the information processing apparatus 10 identifies an event corresponding to a vector whose similarity with the vector #Q indicating the context of the designated event exceeds a predetermined threshold value as a similar event similar to the designated event. For example, the information processing apparatus 10 specifies the event # 1 corresponding to the vector # A1, the event # 2, ... Corresponding to the vector # A2 as similar events.

Subsequently, when the information processing apparatus 10 identifies a similar event, the information processing apparatus 10 provides the user U2 with a list of the identified similar events (step S7). Specifically, the information processing device 10 transmits a list of events # 1, events # 2, ... Specified as similar events to the terminal device 102. More specifically, the information processing apparatus 10 generates a list in which events # 1, events # 2, ... Specified as similar events are arranged and displayed in order from the one with the highest degree of similarity. Subsequently, the information processing device 10 transmits the generated list to the terminal device 102.

As described above, the information processing apparatus 10 acquires information on the network related to each event. Further, the information processing device 10 identifies another event whose information on the network is similar to that of the target event to be determined. As a result, the information processing device 10 can identify an event whose needs and tendencies are similar to those of the target event to be determined. Therefore, the information processing device 10 can improve the accuracy of determining the similarity between events. Further, since the information processing device 10 can accurately determine an event having similar user needs and tendencies, it is possible to prevent a mismatch between the user needs and tendencies and the event, and to attract and hold an event of the business operator. Can be appropriately supported.

In the example shown in FIG. 1, an example in which the information processing device 10 receives one designated event from the user and provides a list of a plurality of similar events to the user has been described, but the present invention is not limited to this. .. Specifically, in step S5 of FIG. 1, the information processing device 10 receives the designation of a plurality of events from the user U2. The information processing device 10 acquires information indicating a plurality of designated events designated by the user U2 from the terminal device 102.

Subsequently, in step S6 of FIG. 1, when the information processing apparatus 10 acquires information indicating a plurality of designated events, the information processing apparatus 10 identifies another event in which the vector indicating the context is similar to the plurality of designated events. Specifically, the information processing device 10 specifies each context vector corresponding to a plurality of designated events. For example, the information processing apparatus 10 specifies vectors # Q1, vector # Q2, vectors # Q3, ... As each context vector corresponding to a plurality of designated events. Subsequently, the information processing apparatus 10 converts the vector set into a multidimensional quantity by using a model that converts the vector set into a multidimensional quantity based on the similarity of the vector set. For example, the information processing apparatus 10 converts a set of vectors (vector # Q1, vector # Q2, vector # Q3, ...) into a vector # V1 obtained by converting the set of vectors into a multidimensional quantity. , Calculated as vector # V1 indicating the context of a plurality of specified events.

Subsequently, the information processing apparatus 10 calculates the similarity between the calculated vector # V1 and the vector indicating the context of another event. Subsequently, the information processing apparatus 10 identifies a vector whose calculated similarity exceeds a predetermined threshold value. Subsequently, the information processing apparatus 10 identifies an event corresponding to a vector whose similarity with the vector # V1 indicating the context of the plurality of designated events exceeds a predetermined threshold value as a similar event similar to the plurality of designated events.

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

(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 between the terminal device 100 and the log server 200.

(Memory 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. Further, as shown in FIG. 2, the storage unit 30 has an event database 31, a user database 32, and a venue database 33.

(Event database 31)
The event database 31 stores various information related to the event. An example of the event database according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram showing an example of an event database according to the embodiment. In the example shown in FIG. 3, the event database 31 has items such as "event ID", "event name", "category", "venue", "venue ID", and "context vector".

The "event ID" indicates identification information for identifying each event. "Event name" indicates the name of each event. "Category" indicates the category of the venue. For example, the "category" may be a category of sports competitions / entertainment / music / exhibitions / exhibitions / festivals, or competitions such as the Olympics / World Cup / high school baseball / marathon. It may be in the category of. "Venue" indicates the location of the venue of the event. For example, the "venue" may be latitude or longitude information indicating a predetermined position of the venue of each event (for example, the center of the venue of the event), or the place name of the venue of each event (for example). , Prefecture name, ward, city, town, village name, etc.) information may be used. The "venue ID" indicates identification information for identifying the venue. The "context vector" indicates a multidimensional quantity indicating the context of each event.

The event database 31 is not limited to the above, and various information may be stored depending on the purpose. For example, the event database 31 stores information about each event such as performers / competitors / groups, performances / competitions / exhibits, organizers, holding time, holding period, time zone, temperature of the day, and weather of each event. You may.

(User database 32)
The user database 32 stores various information about the user. An example of the user database according to the embodiment will be described with reference to FIG. FIG. 4 is a diagram showing an example of a user database according to the embodiment. In the example shown in FIG. 4, the user database 32 has items such as "user ID", "location history", "search history", "browsing history", "purchasing history", and "posting history".

The "user ID" indicates identification information for identifying a user. "Position history" indicates the history of the user's position. The "search history" indicates the history of the search query entered by the user. The "browsing history" indicates the history of the content browsed by the user. “Purchase history” indicates the history of purchases made by users. "Posting history" indicates the history of postings by users.

The user database 32 is not limited to the above, and various information may be stored depending on the purpose. For example, the user database 32 may store information about user attributes such as age, gender, family structure, and financial status of the user. Further, the user database 32 relates to various attributes such as user demographic (demographic attribute), psychographic (psychological attribute), geographic (geographical attribute), and behavioral (behavioral attribute). Information may be stored.

(Venue database 33)
The venue database 33 stores various information about the venue. An example of the venue database according to the embodiment will be described with reference to FIG. FIG. 5 is a diagram showing an example of a venue database according to the embodiment. In the example shown in FIG. 5, the venue database 33 has items such as "venue ID", "venue name", "location", "average price", "form", and "surrounding environment".

The "venue ID" indicates identification information for identifying the venue. "Venue name" indicates the name of the venue. "Location" indicates a specific place name or address of the location of the venue. The "average price" indicates the average usage amount of the users who visited the venue. For example, the "average price" may be the entrance fee of the venue, the participation fee of the event, etc., and is the amount including the average purchase price of products at restaurants, goods sales offices, etc. in the venue. You may. "Form" indicates the form of the venue. For example, the "form" may be a venue such as a conference room / multipurpose hall / stadium / arena / outdoor stage, or a large-scale hall / medium-scale hall / small-scale hall or the like. May be good. "Peripheral environment" indicates the surrounding environment of the venue. For example, the "surrounding environment" may be information on nearby transportation facilities / accommodation facilities / restaurants / sightseeing spots and the like.

The venue database 33 is not limited to the above, and various information may be stored depending on the purpose. For example, the venue database 33 may store information about the venue such as the number of people accommodated in the venue, business hours, smoking in the venue, eating and drinking, toilets, roofed places, and parking lots. ..

(Control unit 40)
The control unit 40 is realized by, for example, using a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like to execute various programs stored in the storage device inside the information processing device 10 using the RAM as a work area. Will be done. Further, the control unit 40 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 includes an acquisition unit 41, an estimation unit 42, a calculation unit 43, a conversion unit 44, a specific unit 45, a reception unit 46, and a provision unit 47. Realize or execute the information processing functions and actions described in. The internal configuration of the control unit 40 is not limited to the configuration shown in FIG. 2, and may be another configuration as long as it is a configuration for performing information processing described later.

(Acquisition unit 41)
The acquisition unit 41 acquires various log data from the log server 200. The acquisition unit 41 acquires information on the network related to each event. Specifically, the acquisition unit 41 acquires information on the network regarding each event from the log server 200. For example, the acquisition department 41 has a category of each event, performers / competitors / groups, performances / competitions / exhibits, organizers, venues, venues, participation fees, holding times, holding periods, time zones, and temperature on the day. , The information on the network regarding each event such as the weather is acquired from the log server 200. Subsequently, the acquisition unit 41 stores the acquired information on the network regarding each event in the event database 31 in association with the event ID.

In addition, the acquisition unit 41 acquires information on the network indicating the behavior of the user related to each event. The acquisition unit 41 acquires information on the network indicating the behavior of the user related to each event from the log server 200. The acquisition unit 41 acquires information on the network indicating the behavior of the user who participated in each event. For example, the acquisition unit 41 acquires a position history, which is a history of the user's position. In addition, the acquisition unit 41 acquires the search history, which is the history of the search query input by the user. In addition, the acquisition unit 41 acquires the browsing history, which is the history of the content browsed by the user. In addition, the acquisition unit 41 acquires the purchase history, which is the purchase history by the user. In addition, the acquisition unit 41 acquires the posting history, which is the history of posting by the user. Subsequently, when the acquisition unit 41 acquires information on the network indicating the user's behavior, the acquisition unit 41 stores the acquired information in the user database 32 in association with the user ID. The acquisition unit 41 acquires various types of information in addition to the above-mentioned information. For example, the acquisition unit 41 acquires information on user attributes such as the user's age, gender, family structure, and financial status from the log server 200. Subsequently, when the acquisition unit 41 acquires information on the user's attributes from the log server 200, the acquisition unit 41 stores the acquired information in the user database 32 in association with the user ID.

In addition, the acquisition unit 41 acquires information on the network regarding the venue where each event is held. The acquisition unit 41 acquires information on the network regarding the venue where each event is held from the log server 200. For example, the acquisition unit 41 acquires information on the network regarding users who have visited the venue where each event is held. The users who visited the venue are not limited to those who entered the venue, but also include users who were around the venue at the time of the event but could not enter the venue because they could not obtain admission tickets, for example. .. In addition, users who visited the venue before the event for preview, users who visited the venue after the event to immerse themselves in the afterglow, and the like may be included. For example, the acquisition unit 41 acquires information about the venue, which is information posted by a user who visits the venue where each event is held. The acquisition unit 41 acquires information about the venue, which is information posted by a user who visited the venue where each event was held, based on the posting history acquired from the log server 200. In addition, the acquisition unit 41 acquires information on the name, location, average price (average customer unit price), form, and surrounding environment of the venue where each event is held from the log server 200. The acquisition unit 41 acquires various types of information in addition to the above-mentioned information. For example, the acquisition unit 41 acquires information about the venue from the log server 200, such as the number of people in the venue, business hours, smoking at the venue, eating and drinking, toilets, roofed places, and parking lots. do. Subsequently, when the acquisition unit 41 acquires information on the network regarding the venue where each event is held, the acquisition unit 41 stores the acquired information in the venue database 33 in association with the venue ID.

In addition, the acquisition unit 41 acquires information on the network indicating the behavior of the user who participated in each event. In addition, the acquisition unit 41 identifies the user based on the position information acquired from the terminal device used by the user, and acquires information on the network indicating the behavior of the identified user. Specifically, when the acquisition unit 41 acquires various log data, it identifies a user who is related to each event, and acquires the log data of the specified user. For example, the acquisition unit 41 refers to the location history of the user database 32 and identifies the users who participated in each event based on the location history of the users. In addition, the acquisition unit 41 identifies the user who participated in each event based on the position history of the user. Subsequently, when the user is specified, the acquisition unit 41 refers to the user database 32 and acquires the log data of the specified user.

In the example shown in FIG. 1, the acquisition unit 41 refers to the position history of the user database 32, and based on the position history of the user, the user # 1 and the user who are the users who participated in the event # 1 Identify # 2, user # 3, ... Subsequently, when the user is specified, the acquisition unit 41 refers to the user database 32 and acquires the log data of the specified user. For example, the acquisition unit 41 refers to the user database 32 and logs data of the user # 1, such as the search history of the user # 1, the browsing history of the user # 1, the purchase history of the user # 1, and so on. To get. In addition, the acquisition unit 41 refers to the user database 32 and acquires information on user attributes such as age, gender, family structure, and financial status of user # 1. Further, the acquisition unit 41 refers to the user database 32 and logs data of the user # 2, such as the search history of the user # 2, the browsing history of the user # 2, the purchase history of the user # 2, and so on. To get. In addition, the acquisition unit 41 refers to the user database 32 and acquires information on user attributes such as age, gender, family structure, and financial status of user # 2. In this way, the acquisition unit 41 acquires the log data of each user who participated in the event # 1. In addition, the acquisition unit 41 acquires information on the attributes of each user who participated in the event # 1.

(Estimation unit 42)
The estimation unit 42 estimates information about the user based on the information acquired by the acquisition unit 41. For example, the estimation unit 42 estimates the information about the user by using a model that estimates the information about the user based on the information acquired by the acquisition unit 41. In addition, the estimation unit 42 estimates the context of the action in the user's event. For example, the estimation unit 42 may estimate the behavior context in the user event by using a model that estimates the behavior context in the user event based on the information acquired by the acquisition unit 41.

Further, the estimation unit 42 may estimate the type of the user's behavior based on the user's position history acquired by the acquisition unit 41. For example, the estimation unit 42 estimates the visit location of the user based on the position history of the user acquired by the acquisition unit 41. Subsequently, the estimation unit 42 estimates the user's behavior based on the estimated visit location. For example, the estimation unit 42 estimates that the user has visited the event venue when the estimated visit location is the event venue. Further, the estimation unit 42 may estimate the user's behavior based on the purchase history of the user acquired by the acquisition unit 41. For example, the estimation unit 42 identifies the product or service purchased by the user based on the purchase history of the user acquired by the acquisition unit 41. For example, the estimation unit 42 estimates the user's behavior based on the comparison between the date and time of the user's position history and the date and time of the purchase history. For example, the estimation unit 42 uses the product when the same user purchases a product in the event venue based on the purchase history at the date and time when the user is estimated to have visited the event venue based on the location history. It may be presumed that the person was attending the event.

Further, the estimation unit 42 may calculate the moving speed of the user based on the position history of the user acquired by the acquisition unit 41. Subsequently, the estimation unit 42 may estimate the user's moving means based on the calculated moving speed. For example, the estimation unit 42 estimates that the user is walking when the calculated moving speed is a predetermined speed per hour. Further, the estimation unit 42 estimates that the user has boarded a car or a railroad when the calculated moving speed exceeds a predetermined speed per hour. For example, when the calculated moving speed exceeds a predetermined speed, the estimation unit 42 estimates that the user has boarded a car if the user's position is on the road, and the user's position is on the railroad track. For example, it is estimated that the user got on the railroad.

In addition, the estimation unit 42 estimates information regarding the interests of the user. For example, the estimation unit 42 estimates the information on the user's interest based on the information acquired by the acquisition unit 41 using a model for estimating the information on the user's interest.

Further, the estimation unit 42 may estimate the user's field of interest based on the search history of the user acquired by the acquisition unit 41. For example, the estimation unit 42 is used when the user has searched for a search query related to the economic field more than a predetermined number of times within a predetermined period based on the search history of the user acquired by the acquisition unit 41. It is presumed that the field of interest of the person is the economic field.

Further, the estimation unit 42 may estimate the user's field of interest based on the browsing history of the user acquired by the acquisition unit 41. For example, if the user has browsed the content related to overseas travel more than a predetermined number of times within a predetermined period based on the browsing history of the user acquired by the acquisition unit 41, the estimation unit 42 may use the user. Is presumed to be interested in traveling abroad.

In the example shown in FIG. 1, the estimation unit 42 estimates the context of the user # 1 who participated in the event # 1 based on the log data of the user # 1 acquired by the acquisition unit 41. For example, the estimation unit 42 estimates the context of the action of the user # 1 in the event # 1. In addition, the estimation unit 42 estimates information regarding the interest of user # 1.

(Calculation unit 43)
The calculation unit 43 calculates a score indicating the characteristics of the user based on the information acquired by the acquisition unit 41. For example, the estimation unit 42 calculates a score indicating the characteristics of the user by using a model for calculating a score indicating the characteristics of the user based on the information acquired by the acquisition unit 41. In the example shown in FIG. 1, the calculation unit 43 uses a model for calculating a score indicating the characteristics of the user, and based on the acquired log data of the user # 1, obtains a score indicating the characteristics of the user # 1. calculate.

(Conversion unit 44)
The conversion unit 44 calculates a multidimensional quantity (for example, a vector) indicating the context of each event based on the log data of each user acquired by the acquisition unit 41 and the information about the venue where each event is held. do. Specifically, the conversion unit 44 converts the information on the network into a multidimensional quantity by using a model that converts the information into a multidimensional quantity based on the similarity between the information on the network. When the conversion unit 44 calculates a multidimensional quantity (hereinafter, referred to as “context vector”) indicating the context of each event, the conversion unit 44 stores the calculated context vector in association with the event ID in the event database 31.

More specifically, the conversion unit 44 uses a model # 1 that converts the user's log data into a multidimensional amount based on the similarity of the user's log data acquired by the acquisition unit 41, and uses the user. Convert the log data of. In the example shown in FIG. 1, the conversion unit 44 participates in the event # 1 by using the model # 1 that converts the user's log data into a multidimensional quantity based on the similarity of the user's log data. Convert the log data of # 1 to vector # 1. More specifically, the conversion unit 44 of the user # 1 such as the search history of the user # 1, the browsing history of the user # 1, the purchasing history of the user # 1, the posting history of the user # 1, and so on. Convert log data to vector # 1. In addition to the log data described above, the conversion unit 44 may convert the log data of the user into a vector based on the similarity of various log data. Further, in the above-mentioned example, the conversion unit 44 combines a plurality of log data into one vector # 1 based on the similarity of a plurality of log data such as a user's search history, browsing history, purchase history, and so on. An example of conversion has been described, but it is not limited to this. For example, the conversion unit 44 uses each model that converts each user's log data into each vector based on the similarity of each user's log data such as search history, browsing history, purchase history, posting history, and so on. , Each user's log data may be converted into a separate vector.

Further, the conversion unit 44 converts the information on the user's attribute into a vector by using the model # 11 that converts the information on the user's attribute into a multidimensional quantity based on the similarity of the information on the user's attribute. .. For example, the conversion unit 44 uses model # 11 to convert information about user attributes into a multidimensional quantity based on the similarity of information about user attributes such as age, gender, family structure, and financial situation, and uses an event. The information about the attribute of the user # 1 who participated in # 1 is converted into the vector # 11. In addition, the conversion unit 44 increases the number of user information based on the similarity of the user information based on the similarity of the user information, which is the information obtained by combining the information related to the user's attributes and the log data of the user. The user information of the user # 1 who participated in the event # 1 may be converted into the vector # 12 by using the model # 12 which converts the dimension quantity.

Further, the conversion unit 44 shows the user's context and the user's characteristics based on the similarity between the user's context estimated by the estimation unit 42 and the score indicating the user's characteristics calculated by the calculation unit 43. Using model # 2, which converts the score into a multidimensional quantity, the score indicating the user's context and the user's characteristics is converted into a vector. In the example shown in FIG. 1, the conversion unit 44 of the user # 1 estimated by the estimation unit 42 using the model # 2 that converts the score indicating the user's context and the user's characteristics into a multidimensional quantity. The score indicating the characteristics of the user # 1 calculated by the context and the calculation unit 43 is converted into the vector # 2.

In addition, the conversion unit 44 uses a model that converts information about the venue where each event is held into a multidimensional quantity based on the similarity of information about the venue where each event is held, and uses a model to convert the information about the venue where each event is held into a multidimensional quantity. Convert information about to a vector. For example, the conversion unit 44 uses model # 3 to convert the venue where the event is held into a multidimensional quantity based on the similarity of the venue where the event is held, and vector # 3 the venue where the event # 1 is held. Convert to 3. Further, the conversion unit 44 uses the model # 4 that converts the average usage amount of the venue where the event is held into a multidimensional amount based on the similarity of the average usage amount of the venue where the event is held, and uses the event # 1 Convert the average usage amount of the venue where the event is held to vector # 4. In addition, the conversion unit 44 uses model # 5 to convert the business hours of the venue where the event is held into a multidimensional quantity based on the similarity of the business hours of the venue, and the business of the venue where the event # 1 is held. Convert time to vector # 5. In addition, the conversion unit 44 uses a model # 6 that converts the possibility of smoking in the venue where the event is held into a multidimensional quantity based on the similarity of the possibility of smoking in the venue, and the conversion unit 44 uses the model # 6 in which the event # 1 is held. The possibility of smoking in is converted into vector # 6. In addition to the above-mentioned information on the venue where each event is held, the conversion unit 44 provides information on the venue where each event is held based on the similarity of the information on the venue where each event is held. It may be converted to a vector. The conversion unit 44 includes the venue where the event is held, the average usage amount of the venue, the capacity of the venue, business hours, whether smoking is allowed at the venue, whether eating and drinking is possible, whether there is a toilet, whether there is a roofed place, and parking. Based on the similarity of a plurality of information about the venue such as the presence or absence of a parking lot, a plurality of information about the venue may be collectively converted into one vector.

Further, the conversion unit 44 converts the information on the network regarding each event into a vector by using a model that converts the information on the network regarding each event into a multidimensional quantity based on the similarity of the information on the network regarding each event. do. For example, the conversion unit 44 has a category of each event, performers / competitors / groups, performances / competitions / exhibits, organizers, venues, venues, participation fees, holding times, holding periods, time zones, and temperature of the day. Convert each information on the network for each event into a multidimensional quantity based on the similarity of each information on the network for each event, such as weather, to each vector for each information on the network for each event. Convert. In addition, the conversion unit 44 describes the category of each event, performers / competitors / groups, performances / competitions / exhibits, organizers, venues, venues, participation fees, holding times, holding periods, time zones, and temperature on the day. , A plurality of information on the network regarding each event may be collectively converted into one vector based on the similarity of a plurality of information on the network regarding each event such as weather.

Subsequently, the conversion unit 44 calculates a multidimensional quantity indicating the context of each event based on the vector converted by each model. For example, the conversion unit 44 converts a set of vectors into a multidimensional quantity by using a model that converts the set of vectors into a multidimensional quantity based on the similarity of the pair of vectors converted by each model. In the example shown in FIG. 1, the conversion unit 44 multidimensionalizes a set of vectors converted by each model (vector # 1, vector # 2, vector # 3, vector # 4, ...). Using a model that transforms into a quantity, it transforms into a vector # A1 that indicates the context of event # 1.

In the above example, the conversion unit 44 converts the log data of each user acquired by the acquisition unit 41, the information about the venue where each event is held, and the information on the network regarding each event into each vector. An example of calculating the context vector of each event based on each converted vector after converting to is described, but the present invention is not limited to this. Specifically, the conversion unit 44 determines each event based on the log data of each user acquired by the acquisition unit 41, the information about the venue where each event is held, and the information on the network regarding each event. Calculate the context vector of. For example, the conversion unit 44 uses log data such as search history, browsing history, purchase history, etc. of each user acquired by the acquisition unit 41, age, gender, family structure, economic status of each user, and the like. Information about the attributes of each event, the venue where each event is held, the average usage amount of the venue, the capacity of the venue, business hours, whether smoking at the venue, eating and drinking, whether there is a toilet, whether there is a roofed place, parking Information about the venue such as the presence or absence of a parking lot, each event category, performers / competitors / groups, performances / competitions / exhibits, organizers, venues, venues, participation fees, holding times, holding periods, time zones, on the day By converting each of these information into a multidimensional quantity using a model that transforms each of these information into a multidimensional quantity, based on the similarity of each information on the network for each event such as temperature, weather, etc. Calculate the event context vector.

(Specific part 45)
The identification unit 45 identifies another event whose information on the network is similar to that of the target event to be determined. Specifically, when the reception unit 46 receives the designated event, the specific unit 45 identifies another event whose vector indicating the context is similar to that of the designated event. More specifically, the identification unit 45 identifies another event whose information on the network is similar to that of the target event, based on the similarity of the multidimensional quantity. For example, the identification unit 45 identifies another event whose behavior on the user's network is similar to that of the target event. In addition, the identification unit 45 identifies another event in which the information about the user estimated by the estimation unit 42 is similar to the target event. In addition, the specific unit 45 identifies another event whose score tendency calculated by the calculation unit 43 is similar to that of the target event. In addition, the identification unit 45 identifies other events whose information on the network regarding the venue is similar to the target event. In addition, the identification unit 45 identifies a plurality of other events whose information on the network is similar to that of the target event.

In the example shown in FIG. 1, when the reception unit 46 receives the designated event, the specific unit 45 identifies another event whose vector indicating the context is similar to that of the designated event. Specifically, the specific unit 45 specifies a vector # Q indicating the context of the designated event received by the reception unit 46. Subsequently, the identification unit 45 calculates the similarity between the specified vector #Q and the vector indicating the context of another event. Subsequently, the identification unit 45 identifies a vector whose calculated similarity exceeds a predetermined threshold value. For example, the identification unit 45 specifies a vector # A1 indicating the context of the event # 1, a vector # A2 indicating the context of the event # 2, ... As a vector whose calculated similarity exceeds a predetermined threshold value. Subsequently, the specifying unit 45 identifies an event corresponding to a vector whose similarity with the vector #Q indicating the context of the designated event exceeds a predetermined threshold value as a similar event similar to the designated event. For example, the identification unit 45 identifies a plurality of similar events such as the event # 1 corresponding to the vector # A1, the event # 2, the event # 2 corresponding to the vector # A2, and the like.

In the example shown in FIG. 1, an example in which the information processing device 10 receives one designated event from the user and provides a list of a plurality of similar events to the user has been described, but the present invention is not limited to this. .. Specifically, the reception unit 46 accepts the designation of a plurality of events from the user. The reception unit 46 acquires information indicating a plurality of designated events designated by the user from the terminal device 100.

Subsequently, when the reception unit 46 receives a plurality of designated events, the specific unit 45 identifies other events having similar vectors to the plurality of designated events. Specifically, the identification unit 45 specifies each context vector corresponding to a plurality of designated events. For example, the specific unit 45 specifies the vector # Q1, the vector # Q2, the vector # Q3, ... As each context vector corresponding to a plurality of designated events. Subsequently, the specific unit 45 converts the set of vectors into a multidimensional quantity by using a model that transforms the set of vectors into a multidimensional quantity based on the similarity of the set of vectors. For example, the specific unit 45 converts a set of vectors (vector # Q1, vector # Q2, vector # Q3, ...) into a vector # V1 obtained by converting the set of vectors into a multidimensional quantity. Calculated as vector # V1 indicating the context of a plurality of specified events.

Subsequently, the identification unit 45 calculates the similarity between the calculated vector # V1 and the vector indicating the context of another event. Subsequently, the identification unit 45 identifies a vector whose calculated similarity exceeds a predetermined threshold value. Subsequently, the identification unit 45 identifies an event corresponding to a vector whose similarity with the vector # V1 indicating the context of the plurality of designated events exceeds a predetermined threshold value as a similar event similar to the plurality of designated events.

(Reception Department 46)
The reception unit 46 accepts the designation of the target event to be determined from the user. Specifically, the reception unit 46 acquires information indicating a designated event designated as a target event to be determined by the user from the terminal device 100. In the example shown in FIG. 1, the reception unit 46 receives the event designation from the user U2. Specifically, the reception unit 46 acquires information indicating a designated event designated by the user U2 from the terminal device 102.

(Providing Department 47)
The providing unit 47 provides the user with information indicating another event specified by the specific unit 45. For example, when a similar event is specified by the specific unit 45, the providing unit 47 transmits a list of the specified similar events to the terminal device 100. For example, the providing unit 47 generates a list in which a plurality of specified similar events are arranged and displayed in order from the one having the highest degree of similarity. Subsequently, the providing unit 47 transmits the generated list to the terminal device 100. In the example shown in FIG. 1, when the providing unit 47 identifies a similar event, the providing unit 47 provides the user U2 with a list of the identified similar events. Specifically, the providing unit 47 transmits a list of a plurality of similar events such as event # 1, event # 2, ... Specified as similar events to the terminal device 102. More specifically, the providing unit 47 generates a list in which events # 1, events # 2, ... Specified as similar events are arranged in order from the one with the highest degree of similarity. Subsequently, the providing unit 47 transmits the generated list to the terminal device 102.

[3. Information processing flow]
Next, the procedure of information processing according to the embodiment will be described with reference to FIG. FIG. 6 is a flowchart showing an information processing procedure according to the embodiment. In the example shown in FIG. 6, the information processing apparatus 10 determines whether or not the timing is predetermined (step S101). When the information processing apparatus 10 determines that the timing is not the predetermined timing (step S101; No), the information processing device 10 waits until the predetermined timing comes.

On the other hand, when the information processing apparatus 10 determines that the timing is predetermined (step S101; Yes), the information processing device 10 acquires log data on the network indicating the user's behavior related to each event (step S102). Subsequently, when the information processing apparatus 10 acquires the log data, the information processing apparatus 10 generates a context vector reflecting the similarity between the acquired log data for each event (step S103).

Subsequently, the information processing device 10 determines whether or not the designated event has been received from the user (step S104). When the information processing apparatus 10 determines that the designated event has not been received from the user (step S104; No), the information processing device 10 waits until the designated event is received from the user.

On the other hand, when the information processing apparatus 10 determines that the designated event has been received from the user (step S104; Yes), the information processing device 10 specifies the context vector of the designated event (step S105). Subsequently, when the information processing apparatus 10 specifies the context vector of the designated event, the information processing apparatus 10 identifies a similar event whose context vector is similar to the designated event (step S106). Subsequently, the information processing device 10 outputs information indicating the specified similar event (step S107).

[4. Modification example]
The information processing device 10 described above may be implemented in various different forms other than the above-described embodiment. Therefore, a modified example of the embodiment will be described below.

In the above embodiment, the information processing apparatus 10 specifies a similar event whose context vector is similar to the designated event, but a derived event derived from the designated event may be regarded as a kind of similar event. For example, before and after an event, related events related to the event (for example, eve / after-night festival, cheering event, etc.) may be held. In addition, a user who participates in a certain event may meet or kill time at a nearby coffee shop or fast food restaurant before the event, or head to a restaurant or bar in the area after the event. In addition, a user who participates in a local event may visit famous places and famous facilities in the region or use a bathing facility such as a hot spring in the region while participating in the event. Such a derived event can be another event whose context vector is similar to that of the target event. In addition, by setting the derived event that occurs accompanying the specified event as a similar event, information about the derived event that occurs before and after the event and related facilities, etc. can be provided to users who are planning to participate in the event. May be provided (introduction / suggestion / recommendation).

In addition, when providing information on similar events similar to the event to users who participated in a certain event, information on derivative events that occur before and after the similar event and related facilities is also provided. You may. In addition, users who are planning to participate in a certain event may be provided with information on sightseeing spots, facilities, etc. that the participants stopped by at the time of a similar event in the past similar to the event. Further, a user who is scheduled to participate in a certain event may be provided with information on a secondary derivative event that occurs secondarily from a derivative event that occurs before and after the event, a facility related thereto, and the like.

Further, in the above embodiment, the information processing apparatus 10 calculates a multidimensional amount indicating the context of each event based on the log data of each user and the information about the venue where each event is held. However, a multidimensional quantity indicating the context of each event may be calculated based on information about the area where each event is held. In the case of community-based events, such as festival events and other local events, information about the area where each event is held also affects the multidimensional amount that indicates the context of each event. It is presumed. Note that the log data of each user and the information about the venue where each event is held may include information about the area where each event is held.

Further, in the above embodiment, the information processing apparatus 10 determines the similarity between events, but is not limited to between events, but is a keyword indicating the characteristics of the event and the area where the event is held. The similarity of the above may be determined. As a result, the information processing device 10 can accurately determine keywords indicating tourism resources whose needs and tendencies are similar to those of the target event, and thus appropriately supports tourism and the like accompanying the participation of the user in the event. be able to. For example, due to the similarity between an event held in a certain area and a specific facility in the area, the user can be guided to the specific facility after the event ends.

Further, in the above embodiment, the information processing apparatus 10 determines the similarity between events, but may determine the similarity between keywords related to various matters that can be a feature of the area. That is, the similarity between keywords indicating the characteristics of a certain area may be determined. Specifically, the information on the network related to each keyword indicating the characteristics of the area is acquired. Then, another keyword whose information on the network is similar to the target keyword to be determined is specified. For example, it is possible to determine the similarity between keywords related to items that have the ability to attract customers in a certain area and other items. The event held in that area is also one of the features of the area.

[5. effect〕
As described above, the information processing apparatus 10 according to the embodiment has an acquisition unit 41 and a generation unit. The acquisition unit 41 acquires information on the user's network that is related to the event. The generation unit generates feature information indicating the characteristics of the event based on the information acquired by the acquisition unit. The generation unit corresponds to the conversion unit 44 in the above embodiment. Further, the feature information indicating the feature of the event is, for example, a multidimensional quantity (for example, a vector) indicating the context of the event.

As a result, the information processing apparatus 10 can appropriately identify the events, and as a result, the accuracy of determining the similarity between the events can be improved. Specifically, the similarity between events can be determined based on the similarity of information on the user's network that is related to the event.

Further, the information processing apparatus according to the embodiment further includes a specific unit 45. The identification unit 45 identifies another event similar to the target event to be determined based on the information acquired by the acquisition unit 41.

As a result, the information processing device 10 can identify an event whose needs and tendencies are similar to those of the target event to be determined. Therefore, the information processing device 10 can improve the accuracy of determining the similarity between events. Further, since the information processing device 10 can accurately determine events having similar user needs and tendencies, it is possible to appropriately support the event attraction / holding plan of the business operator.

In addition, the acquisition unit 41 acquires information indicating the behavior of the users who participated in each event on the network. In addition, the acquisition unit 41 identifies the user located at a position related to the event based on the position information acquired from the terminal device used by the user, and indicates the behavior of the identified user on the network. Get information.

As a result, the information processing device 10 can determine the similarity between the events based on the similarity of the information indicating the behavior of the users who participated in each event on the network.

Further, the acquisition unit 41 identifies the user who has input the search query related to the event based on the history of the search query input by the user, and information on the network indicating the behavior of the specified user. To get.

Thereby, the information processing apparatus 10 can determine the similarity between the events based on the similarity of the search history of the users who are related to each event.

In addition, the acquisition unit 41 identifies the user who has viewed the content related to the event based on the history of the content viewed by the user, and acquires information on the network indicating the behavior of the specified user. do.

Thereby, the information processing apparatus 10 can determine the similarity between the events based on the similarity of the browsing history of the users who are related to each event.

In addition, the acquisition unit 41 identifies the user who purchased the transaction target related to the event based on the purchase history by the user, and acquires information on the network indicating the behavior of the specified user. ..

Thereby, the information processing apparatus 10 can determine the similarity between the events based on the similarity of the purchase history of the users who are related to each event.

Further, the information processing device 10 according to the embodiment has an estimation unit 42. The estimation unit 42 estimates information about the user based on the information acquired by the acquisition unit 41. The generation unit generates feature information indicating the characteristics of the event based on the information estimated by the estimation unit 42.

Thereby, the information processing apparatus 10 can determine the similarity between the events based on the similarity of the estimated information about the users who are related to each event.

In addition, the estimation unit 42 estimates the context of the action in the user's event.

Thereby, the information processing apparatus 10 can determine the similarity between the events based on the similarity of the behavioral context in the user's event which is related to each event.

In addition, the estimation unit 42 estimates information regarding the interests of the user.

Thereby, the information processing apparatus 10 can determine the similarity between the events based on the similarity of the information regarding the interests of the users who are related to each event.

Further, the information processing device 10 according to the embodiment has a calculation unit 43. The calculation unit 43 calculates a score indicating the characteristics of the user based on the information acquired by the acquisition unit 41. The generation unit generates feature information indicating the features of the event based on the score calculated by the calculation unit 43.

Thereby, the information processing apparatus 10 can determine the similarity between the events based on the similarity of the scores showing the characteristics related to each event.

In addition, the acquisition unit 41 acquires information on the user's network related to the venue where each event is held. The generation unit generates characteristic information indicating the characteristics of the event held at the venue based on the information on the user's network related to the venue.

Thereby, the information processing apparatus 10 can determine the similarity between the events based on the similarity of the information on the network regarding the venue where each event is held.

In addition, the acquisition unit 41 acquires information on the network regarding users who have visited the venue where each event was held.

Thereby, the information processing apparatus 10 can determine the similarity between the events based on the similarity of the information on the network regarding the users who visited the venue where each event was held.

In addition, the acquisition unit 41 acquires information posted by users who participated in each event and is related to at least one of each event and the venue.

As a result, the information processing device 10 has similarities between events based on the similarity of information regarding at least one of each event and the venue among the information posted by the users who visited the venue where each event was held. Can be determined.

Further, the acquisition unit 41 further acquires information on the network related to each event, and the generation unit is based on the information of the user related to each event and the information on the network related to each event. Then, feature information indicating the features of each event is generated.

As a result, the information processing device 10 can be used for each event category, performer / competitor / group, program / competition / exhibit, organizer, venue, venue, participation fee, holding time, holding period, time zone, and day. You can get information on the network about each event such as temperature and weather.

The generation unit converts the information on the network into a multidimensional quantity by using a model that converts the information into a multidimensional quantity based on the similarity between the information on the network acquired by the acquisition unit 41.

As a result, the information processing apparatus 10 can objectively determine the similarity between events based on the similarity of the vectors showing the characteristics of each event.

Further, the information processing device 10 according to the embodiment has a reception unit 46 and a provision unit 47. The reception unit 46 accepts the designation of the target event to be determined from the user. The providing unit 47 provides the user with information indicating another event specified by the specific unit 45.

As a result, the information processing device 10 can improve the usability of the service for providing similar events.

In addition, the providing unit 47 provides the user with information on a derived event that occurs in association with another event.

As a result, when introducing a similar event to the user, the information processing device 10 provides the user with a related event that occurs before and after the similar event, a facility for breaks and sightseeing performed before and after the similar event, and the like. Can be introduced appropriately.

From another viewpoint, the information processing apparatus according to the embodiment includes an acquisition unit 41, a generation unit, and a specific unit 45. The generation unit corresponds to the conversion unit 44 in the above embodiment. The acquisition unit 41 acquires information on the network related to the event, keywords indicating the characteristics of the area where the event is held, and information on the network related to the event. The generation unit generates feature information indicating the characteristics of the event and feature information indicating the characteristics of the keyword based on the information acquired by the acquisition unit. The identification unit 45 identifies a keyword similar to the keyword based on the similarity between the feature information indicating the characteristics of the event and the feature information indicating the characteristics of the keyword.

As a result, the information processing device 10 can accurately determine keywords indicating tourism resources whose needs and tendencies are similar to those of the target event, and thus appropriately supports tourism and the like accompanying the participation of the user in the event. be able to. For example, due to the similarity between an event held in a certain area and a specific facility in the area, the user can be guided to the specific facility after the event ends.

From yet another viewpoint, the information processing apparatus 10 according to the embodiment includes an acquisition unit 41, a generation unit, and a specific unit 45. The generation unit corresponds to the conversion unit 44 in the above embodiment. The acquisition unit 41 acquires information on the network related to each keyword indicating the characteristics of the area. The generation unit generates feature information indicating the characteristics of each keyword based on the information acquired by the acquisition unit. The identification unit 45 identifies other keywords similar to the target keyword to be determined based on the similarity of the feature information of each keyword.

As a result, the information processing device 10 can identify a target keyword to be determined and a keyword having similar needs and tendencies of participating users for each keyword indicating the characteristics of the area. Therefore, the information processing device 10 can improve the accuracy of determining the similarity between keywords.

[6. Hardware configuration]
Further, the information processing apparatus 10 according to the above-described embodiment is realized by, for example, a computer 1000 having a configuration as shown in FIG. 7. FIG. 7 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device 10. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400, and controls each part. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started, a program 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 a predetermined communication network and sends it to the CPU 1100, and transmits the data generated by the CPU 1100 to the other device via the predetermined communication network.

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

The media interface 1700 reads a program or data stored in the recording medium 1800 and provides the program or data to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. And so on.

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

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

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

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

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

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

1 Information processing system 10 Information processing device 20 Communication unit 30 Storage unit 31 Event database 32 User database 33 Venue database 40 Control unit 41 Acquisition unit 42 Estimate unit 43 Calculation unit 44 Conversion unit 45 Specific unit 46 Reception unit 47 Providing unit 100 Terminal Equipment 200 log server

Claims (21)

An acquisition unit that acquires information on the user's network that is related to the event,
An information processing device having a generation unit that generates feature information indicating the characteristics of the event based on the information acquired by the acquisition unit. The information processing apparatus according to claim 1, further comprising a specific unit that identifies another event similar to the target event to be determined based on the similarity of the feature information generated for each event. .. The acquisition unit identifies a user located at a position related to the event based on the position information acquired from the terminal device used by the user, and indicates the behavior of the identified user on the network. The information processing apparatus according to claim 1 or 2, wherein information is acquired. Based on the history of the search query entered by the user, the acquisition unit identifies the user who has entered the search query related to the event, and provides information on the network indicating the behavior of the identified user. The information processing apparatus according to any one of claims 1 to 3, wherein the information processing apparatus is to be acquired. The acquisition unit identifies a user who has browsed the content related to the event based on the history of the content browsed by the user, and acquires information on the network indicating the behavior of the identified user. The information processing apparatus according to any one of claims 1 to 4. The acquisition unit identifies a user who has purchased a transaction target related to the event based on the purchase history by the user, and acquires information on a network indicating the behavior of the specified user. The information processing apparatus according to any one of claims 1 to 5. It has an estimation unit that estimates information about the user based on the information acquired by the acquisition unit.
The information processing apparatus according to any one of claims 1 to 6, wherein the generation unit generates feature information indicating the characteristics of the event based on the information estimated by the estimation unit. .. The information processing device according to claim 7, wherein the estimation unit estimates the context of the behavior of the user in the event. The information processing device according to claim 7 or 8, wherein the estimation unit estimates information regarding the interests of the user. It has a calculation unit that calculates a score indicating the characteristics of the user based on the information acquired by the acquisition unit.
The information processing apparatus according to any one of claims 1 to 9, wherein the generation unit generates feature information indicating the characteristics of the event based on the score calculated by the calculation unit. .. The acquisition department acquires information on the user's network related to the venue where each event is held, and obtains information.
Any one of claims 1 to 10, wherein the generation unit generates characteristic information indicating the characteristics of the event held at the venue based on the information on the network of users related to the venue. The information processing device according to one. The information processing device according to claim 11, wherein the acquisition unit acquires information on a network regarding a user who has visited the venue where each event is held. The acquisition unit according to claim 11 or 12, which is information posted by a user who visits the venue where each event is held, and is characterized by acquiring information about each event and at least one of the venues. Information processing equipment. The acquisition unit further acquires information on the network related to each event, and obtains information on the network.
The claim is characterized in that the generation unit generates feature information indicating the characteristics of each event based on the information of the user related to each event and the information on the network related to each event. The information processing apparatus according to any one of 1 to 13. The generation unit converts the information on the network into a multidimensional quantity by using a model that converts the information into a multidimensional quantity based on the similarity between the information on the network acquired by the acquisition unit. The information processing apparatus according to any one of claims 1 to 14. The reception department that accepts the designation of the target event to be judged from the user,
The information processing apparatus according to claim 2, further comprising a providing unit that provides information indicating another event specified by the specific unit to the user. The information processing device according to claim 16, wherein the providing unit provides information on a derived event that occurs in association with the other event to the user. An acquisition unit that acquires information on the network related to the event, keywords indicating the characteristics of the area where the event is held, and information on the network related to the event.
A generation unit that generates feature information indicating the characteristics of the event and feature information indicating the characteristics of the keyword based on the information acquired by the acquisition unit.
An information processing device including a specific unit that identifies the keyword similar to the event based on the similarity between the feature information indicating the characteristics of the event and the feature information indicating the characteristics of the keyword. An acquisition unit that acquires information on the network related to each keyword that indicates the characteristics of the area,
Others similar to the target keyword to be determined based on the similarity between the generation unit that generates the feature information indicating the characteristics of each keyword based on the information acquired by the acquisition unit and the feature information of each keyword. An information processing device characterized by having a specific unit that specifies a keyword. It is an information processing method executed by an information processing device.
The acquisition process to acquire information on the network of users related to the event,
An information processing method including a generation step of generating feature information indicating the features of the event based on the information acquired in the acquisition step. Acquisition procedure to acquire information on the network of users related to the event,
A program for causing a computer to execute a generation procedure for generating feature information indicating the characteristics of the event based on the information acquired in the acquisition procedure.

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