JP2021125128A - Information processing device, information management method, and information processing program - Google Patents

Abstract

To more effectively analyze a vector on service uses of a user.SOLUTION: An information processing device comprises a generation unit which generates a vector per service from sequence data indicating a transition of service uses of each user on the basis of characteristics of a service use transition mode, and a visualization unit which visualizes a vector per service. At this time, the generation unit learns sequence data by deep learning of a vector representation method, and generates a vector per service. Examples of the vector representation method include Word2 Vec and the like.SELECTED DRAWING: Figure 1

Description

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

A technique for vectorizing a user's behavior history is disclosed.

Japanese Unexamined Patent Publication No. 2009-128937 Japanese Unexamined Patent Publication No. 2017-208044

However, in the above-mentioned prior art, the browsing behavior is categorized from the record of the Web browsing behavior performed by the user on the PC to obtain the feature vector, and the target variable set and the feature vector are obtained from the behavior history data. It is to learn the parameter vector of the classifier for generating a set and classifying whether or not the item is an item adopted by many users based on the feature vector.

The present application has been made in view of the above, and an object of the present application is to analyze a vector regarding a user's service use more effectively.

The information processing apparatus according to the present application uses a generation unit that generates a vector for each service and a vector for each service based on the characteristics of the transition mode of service use from sequence data indicating the transition of service use of each user. It is characterized by including a visualization unit for visualization.

According to one aspect of the embodiment, it is possible to analyze the vector regarding the user's service use more effectively.

FIG. 1 is an explanatory diagram showing an outline of an information processing method according to an embodiment. FIG. 2A is a diagram showing an example of quantification of the purpose of using the service in one period of the first year. FIG. 2B is a diagram showing an example of quantification of the purpose of using the service in the same period of the next fiscal year. FIG. 2C is a diagram showing an example of grouping between the service to be surveyed and the service for the purpose of use close to it. FIG. 3 is a diagram showing a configuration example of the information processing system according to the embodiment. FIG. 4 is a diagram showing a configuration example of the information processing device according to the embodiment. FIG. 5 is a diagram showing an example of a user database. FIG. 6 is a diagram showing an example of a sequence database. FIG. 7 is a diagram showing an example of a service usage purpose database. FIG. 8 is a flowchart showing a processing procedure according to the embodiment. FIG. 9 is a diagram showing an example of a hardware configuration.

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

[1. Information processing method overview]
First, with reference to FIG. 1, an outline of an information processing method performed by the information processing apparatus according to the embodiment will be described. FIG. 1 is an explanatory diagram showing an outline of an information processing method according to an embodiment. In FIG. 1, a case where the purpose of using the service (how it is used) is quantitatively analyzed and a vector for each service is generated will be described as an example.

As shown in FIG. 1, the information processing system 1 includes a terminal device 10, an information processing device 100, and an information providing device 200. The terminal device 10, the information processing device 100, and the information providing device 200 are connected to each other via a network N (see FIG. 3) so as to be able to communicate with each other by wire or wirelessly.

The terminal device 10 is an information processing device used by the user U. The information processing device 100 is an information processing device that analyzes the service usage purpose of each user U. The information providing device 200 is an information processing device that provides various services to the terminal device 10 of each user U. The number of information providing devices 200 may be plurality.

The information processing device 100 may be one of the information providing devices 200. That is, the information processing device 100 may also be an information processing device that provides services. Further, the information providing device 200 may be a relay server (Proxy), a redirector (redirector), or the like.

For example, the user U uses the terminal device 10 to use various services provided by each information providing device 200 (step S1).

Each information providing device 200 aggregates the accesses from the terminal device 10 of each user U and generates an access log of each user U (step S2).

The information processing device 100 acquires the access log of each user U from each information providing device 200 (step S3).

At this time, the information processing apparatus 100 may acquire only the access log of the user U who has consented to the use of the access log in advance. For example, the information processing device 100 may acquire only the access log of the user U registered in advance.

When the information processing device 100 itself provides a service to the terminal device 10 of each user U, the information processing device 100 can generate an access log of each user U regarding the service provided by the information processing device 100. ..

The information processing device 100 creates service usage transition sequence data related to service usage in the same session from the access log of each user U (step S4).

Actually, a series of accesses of the user U within a predetermined period may be used as the use of the service in the same session. In addition to the access performed within the predetermined period, the service may be used in the same session even when the link is followed, for example.

Further, in addition to the access from the same browser and the same application, the access from different browsers and different applications in the same time zone may be used as the service in the same session. That is, in addition to the case where the services are continuously accessed from the same browser or the like, the access that is continuously performed within a predetermined period may be used as the service in the same session.

Further, the service may be any service. For example, the service may be web content or an application (hereinafter referred to as an application). Further, the service may be a function or data of a program provided to the terminal device 10 via an API (Application Programming Interface). Further, the unit of service may be for each web content, each application, each domain, each subdomain, or the like. Further, the information processing apparatus 100 may consider the behavior of the user to be analyzed as a service at an arbitrary particle size, and create service use transition sequence data showing the transition for each service.

Here, the information processing device 100 collects access logs at an arbitrary particle size such as the order of accessed domains, the order of accessed applications, the order of accessed web contents, and the contents of services used by user U. Create service usage transition sequence data showing the order.

In the example shown in FIG. 1, the service usage transition sequence data is the "first search site", "local summary site", "first map WEB", "first map application", "first search site", "1st search site". It includes "travel site" and "accommodation reservation site", and indicates that the use of the service is transitioning in this order.

The "first search site" indicates a search service (search engine) provided via a Web browser. In addition, the "local summary site" indicates a summary site for information originating in the area, tourist information, etc. accessed using a Web browser. Further, the "first map WEB" indicates a map service provided via a Web browser. Further, the "first map application" indicates a map service provided via a map-dedicated application. Further, the "travel site" indicates a travel-related site accessed by using a Web browser. Further, the "accommodation reservation site" indicates a reservation site such as an accommodation facility accessed by using a Web browser.

In generating such service usage transition sequence data, a user ID commonly used by the user U across each service may be used. For example, a table that converts the user IDs individually used by the user U into a common user ID may be used in each service. Further, the user ID may be, for example, identification information of the terminal device 10.

The information processing device 100 is a feature of the transition mode of service use (feature indicated by the service use transition sequence data), and is a vector for each service by learning the feature of the transition order of the service to be used in the model. Is generated (step S5).

For example, the information processing apparatus 100 uses a learning method such as a vector representation method (for example, Word2Vec) or a similar method to make the model learn the features of the service use transition sequence data. Then, the information processing apparatus 100 uses the trained model to generate a vector for each service.

Any type of model can be adopted as the model. For example, the information processing apparatus 100 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.

Further, the information processing apparatus 100 regards the behavior of the user to be analyzed as a service at an arbitrary particle size, creates service use transition sequence data indicating the transition for each service, and features the transition of service use in the created transition sequence data. Therefore, a model may be generated.

Further, in a learning method such as a vector representation method, there are a learning phase in which a model learns the features of training data and a measurement phase in which vectorization is performed using a trained model. In the training phase, when multiple training data similar to the model are input, the model is modified so that a similar vector (for example, a vector with a smaller cosine distance) is generated for each training data. conduct. On the contrary, when a plurality of training data dissimilar to the model are input, the model is trained so as to generate a vector dissimilar to each training data.

Specifically, in the vector representation method, when each word in a sentence is vectorized, it is vectorized as shown in (a) to (c) below. The learning method is not limited to this.

(A) The words before and after the word to be vectorized (how many words before and after are arbitrary) are input to the model, and the output of the model is trained with the target word as correct answer data (Word2Vec CBOW method).

(B) Input the word to be vectorized into the model, and train the output of the model by learning the words before and after it (how many words before and after it are arbitrary) as correct answer data (Word2Vec SkipGram method).

(C) Input a sequence of words into the model and train the model by learning the next character of each word as correct answer data or by learning the next character of the entire input sequence as correct answer data. (RNN and LSTM models).

Then, the higher the score obtained by vectorizing each of the words, the more the learning is performed so as to generate a similar vector. The score is a numerical value indicating the degree of similarity / relevance. For example, the score is the cosine similarity of the vector corresponding to each service.

The above method is also used in this embodiment. For example, the information processing device 100 regards the service used (accessed) by each user U as a "word" in the vector representation method, and regards the service use transition sequence data generated from the access log as a "sentence".

Then, the information processing apparatus 100 generates a model for vectorizing the service usage transition sequence data based on the following concepts (A) to (C). The learning method is not limited to this.

(A) Input the services of the sequence before and after the service to be vectorized (how many services before and after are arbitrary) into the model, and train the output of the model with the target service as correct answer data (Word2Vec CBOW method). ..

(B) Input the service to be vectorized into the model, and train the output of the model as the correct answer data for the service of the sequence before and after it (how many words before and after it are arbitrary) (Word2Vec SkipGram method).

(C) The sequence of services is input to the model, and the next service of each service is trained as correct answer data, or the next service of the entire input sequence is trained as correct answer data, and the model is trained (RNN). And LSTM model).

The vector for each service may be a dense vector or a sparse vector (sparse vector). That is, it may be any kind of vector. Further, the information processing apparatus 100 may generate a dense vector in which the sparseness is eliminated after the sparse vector is generated.

In addition, the vector for each service is a multidimensional vector. Here, the information processing apparatus 100 generates a 128-dimensional vector. The number of dimensions of the vector may be any number of dimensions.

The information processing device 100 dimensionally compresses and visualizes the vector for each service in two dimensions (step S6).

As a result, it is roughly classified by service genre (each with a different purpose of use). By learning the service usage transition sequence in this way, it is possible to analyze the purpose of service usage.

[1-1. Use case 1]
In the service usage transition sequence, the information processing device 100 enumerates services with a purpose of use (how to be used) close to the service to be investigated, grasps the purpose of use of the service to be investigated, and groups them.

Here, the "first map" (first map application) is listed as the survey target service, and the "second map" is listed as the competing service.

For example, as shown in FIGS. 2A and 2B, the information processing apparatus 100 quantifies the purpose of use of the service to be investigated and visualizes the change. FIG. 2A is a diagram showing an example of quantification of the purpose of using the service in one period of the first year. FIG. 2B is a diagram showing an example of quantification of the purpose of using the service in the same period of the next fiscal year.

Here, FIG. 2A shows the scores obtained by vectorizing the services close to each of the "first map" and the "second map" in "April-June 2018". The score is a numerical value indicating the degree of similarity / relevance. For example, the score is the cosine similarity of the vector corresponding to each service.

In the example shown in FIG. 2A, a "news site" and a "weather site" are shown near the "first map". The score of the "news site" for the "first map" is "0.37". The score of the "weather site" for the "first map" is "0.35".

Further, in the example shown in FIG. 2A, a "gourmet review site" and a "travel word-of-mouth site" are shown near the "second map". The score of the "gourmet review site" for the "second map" is "0.44". The score of the "travel word-of-mouth site" for the "second map" is "0.43".

Further, the score of the "second map" with respect to the "first map" is "0.07". The score of the "gourmet review site" for the "first map" is "-0.05".

In this way, "news sites" and "weather sites" are close to the "first map", and information that is of concern for weather forecast confirmation purposes such as rain cloud radar and PUSH notifications such as news (news). It is presumed that it is used for the purpose of confirming the site when the security map etc.) is sent.

On the other hand, "gourmet review sites" and "travel word-of-mouth sites" are close to the "second map", and it is presumed that they are used for the purpose of local information surveys and reservation functions.

FIG. 2B shows the score obtained by vectorizing the services close to each of the "first map" and the "second map" in "April-June 2019" in the same period of the next fiscal year in the example shown in FIG. 2A. It is shown.

Here, the trained model is different between the example shown in FIG. 2A and the example shown in FIG. 2B. For example, when learning is performed using the data of "April-June 2018" as shown in the example shown in Fig. 2A, and the data of "April-June 2019" is used as shown in the example shown in Fig. 2B. Since the output vector changes even in the same service, the display differs between FIGS. 2A and 2B.

In the example shown in FIG. 2B, a "news site" and a "weather site" are shown near the "first map". The score of the "news site" for the "first map" is "0.40". The score of the "weather site" for the "first map" is "0.37". That is, it is presumed that the value is improved as compared with the example shown in FIG. 2A.

Further, in the example shown in FIG. 2B, a "gourmet review site" and a "travel word-of-mouth site" are shown near the "second map". The score of the "gourmet review site" for the "second map" is "0.47". The score of the "travel word-of-mouth site" for the "second map" is "0.45". That is, it is presumed that the value is improved as compared with the example shown in FIG. 2A.

Further, the score of the "second map" with respect to the "first map" is "0.03". The score of the "gourmet review site" for the "first map" is "-0.07". That is, it is presumed that the value is lower than that of the example shown in FIG. 2A.

Then, as shown in FIG. 2C, the information processing device 100 groups the service to be surveyed and the service for the purpose of use (how to be used) close to it, and sets the purpose of use (how to be used) of the service to be surveyed. Based on this, we will analyze the future growth margin (market potential). FIG. 2C is a diagram showing an example of grouping between the service to be surveyed and the service for the purpose of use close to it.

For example, since the current usage of the "first map" is "weather site" and "news site", it is estimated that the growth margin depends on the size and growth potential of these markets. In addition, if further growth is desired, it is estimated that it is necessary to shift to a large market or a growth market.

[1-2. Use case 2]
Like the vector representation method, when similar objects (for example, words) are vectorized from a certain point of view (for example, literature or treatise), similar vectors are generated, and when dissimilar objects are vectorized, they are not similar. It is known that when a vector is generated, the model is considered to be able to learn the concept of the object in that respect. For example, it is possible to make a model learn the concept of each word by learning a model using similarity based on the frequency of appearance, the order of appearance, co-occurrence in a sentence, etc. for two words. Presumed.

It is known that when the above concepts are learned, it is possible to add or subtract the concepts of words. For example, when the words "king", "queen", "man", and "woman" are vectorized using a model that has learned the concepts of various words, the vector of "king" to the vector of "man" It is known that a vector similar to the vector of "queen" can be obtained by subtracting and adding the vector of "woman".

On the other hand, as described above, when the co-occurrence of a plurality of services used in the same session (in other words, a plurality of services used by the users with a common intention) is learned by the model, the user It is possible to let the model learn the concept of what kind of intention the service was used for. As a result, it is considered that the addition and subtraction of the vector of each service can be realized.

Therefore, the information processing apparatus 100 analyzes what is in the competing service (= difference) that is not in the surveyed service by subtracting the vector between the surveyed service and the competing service.

For example, as a result of subtracting the vectors of the "first map" and the "second map", the service usage purpose (how to be used) that is in the "second map" but not in the "first map" is "navigation". , "Restaurant information (reservation)", "Gourmet, travel article content", "Travel / accommodation (reservation)".

In this case, it can be interpreted that each user U uses the "second map" for on-site surveys, reservations, and navigation. Moreover, if it is desired to shift the "first map" to the market on the "second map" side, it can be inferred that these need to be strengthened.

[2. Information processing system configuration example]
Next, the configuration of the information processing system 1 including the information processing device 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram showing a configuration example of the information processing system 1 according to the embodiment. As shown in FIG. 3, the information processing system 1 according to the embodiment includes a terminal device 10, an information processing device 100, and an information providing device 200. These various devices are connected via network N so as to be communicable by wire or wirelessly. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

Further, the number of each device included in the information processing system 1 shown in FIG. 3 is not limited to that shown in the figure. For example, in FIG. 3, for simplification of the illustration, only one terminal device 10 and one information providing device 200 are shown, but this is merely an example and is not limited to two or more. May be good.

The terminal device 10 is an information processing device used by the user U. For example, the terminal device 10 is a smart device such as a smartphone or tablet terminal, a feature phone, a PC (Personal Computer), a PDA (Personal Digital Assistant), a car navigation system, a wearable device such as a smart watch or a head-mounted display (Wearable Device). , Smart glasses, etc.

Further, the terminal device 10 includes a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), 5G (5th Generation: 5th generation mobile communication system), Bluetooth (registered trademark), and wireless LAN (Local). It can be connected to the network N via short-range wireless communication such as Area Network) and communicate with the information processing device 100 and the information providing device 200.

The information processing device 100 and the information providing device 200 are, for example, a PC, a server, a mainframe, a workstation, or the like. The information processing device 100 and the information providing device 200 may be realized by cloud computing.

[3. Information processing device configuration example]
Next, the configuration of the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 4 is a diagram showing a configuration example of the information processing device 100 according to the embodiment. As shown in FIG. 4, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. Further, the communication unit 110 is connected to the network N (see FIG. 3) by wire or wirelessly.

(Memory unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk. As shown in FIG. 4, the storage unit 120 has a user database 121, a sequence database 122, and a service use purpose database 123.

(User database 121)
The user database 121 stores various information about the user U. For example, the user database 121 stores various information such as the attributes of the user U. FIG. 5 is a diagram showing an example of the user database 121. In the example shown in FIG. 5, the user database 121 has items such as "user ID (Identifier)", "age", "gender", "home", "work location", "service", and "access log". Has.

The "user ID" indicates identification information for identifying the user U. Further, "age" indicates the age of the user U identified by the user ID. The "age" may be the specific age of the user U identified by the user ID, such as 35 years old. Further, "gender" indicates the gender of the user U identified by the user ID.

Further, "home" indicates the location information of the home of the user U identified by the user ID. In the example shown in FIG. 5, "home" is illustrated with an abstract code such as "LC11", but may be latitude / longitude information or the like. Further, for example, "home" may be an area name or an address.

Further, the "work location" indicates the location information of the work location (school in the case of a student) of the user U identified by the user ID. In the example shown in FIG. 5, the “work location” is illustrated by an abstract code such as “LC12”, but may be latitude / longitude information or the like. Further, for example, the "work location" may be a region name or an address.

Further, "service" indicates the service currently used by the user U identified by the user ID. The number of "services" may be plural. That is, one user U may use a plurality of services at the same time.

Further, the "access log" is an access log of the user U identified by the user ID, and indicates the access log of the user U acquired from each information providing device 200.

For example, in the example shown in FIG. 5, the age of the user U identified by the user ID "U1" is "20's", and the gender is "male". Further, for example, the user U identified by the user ID "U1" indicates that his / her home is "LC11". Further, for example, the user U identified by the user ID "U1" indicates that the work location is "LC12". Further, for example, it indicates that the user U identified by the user ID "U1" is using "service # 11" and the access log is recorded in "log # 1".

Here, in the example shown in FIG. 5, abstract values such as "U1", "LC11", "LC12", "service # 11", and "log # 1" are used for illustration, but "U1" and "U1", " It is assumed that information such as specific character strings and numerical values is stored in "LC11", "LC12", "service # 11", and "log # 1". Hereinafter, abstract values may be illustrated also in figures relating to other information.

The user database 121 is not limited to the above, and various information may be stored depending on the purpose. For example, the user database 121 may store various information about the terminal device 10 of the user U. In addition, the user database 121 has attributes such as demographic (demographic attribute), psychographic (psychological attribute), geographic (geographical attribute), and behavioral (behavioral attribute) of user U. May remember information about. For example, the user database 121 includes name, family structure, occupation, position, income, qualification, living style (detached house, apartment, etc.), presence / absence of a car, commuting time, commuting route, commuter pass section (station). , Routes, etc.), Frequently used stations (other than the nearest station to your home / work location), Behavior history (Frequently used location information), Lessons (location, time zone, etc.), Hobbies, interests, lifestyle, etc. Information may be stored. In addition, the user database 121 may store information about a search query (search keyword) entered by the user U into a search engine or the like.

(Sequence database 122)
The sequence database 122 stores various information related to the service usage transition sequence data created from the access log related to the service usage in the same session of the user U. FIG. 6 is a diagram showing an example of the sequence database 122. In the example shown in FIG. 6, the sequence database 122 has items such as "user ID", "sequence ID", and "service use transition sequence".

The "user ID" indicates identification information for identifying the user U. Further, the "sequence ID" indicates identification information for identifying the service use transition sequence data. Further, the "service usage transition sequence" indicates each service included in the service usage transition sequence data and the usage order thereof. The information indicating each service may be identification information for identifying each service.

Further, the "service usage transition sequence" includes items such as "service" and "use date and time". “Service” indicates a service used by user U. The "use date and time" indicates the date and time when the user U used the service. The "use date and time" is information indicating the order in which the user U used the service (for example, "1, 2, 3, ...", "A, B, C, ..."). There may be.

For example, in the example shown in FIG. 6, the service usage transition sequence data identified by the sequence ID "SQ1" is obtained from the access log relating to the use of the service in the same session of the user U identified by the user ID "U1". Indicates that it was created. Further, in the service usage transition sequence data identified by the sequence ID "SQ1", the services used by the user U are the "first search site", the "local summary site", the "first map WEB", and the "first". It indicates that the transition is made in the order of "1 map application", "first search site", "travel site", and "accommodation reservation site".

The sequence database 122 is not limited to the above, and various information may be stored depending on the purpose. For example, the sequence database 122 may store information regarding the usage date and time of each service included in the service usage transition sequence data. In addition, the sequence database 122 may store information regarding the usage location of each service included in the service usage transition sequence data.

(Service usage purpose database 123)
The service usage purpose database 123 stores various information related to the service to be surveyed. FIG. 7 is a diagram showing an example of the service usage purpose database 123. In the example shown in FIG. 7, the service usage purpose database 123 has items such as "target service", "neighborhood service", and "score".

“Target service” indicates the service to be investigated in the analysis of the purpose of use of the service. "Neighborhood service" indicates a service whose purpose of use (how it is used) is close to that of the service to be surveyed. The "score" indicates the score obtained by vectorizing the service for the purpose of use close to the service to be surveyed with respect to the service to be surveyed.

For example, in the example shown in Fig. 7, the score of the purpose of use "news site" is "0.37" in April-June 2018 and in April-June 2019 for the surveyed service "1st map". Indicates that it is "0.40". In addition, the score of the purpose of use "weather site" is "0.35" in April-June 2018 and "0.37" in April-June 2019 with respect to the survey target service "1st map". Show that.

In addition, the score of the purpose of use "Gourmet Review Site" was "0.44" in April-June 2018 and "0.47" in April-June 2019 for the surveyed service "2nd Map". Indicates that there is. In addition, the score of the purpose of use "Travel word-of-mouth site" was "0.43" in April-June 2018 and "0.45" in April-June 2019 for the surveyed service "2nd map". Indicates that there is.

The service use purpose database 123 is not limited to the above, and various information may be stored depending on the purpose. For example, the service usage purpose database 123 may store information regarding the position coordinates and vector orientations of the survey target service and the service of the purpose of use close to the survey target service.

(Control unit 130)
Returning to FIG. 4, the description will be continued. The control unit 130 is a controller, and is an information processing device 100 using, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like. It is realized by executing various programs (corresponding to an example of an information processing program) stored in an internal storage device using a storage area such as a RAM as a work area. In the example shown in FIG. 4, the control unit 130 includes a reception unit 131, an acquisition unit 132, a creation unit 133, a learning unit 134, a visualization unit 135, an analysis unit 136, and a provision unit 137.

(Reception Department 131)
The reception unit 131 receives the registration of various information about each user U from the terminal device 10 of each user U via the communication unit 110, and stores the registration in the user database 121.

(Acquisition unit 132)
The acquisition unit 132 acquires the access log of each user U from each information providing device 200 via the communication unit 110.

(Creation unit 133)
The creation unit 133 creates service usage transition sequence data related to service usage in the same session from the acquired access log of each user U.

For example, the creation unit 133 collects the access logs of each user U at an arbitrary particle size such as the order of accessed domains, the order of services accessed, or the order of accessed web contents, and the service used by user U. Create service usage transition sequence data that shows the contents and their order.

Further, when the terminal device 10 of each user U is using the service provided by the information processing device 100, the creation unit 133 automatically obtains an access log related to the service provided by the information processing device 100. It may be generated.

(Learning Department 134)
The learning unit 134 makes the model learn the features of the service usage transition sequence data by using a learning method such as a vector representation method or a similar method. Then, the learning unit 134 generates a vector for each service by using the trained model. Any type of model can be adopted as the model.

The learning is, for example, deep learning (deep learning) using a deep neural network (DNN). Data mining and other machine learning algorithms may also be used. The learning unit 134 learns the model by the various learning methods described above.

For example, the learning unit 134 inputs the services of the sequence before and after the service to be vectorized (how many services before and after are arbitrary) into the model, and trains the output of the model with the target service as correct answer data (Word2Vec). CBOW method).

Alternatively, the learning unit 134 inputs the service to be vectorized into the model, and trains the output of the model as the correct answer data for the service of the sequence before and after it (how many words before and after it are arbitrary) (Word2Vec SkipGram method). ).

Alternatively, the learning unit 134 inputs a sequence of services to the model and trains the next service of each service as correct answer data, or learns the next service of the entire input sequence as correct answer data to make the model. Learn (RNN and LSTM models).

(Visualization unit 135)
The visualization unit 135 compresses the vector for each service in two dimensions and visualizes it.

Visualization refers to making phenomena, events, and relationships that are invisible to the human eye visible (images, graphs, figures, tables, etc.). Two dimensions are just an example. Actually, it may be three-dimensional.

For example, the visualization unit 135 may function as a display control unit that displays information on a vector for each service on a display device such as a display.

Further, the visualization unit 135 may generate information regarding the vector for each visualized service. The information about the visualized vector for each service may be in a file format or a report format.

(Analysis Department 136)
The analysis unit 136 analyzes the purpose of using the service based on the visualized information on the vector for each service.

In addition, the analysis unit 136 groups the survey target service and the service for the purpose of use (how it is used) that is close to the survey target service. For example, when the service to be surveyed is the "first map", the analysis unit 136 outputs the analysis result that the "news application", the "weather application", etc. are close to the "first map". You may.

In addition, the analysis unit 136 analyzes the future growth margin of the survey target service based on the purpose of use of the survey target service.

Further, the analysis unit 136 analyzes what is not in the survey target service but is in the competing service (= difference) by subtracting the vector between the survey target service and the competing service. For example, when the service to be investigated is the "first map" and the competing service is the "second map", the analysis unit 136 is in the "second map" and the purpose of use (use) not in the "first map". Analysis results such as "navigation", "restaurant information (reservation)", "gourmet, travel article content", "travel / accommodation (reservation)", etc. may be output.

(Providing Department 137)
The providing unit 137 provides information on the analysis result of the purpose of using the service to the outside via the communication unit 110. For example, the providing unit 137 may provide a report on the analysis result of the purpose of using the service in response to a request from the outside. The providing unit 137 may function as a display control unit that displays information on the analysis result of the purpose of using the service on a display device such as a display.

Further, the providing unit 137 may provide the information regarding the visualized vector for each service to the outside via the communication unit 110. For example, the providing unit 137 may provide information on the visualized vector for each service to the outside in response to a request from the outside.

Further, the providing unit 137 may provide various services to the terminal device 10 of each user U via the communication unit 110.

[4. Processing procedure]
Next, the processing procedure by the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 8 is a flowchart showing a processing procedure according to the embodiment. The processing procedure shown below is repeatedly executed by the control unit 130 of the information processing apparatus 100.

The control unit 130 of the information processing device 100 acquires the access log of each user U from each information providing device 200 via the communication unit 110 (step S101).

The control unit 130 creates service usage transition sequence data related to service usage in the same session from the access log of each user U (step S102).

The control unit 130 inputs service usage transition sequence data into the model, makes the user U learn the intention (purpose of use) of using the service in the same session, and generates a vector for each service based on the learning result. (Step S103).

The control unit 130 dimensionally compresses and visualizes the vector for each service in two dimensions (step S104).

The control unit 130 analyzes the purpose of using the service based on the visualized information about the vector for each service (step S105).

The control unit 130 provides the analysis result of the purpose of using the service to the outside via the communication unit 110 (step S106).

In reality, the control unit 130 may provide information regarding the visualized vector for each service to the outside via the communication unit 110. As a result, information on the visualized vector for each service is displayed on the external device, and the purpose of using the service can be analyzed externally.

[5. Modification example]
The terminal device 10 and the information processing device 100 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 service usage transition sequence data may be created from the access log related to the service usage in the same session of the user U having the same attribute. For example, since the service usage transition sequence is considered to differ depending on the attributes of the user U (gender / age group / residential area / family composition / hobbies, etc.), the service usage transition sequence data classified according to the attributes of the user U. May be created.

In addition, a model may be generated for each attribute. For example, the service usage transition sequence data of the user of the attribute corresponding to the model is extracted, and the model is generated from the extracted service usage transition sequence data.

Moreover, you may make a proposal for each attribute using the model generated for each attribute. For example, as a result of analysis, in men, the map service is related to news, but in women, when the map service is related to the weather, when proposing service improvement, service improvement for men and service improvement for women You may make separate proposals for and. Also, in the examples shown in FIGS. 2A to 2C, if the first map is closer to the second map for men, it should be closer to travel reviews for men, but closer to the review site for women. You may make some suggestions.

Further, in the above embodiment, the service use transition sequence data may be created from the access log related to the use of the service in the same session according to the situation (context) when the user U uses the service. For example, the service usage transition sequence is considered to change depending on the situation of user U and changes in the environment (moving, purchasing a car, going on to school / employment, changing jobs, getting married, giving birth / childcare, etc.). Service usage transition sequence data classified according to the situation may be created.

In addition, as the situation when the service of the user U is used, the location (at home / going out, inside the commercial facility / outdoors, etc.) and the movement mode (stopped / moving on foot / moving vehicle /) when using the service of the user U Since it is thought that it will change depending on (such as when moving by rail), for example, location confirmation based on location information when using the service (inside home / store / on road / on railroad line, etc.), movement speed judgment (whether or not the speed is 30 km / h or more) Etc.) and / or, depending on the output results of various sensors, etc., the service use transition sequence data classified according to the situation in which the user U is placed may be created.

In addition, a model may be generated for each situation when the service is used. For example, the service usage transition sequence data of the user in the situation corresponding to the model is extracted, and the model is generated from the extracted service usage transition sequence data. In addition, a proposal for each attribute may be made using a model generated for each situation when the service is used.

[6. effect〕
As described above, the information processing apparatus 100 according to the present application includes a generation unit (learning unit 134) and a visualization unit 135. The generation unit generates a vector for each service based on the characteristics of the service usage transition mode from the sequence data indicating the service usage transition of each user U. For example, the generation unit trains the model to learn the characteristics of the transition mode of service use such as "service transition order" and "whether they are used in the same session", and vectorizes each service from the learning result. The visualization unit 135 visualizes the vector for each service. This makes it possible to analyze the vector regarding the user's service usage more effectively.

The generator generates similar vectors for services that appear in the same session in the sequence data. In addition, the generation unit generates a similar vector for services used at close timings in the sequence data. In addition, the generation unit generates a similar vector for services that are continuously used in the sequence data. As a result, similar vectors can be generated in the sequence data according to the closeness of services within the same session.

The generation unit learns the sequence data by deep learning of the vector representation method, and generates a vector for each service. This makes it possible to represent each service as a vector and generate a vector for each service.

Further, the information processing device 100 according to the present application further includes an acquisition unit 132. The acquisition unit 132 acquires an access log indicating the service usage of each user U from each service provider. As a result, it is possible to obtain the access log of each user U and create sequence data showing the transition of service use of each user U.

Further, the information processing apparatus 100 according to the present application further includes a creation unit 133. The creation unit 133 creates sequence data indicating the transition of service usage in the same session from the access log. As a result, the information processing apparatus 100 can collect access logs and create service usage transition sequence data in the same session.

The visualization unit 135 quantifies the purpose of use of the service to be surveyed and visualizes the change. This makes it possible to quantitatively analyze the purpose of use (how it is used) of the service to be surveyed.

Further, the information processing apparatus 100 according to the present application further includes an enumeration unit (control unit 130) and an analysis unit 136. The enumeration section enumerates the services that are used in a manner similar to the service to be surveyed. The analysis unit 136 analyzes the purpose of use of the survey target service from the survey target service and the listed services. This makes it possible to analyze the purpose of use of the surveyed service from the services that are used in a manner similar to that of the surveyed service.

The analysis unit 136 groups the services to be surveyed and the services listed. As a result, it is possible to classify by service genre (each with a different purpose of use). In addition, it is possible to judge the similarity of a plurality of services belonging to a certain category.

The analysis unit 136 analyzes the future growth margin of the survey target service based on the purpose of use of the survey target service. This makes it possible to analyze the market potential (potential growth potential) according to the purpose of use of the surveyed service.

The visualization unit 135 visualizes the vector of the service to be investigated and the vector of the competing service. The analysis unit 136 analyzes what is not in the survey target service but in the competing service by subtracting the vector between the survey target service and the competing service. This makes it possible to make a relative comparison between the surveyed service and the competing service.

The generator generates a multidimensional vector. The visualization unit 135 visualizes a multidimensional vector by compressing it in two dimensions. This makes it easier to visually recognize the vector for each service.

Further, the information processing apparatus 100 according to the present application further includes a providing unit 137. The provider 137 provides information about the visualized vector for each service. This enables an external analysis of the vector for each service. In addition, the analysis results can be reported to the outside.

The generation unit generates a vector for each service based on the characteristics of the service usage transition mode from the sequence data indicating the service usage transition according to the attribute of each user. This makes it possible to create service usage transition sequence data classified according to the attributes of user U, depending on the attributes of user U (gender / age group / residential area / license / presence / absence of car / hobby, etc.). It is possible to deal with different purposes of using the service.

The generation unit generates a vector for each service based on the characteristics of the service usage transition mode from the sequence data indicating the service usage transition according to the service usage status of each user. As a result, it is possible to create service usage transition sequence data classified according to the situation (context) in which the user U is placed, and to deal with the difference in the purpose of using the service depending on the situation (context) in which the user U is placed. Can be done.

[7. Hardware configuration]
Further, the terminal device 10 and the information processing device 100 according to the above-described embodiment are realized by, for example, a computer 1000 having a configuration as shown in FIG. Hereinafter, the information processing device 100 will be described as an example. FIG. 9 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to the output device 1010 and the input device 1020, and the arithmetic unit 1030, the primary storage device 1040, the secondary storage device 1050, the output I / F (Interface) 1060, the input I / F 1070, and the network I / F 1080 are bused. It has a form connected by 1090.

The arithmetic unit 1030 operates based on a program stored in the primary storage device 1040 or the secondary storage device 1050, a program read from the input device 1020, or the like, and executes various processes. The arithmetic unit 1030 is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like.

The primary storage device 1040 is a memory device that temporarily stores data used by the arithmetic unit 1030 for various calculations, such as a RAM (Random Access Memory). Further, the secondary storage device 1050 is a storage device in which data used by the calculation device 1030 for various calculations and various databases are registered, and is a ROM (Read Only Memory), an HDD (Hard Disk Drive), and an SSD (Solid). State Drive), flash memory, etc. The secondary storage device 1050 may be internal storage or external storage. Further, the secondary storage device 1050 may be a removable storage medium such as a USB memory or an SD (Secure Digital) memory card. Further, the secondary storage device 1050 may be a cloud storage (online storage), a NAS (Network Attached Storage), a file server, or the like.

The output I / F 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various information such as a display, a projector, and a printer. For example, USB (Universal Serial Bus) or DVI. (Digital Visual Interface), HDMI (registered trademark) (High Definition Multimedia Interface), etc. are realized by standard connectors. The input I / F 1070 is an interface for receiving information from various input devices 1020 such as a mouse, keyboard, keypad, buttons, and a scanner, and is realized by, for example, USB.

Further, the output I / F 1060 and the input I / F 1070 may be wirelessly connected to the output device 1010 and the input device 1020, respectively. That is, the output device 1010 and the input device 1020 may be wireless devices.

Further, the output device 1010 and the input device 1020 may be integrated like a touch panel. In this case, the output I / F 1060 and the input I / F 1070 may also be integrated as input / output I / F.

The input device 1020 is, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), or a tape. It may be a device that reads information from a medium, a magnetic recording medium, a semiconductor memory, or the like.

The network I / F 1080 receives data from another device via the network N and sends it to the arithmetic unit 1030, and also transmits the data generated by the arithmetic unit 1030 to the other device via the network N.

The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output I / F 1060 and the input I / F 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040, and executes the loaded program.

For example, when the computer 1000 functions as the information processing device 100, the arithmetic unit 1030 of the computer 1000 realizes the function of the control unit 130 by executing the program loaded on the primary storage device 1040. Further, the arithmetic unit 1030 of the computer 1000 may load a program acquired from another device via the network I / F 1080 onto the primary storage device 1040 and execute the loaded program. Further, the arithmetic unit 1030 of the computer 1000 may cooperate with other devices via the network I / F 1080 to call the functions and data of the program from other programs of the other devices and use them.

[8. others〕
Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. In addition, the above-mentioned components include those that can be easily assumed by those skilled in the art, those that are substantially the same, that is, those in a so-called equal range. Furthermore, the components described above can be combined as appropriate. Further, various omissions, replacements or changes of the components can be made without departing from the gist of the above-described embodiment.

Further, among the processes described in the above-described embodiment, all or a part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed can be performed. All or part of it can be done automatically 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 arbitrary units according to various loads and usage conditions. Can be integrated and configured.

For example, the information processing device 100 described above may be realized by a plurality of server computers, or may be realized by calling an external platform or the like by API (Application Programming Interface), network computing, or the like depending on the function. Can be changed flexibly.

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 acquisition unit can be read as an acquisition means or an acquisition circuit.

1 Information processing system 10 Terminal equipment 100 Information processing equipment 110 Communication unit 120 Storage unit 121 User database 122 Sequence database 123 Service usage purpose database 130 Control unit 131 Reception unit 132 Acquisition unit 133 Creation unit 134 Learning unit 135 Visualization unit 136 Analysis unit 137 Provider 200 Information Provider

Claims (18)

A generator that generates a vector for each service based on the characteristics of the service usage transition mode from the sequence data indicating the service usage transition of each user.
An information processing device characterized by having a visualization unit that visualizes a vector for each service. The information processing apparatus according to claim 1, wherein the generation unit generates similar vectors for services appearing in the same session in the sequence data. The information processing apparatus according to claim 1 or 2, wherein the generation unit generates similar vectors for services used at close timings in the sequence data. The information processing apparatus according to any one of claims 1 to 3, wherein the generation unit generates a similar vector for services continuously used in the sequence data. The information processing apparatus according to any one of claims 1 to 4, wherein the generation unit learns the sequence data by deep learning of a vector representation method and generates a vector for each service. The information processing apparatus according to any one of claims 1 to 5, further comprising an acquisition unit that acquires an access log indicating each user's service usage from each service provider. The information processing apparatus according to claim 6, further comprising a creation unit that creates sequence data indicating a transition of service use in the same session from the access log. The information processing apparatus according to any one of claims 1 to 7, wherein the visualization unit quantifies the purpose of use of the service to be investigated and visualizes changes. An enumeration section that lists services that are used in a manner similar to the service to be surveyed,
The information processing apparatus according to any one of claims 1 to 8, further comprising an analysis unit that analyzes the purpose of use of the survey target service from the survey target service and the listed services. .. The information processing apparatus according to claim 9, wherein the analysis unit groups the service to be investigated and the services listed. The information processing apparatus according to claim 9 or 10, wherein the analysis unit analyzes the future growth margin of the survey target service based on the purpose of use of the survey target service. The visualization unit visualizes the vector of the service to be investigated and the vector of the competing service.
Claims 9 to 11 are characterized in that the analysis unit analyzes a service that is not in the service to be investigated but is in the competing service by subtracting a vector between the service to be investigated and the competing service. The information processing apparatus according to any one of the above. The generator generates a multidimensional vector and
The information processing apparatus according to any one of claims 1 to 12, wherein the visualization unit visualizes the multidimensional vector by dimensionally compressing it in two dimensions. The information processing apparatus according to any one of claims 1 to 13, further comprising a providing unit that provides information on a vector for each visualized service. Claim 1 is characterized in that the generation unit generates a vector for each service based on the characteristics of the service usage transition mode from sequence data indicating the service usage transition according to the attributes of each user. The information processing apparatus according to any one of ~ 14. The generator is characterized in that it generates a vector for each service based on the characteristics of the service usage transition mode from the sequence data indicating the service usage transition according to the service usage status of each user. The information processing apparatus according to any one of claims 1 to 15. It is an information processing method executed by an information processing device.
A generation process that generates a vector for each service based on the characteristics of the service usage transition mode from the sequence data indicating the service usage transition of each user.
An information processing method including a visualization process for visualizing a vector for each service. A generation procedure for generating a vector for each service based on the characteristics of the service usage transition mode from sequence data indicating the service usage transition of each user, and a generation procedure.
An information processing program that allows a computer to execute a visualization procedure that visualizes a vector for each service.

Images