JP7032594B1 - Information providers, information processing methods, and programs - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an information providing device, an information processing method, and a program for providing information requested by a provider. An information providing device according to the present application includes an acquisition unit and an estimation unit. The acquisition unit acquires information about the provider cluster in which the provider user is clustered based on the data of the provider user as the provider cluster data. The estimation unit is based on the similarity between the operator cluster data, which is information about the operator cluster in which the user of the predetermined operator is clustered based on the data of the user of the predetermined operator, and the provider cluster data. Estimate the relationship between the cluster and the operator cluster. [Selection diagram] Fig. 1

Description

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

Conventionally, an electronic shopping street service that allows purchase of transaction targets sold by a plurality of businesses has been provided. As an example of such a service, a technique of proposing a transaction target selected according to a user's attribute is known.

Japanese Unexamined Patent Publication No. 2014-186582

When proposing a transaction target, information is provided from the business operator to the provider, and the provider can also propose the transaction target to the user of the provider based on the provided information. However, in this case, the business operator may not be able to provide the information requested by the provider to the provider.

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

The information providing device according to the present application includes an acquisition unit and an estimation unit. The acquisition unit acquires information about the provider cluster in which the provider user is clustered based on the data of the provider user as the provider cluster data. The estimation unit is based on the similarity between the operator cluster data, which is information about the operator cluster in which the user of the predetermined operator is clustered based on the data of the user of the predetermined operator, and the provider cluster data. Estimate the relationship between the cluster and the operator cluster.

According to one aspect of the embodiment, the information providing device, the information processing method, and the program can provide the information requested by the provider.

FIG. 1 is an explanatory diagram showing an outline of an information processing method according to an embodiment. FIG. 2 is a diagram showing a configuration example of an information processing system according to an embodiment. FIG. 3 is a block diagram showing a configuration example of the information providing device according to the embodiment. FIG. 4 is a flowchart illustrating the statistical information providing process according to the embodiment. FIG. 5 is a table showing the correspondence of preferences (interests) among users of businesses belonging to the business cluster. FIG. 6 is a hardware configuration diagram showing an example of a computer that realizes the function of the information providing device.

Hereinafter, the information providing device, the information processing method, and the embodiment for implementing the program (hereinafter referred to as “the embodiment”) according to the present application will be described in detail with reference to the drawings. It should be noted that this embodiment does not limit the information providing device, the information processing method, and the program according to the present application.

<Overview of information processing method>
First, with reference to FIG. 1, an outline of an information processing method performed by the information providing apparatus 1 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.

The information providing device 1 is connected to the external device 100 via a network N (see FIG. 2) so as to be able to communicate with each other by wire or wirelessly.

The external device 100 is an information processing device to which various information is provided from the information providing device 1, and is realized by a server device, a cloud system, or the like. The external device 100 has data about the user to whom it is provided. Data about the user includes the attributes of the user.

User attributes include user demographics (demographic attributes), geographics (geographical attributes), behavioral attributes (behavioral attributes), and the like. For example, user-related data includes name, age, gender, family structure, place of origin (local), occupation, position, income, qualifications, living style (detached house, apartment, etc.), presence or absence of a car, commuting time, commuting to school.・ Commuting route, commuter pass section (stations, routes, etc.), work location, frequently used stations (other than the nearest station to your home / work location), lessons (location, time zone, etc.), hobbies, interests, lifestyle, etc. Includes informational data.

In addition, the data about the user may include the user's action log. The user's action log contains informational data such as search queries and actions. The search query indicates a keyword entered by the user into a search engine or the like. The action is, for example, a stepwise action (clicking, adding to a cart, pressing a purchase button, etc.) leading to a conversion (final action) such as a purchase.

The external device 100 clusters the users of the provision destination based on the data regarding the users of the provision destination by using the first classification model.

The first classification model is a model for clustering the users of the provision destination based on the data regarding the users of the provision destination. The first classification model is generated by applying an arbitrary machine learning algorithm. Arbitrary machine learning algorithms include deep learning, logistic analysis, random forests, support vector machines, decision trees, and more.

The first classification model is generated so that the number of destination users included in each cluster is equal to or greater than a predetermined number. The predetermined number is a number that prevents the user from being specified, and is, for example, "10".

The external device 100 generates the first classification model based on the data regarding the provided user. The external device 100 may store the first classification model generated by another device and use the stored first classification model.

The external device 100 clusters the users of the provision destination by using the first classification model, and generates information about the cluster of the users of the provision destination (hereinafter, referred to as “providance destination cluster”) as the provision destination cluster data. The destination cluster data includes at least one of the label of the destination cluster, statistics about the destination user belonging to the destination cluster, and the attributes of the destination user belonging to the destination cluster.

Statistical information is statistical information about users belonging to each cluster. For example, the statistical information about the provider user includes the hobby of the provider user, the interest of the provider user, the purchase record of the provider user, and the like. For example, the statistical information about the provided user is that the provided user likes a car, likes wine, and the like.

The statistical information may be a score to the extent that the user's attributes are satisfied. For example, the statistical information about the provided user may be a score indicating the possibility that the provided user purchases the refrigerator.

The information providing device 1 is an information processing device of a business operator (predetermined business operator), and is realized by a server device, a cloud system, or the like. The information providing device 1 has data about the user of the business operator. The information providing device 1 clusters the users of the business operator based on the data about the users of the business operator by using the second classification model.

The second classification model is a model for clustering the users of the business based on the data about the users of the business. The second classification model is generated by applying an arbitrary machine learning algorithm. Arbitrary machine learning algorithms include deep learning, logistic analysis, random forests, support vector machines, decision trees, and more.

The second classification model is generated so that the number of users of the business operator included in each cluster becomes a predetermined number or more.

The information providing device 1 generates the second classification model based on the data about the user of the business operator. The information providing device 1 may store a second classification model generated by another device and use the stored second classification model.

The information providing device 1 clusters the users of the business operator using the second classification model, and generates information about the cluster of the user of the business operator (hereinafter, referred to as “business operator cluster”) as the business operator cluster data. .. The operator cluster data includes at least one of the label of the operator cluster, statistical information about the user of the operator belonging to the operator cluster, and the attribute of the user of the operator belonging to the operator cluster.

The information providing device 1 acquires the providing destination cluster data from the external device 100 (S1). The information providing device 1 estimates the relationship between the providing destination cluster and the operator cluster based on the similarity between the acquired provider cluster data and the operator cluster data (S2).

For example, when "cluster A" is acquired as the destination cluster data, the information providing device 1 determines the similarity between the label of "cluster A" and the label of each cluster of the operator. Then, the information providing device 1 estimates the cluster "cluster ab" of the business operator most similar to the label of "cluster A" as the business operator cluster related to the providing destination cluster.

As will be described later, the information providing device 1 describes not only the label of each cluster (the label set before clustering the users, that is, the label that is the correct answer, but also the commonality of information about the clustered users). It is a concept that includes labels that are applied ex post facto based on.) The cluster with the highest score indicating similarity between the operator cluster and the destination cluster based on the similarity between categories. A combination or a combination of clusters whose score value exceeds a predetermined threshold may be regarded as a related cluster. In this way, which provider cluster and which operator cluster are associated as related clusters will be described later.

The information providing device 1 provides the statistical information of the operator cluster related to the providing destination cluster to the providing destination (S3). Specifically, the information providing device 1 transmits the statistical information of the operator cluster related to the providing destination cluster to the external device 100. For example, the information providing device 1 provides the statistical information of the operator cluster "cluster ab" to the providing destination.

The provider to whom the statistical information is provided can make a proposal or the like to the user of the provider based on the provided statistical information. Therefore, the provider can make a proposal to the provider user based on the statistical information that is highly relevant to the provider user.

In addition, personal information of each user is not transmitted between the business operator and the provider. That is, the information that identifies each user is not transmitted or received between the business operator and the provider.

<Information processing system configuration>
Next, the configuration of the information processing system 300 will be described with reference to FIG. FIG. 2 is a diagram showing a configuration example of the information processing system 300 according to the embodiment. The information processing system 300 includes an external device 100, an information providing device 1, and a terminal device 200.

The number of each device included in the information processing system 300 shown in FIG. 2 is not limited to that shown in the figure. For example, in FIG. 2, for simplification of the illustration, only one terminal device 200 is shown, but this is merely an example and is not limited, and may be two or more.

The terminal device 200 is an information processing device used by the user. For example, the terminal device 200 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 200 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 external device 100 and the information providing device 1.

The external device 100 can acquire data about a user (user of a provider) via the terminal device 200. The information providing device 1 can acquire data about a user (user of a business operator) via a terminal device 200.

<Configuration of information providing device>
Next, the configuration of the information providing device 1 will be described with reference to FIG. FIG. 3 is a diagram showing a configuration example of the information providing device 1 according to the embodiment. The information providing device 1 includes a communication unit 10, a storage unit 20, and a control unit 30.

The communication unit 10 is realized by, for example, a NIC (Network Interface Card) or the like. Further, the communication unit 10 is connected to the network N (see FIG. 2) by wire or wirelessly.

The storage unit 20 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.

The storage unit 20 stores data, a second classification model, statistical information, and the like regarding the user of the business operator. The storage unit 20 has a user information database 21, a model database 22, and a statistical information database 23.

The user information database 21 stores various information about the user of the business operator. For example, the user information database 21 stores various information such as attributes of the user of the business operator. For example, the user information database 21 has items such as "user ID (Identifier)", "age", "gender", "home", "work location", and "interest". The user ID indicates identification information for identifying the user of the business operator.

Further, the user information database 21 stores various information related to the action log of the user of the business operator. For example, the user information database 21 has items such as "user ID", "search query", and "behavior".

The user information database 21 is not limited to the above, and various information may be stored depending on the purpose. For example, the user information database 21 may store information such as the position history, search history, browsing history, purchase history, and posting history of the user of the business operator.

The model database 22 stores the second classification model generated by the generation unit 32 described later.

The statistical information database 23 stores statistical information for each cluster of users of the business operator classified by the second classification model. The statistical information database 23 may store a plurality of statistical information for each cluster. For example, the statistical information database 23 stores hobbies, interests, purchase records, etc. of users of businesses belonging to each cluster.

The control unit 30 is a controller, and is an information providing device 1 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. It is realized by executing various programs (corresponding to an example of an information processing program) stored in the internal storage device of the ASIC with a storage area such as a RAM as a work area.

In the example shown in FIG. 3, the control unit 30 includes an acquisition unit 31, a generation unit 32, an estimation unit 33, a specific unit 34, and a provision unit 35. The acquisition unit 31, the generation unit 32, the estimation unit 33, the specific unit 34, and the provision unit 35 are not limited to this, and may be integrated or may be divided and configured.

The acquisition unit 31 acquires data about the user of the business operator from the terminal device 200 via the communication unit 10. For example, the acquisition unit 31 acquires the attributes of the user of the business operator and the action log of the user of the business operator from the terminal device 200 of the user in response to the operation of the terminal device 200 via the communication unit 10.

The acquisition unit 31 acquires the provider cluster data from the external device 100 via the communication unit 10.

The generation unit 32 performs machine learning using the data about the user of the business operator acquired by the acquisition unit 31 to generate the second classification model.

The estimation unit 33 estimates the relationship between the provider cluster and the operator cluster based on the similarity between the provider cluster data and the operator cluster data.

When the provider cluster and the operator cluster are labeled, the estimation unit 33 determines the similarity between the label of the provider cluster and the label of the operator cluster. The estimation unit 33 determines the similarity between the label of the provider cluster and the label of the operator cluster based on the correspondence set between the label of the provider cluster and the label of the operator cluster.

The similarity between the label of the provider cluster and the label of the operator cluster may be determined using a similarity determination model. The similarity judgment model is generated by applying an arbitrary machine learning algorithm. The similarity determination model may be learned based on the provision result of the provided information from the provider when the statistical information is provided to the provider.

The estimation unit 33 may determine the similarity between the provider cluster data and the operator cluster data based on the statistical information of the provider user and the statistical information of the operator user. For example, when the provider user in the provider cluster is "car lover", the estimation unit 33 determines the operator cluster to which the user of the "car enthusiast" operator belongs as a cluster similar to the provider cluster. ..

If the destination cluster and the operator cluster are not labeled, the estimation unit 33 is similar to the attributes of the provider user belonging to the provider cluster and the attributes of the operator user belonging to the operator cluster. Judge the sex. The estimation unit 33 determines a cluster to which a user of a business operator having an attribute similar to the attribute of the user of the provider belonging to the provider cluster belongs as a business operator cluster similar to the provider cluster.

The estimation unit 33 estimates the operator cluster similar to the provider cluster as the operator cluster related to the provider cluster. When there are a plurality of business clusters similar to the provider cluster, the estimation unit 33 estimates the business cluster most similar to the provider cluster as the business cluster related to the provider cluster.

The identification unit 34 identifies the statistical information of the operator cluster related to the provider cluster estimated by the estimation unit 33. The type of statistical information is set by the business operator, for example. The type of statistical information may be specified to the destination. The identification unit 34 may specify a plurality of statistical information.

The providing unit 35 transmits the statistical information specified by the specifying unit 34 to the external device 100 via the communication unit 10. That is, the providing unit 35 provides the providing destination with the statistical information of the operator cluster related to the providing destination cluster.

<Explanation of statistical information provision processing>
Next, the statistical information providing process according to the embodiment will be described with reference to FIG. FIG. 4 is a flowchart illustrating the statistical information providing process according to the embodiment.

The control unit 30 of the information providing device 1 acquires the providing destination cluster data from the external device 100 (S100).

The control unit 30 of the information providing device 1 estimates the relationship between the providing destination cluster and the operator cluster based on the similarity between the providing destination cluster data and the operator cluster data (S101).

The control unit 30 of the information providing device 1 specifies the statistical information of the operator cluster related to the providing destination cluster (S102).

The control unit 30 of the information providing device 1 transmits the statistical information of the operator cluster related to the providing destination cluster to the external device 100 (S103).

<Effect>
The information providing device 1 includes an acquisition unit 31 and an estimation unit 33. The acquisition unit 31 acquires information about the provider cluster as the provider cluster data. The estimation unit 33 estimates the relationship between the provider cluster and the operator cluster based on the similarity between the operator cluster data, which is information about the operator cluster, and the provider cluster data.

As a result, the information providing device 1 can estimate the operator cluster that is highly related to the providing destination cluster. Therefore, the information providing device 1 can provide information highly related to the providing destination cluster to the providing destination.

The information providing device 1 includes a providing unit 35. The provider 35 provides the provider with statistical information about the operator cluster estimated to be related to the destination cluster by the estimation unit 33.

As a result, the information providing device 1 can provide the statistical information of the operator cluster that is highly related to the providing destination cluster to the providing destination. Further, the information providing device 1 can provide the information requested by the providing destination without transmitting the personal information of the user of the business operator to the providing destination.

The estimation unit 33 estimates the relationship between the provider cluster and the operator cluster based on the similarity between the label of the provider cluster and the label of the operator cluster.

As a result, the information providing device 1 can easily estimate the operator cluster that is highly related to the providing destination cluster.

The estimation unit 33 determines the relationship between the provider cluster and the provider cluster based on the similarity between the attributes of the provider user belonging to the provider cluster and the attributes of the provider user belonging to the operator cluster. presume.

As a result, the information providing device 1 can accurately estimate the operator cluster that is highly related to the providing destination cluster.

The estimation unit 33 estimates the relationship between the provider cluster and the operator cluster using the similarity determination model.

As a result, the information providing device 1 can accurately estimate the operator cluster that is highly related to the providing destination cluster.

<Modification example>
The information providing device 1 according to the modification may estimate the label of the business cluster based on the attributes of the user of the business. For example, the information providing device 1 according to the modification may estimate the label of the business operator cluster using a model that executes the labeling process based on the attributes of the business operator user.

As a result, in the information providing device 1 according to the modified example, when the provider cluster is labeled and the operator cluster is not labeled, the label of the provider cluster and the estimated operator cluster are attached. Based on the label of, the operator cluster related to the destination cluster can be estimated.

Even if the destination cluster is not labeled, the label of the destination cluster may be estimated using the model that executes the labeling process in the same manner. The label estimation of the destination cluster may be performed by the external device 100 or may be performed by the information providing device 1.

Further, when the provider cluster and the operator cluster are not labeled, the provider cluster and the operator cluster may be labeled by using the model for executing the labeling process. In this case, the same model may be used to label the provider cluster and the operator cluster, respectively. As a result, the information providing device 1 according to the modified example can estimate the business operator cluster similar (matching) to the providing destination cluster, and can accurately estimate the business operator cluster related to the providing destination cluster. Therefore, the information providing device 1 according to the modified example can provide the statistical information requested by the providing destination to the providing destination.

The information providing device 1 according to the modification may estimate the statistical information of the business operator cluster related to the provider cluster based on the similarity between the statistical information in the provider cluster and the statistical information in the business operator cluster. .. For example, as shown in FIG. 5, the information providing device 1 according to the modified example has the data of the correspondence relationship of the statistical information of the operator cluster. FIG. 5 is an example showing the correspondence data of the statistical information of the operator cluster. FIG. 5 is a table showing the correspondence of preferences (interests) among users of businesses belonging to the business cluster. In FIG. 5, there is a correspondence relationship, that is, a positive relationship is indicated by “◯”, and a negative relationship is indicated by “×”.

For example, when the statistical information of the operator cluster that is similar to (matches) the statistical information in the provider cluster is "GH like", the user of the operator of "GH like" is "JK like" and "PQ like". Is. Moreover, the user of the business operator who "likes GH" is not "likes LM".

In such a case, the information providing device 1 according to the modified example belongs to the providing destination cluster and provides as statistical information that the user of the providing destination of "GH like" is "JK like" and "PQ like". do. The information providing device 1 according to the modified example may be provided as statistical information that the user of the providing destination of "GH liking" does not belong to the providing destination cluster and is not "LM liking". The statistical information having a correspondence relationship may be a search query entered by the user, a landing page, or the like.

As a result, the information providing device 1 according to the modified example can provide the providing destination with statistical information that cannot be estimated from the statistical information of the providing destination user possessed by the providing destination.

The information providing device 1 according to the modified example may receive the evaluation result of the statistical information provided to a plurality of providing destinations from the providing destination. The information providing device 1 according to the modification is a method of determining the similarity between the providing destination cluster data and the operator cluster data for each providing destination, for example, statistics on the attributes of the user used for determining the similarity. It may be changed based on the evaluation result of the information.

As a result, the information providing device 1 according to the modified example can estimate the operator cluster related to the provider cluster according to the provider, and estimate the operator cluster related to the provider cluster. The accuracy can be improved. Therefore, the information providing device 1 according to the modified example can provide the statistical information requested by the providing destination to the providing destination.

The information providing device 1 according to the modification may generate a category ID based on the attributes of the user of the business operator. For example, the information providing device 1 according to the modified example uses a model for determining whether or not the condition of the attribute of the user of the business operator is satisfied, and is "0" or "1" with respect to the condition of the attribute of the user of the business operator. Is given. Then, the information providing device 1 according to the modification converts the character string in which the attribute "0" or "1" of the user of the business operator is concatenated into a hexadecimal number. In this way, the information providing device 1 according to the modified example generates the category ID. The generated category ID is stored in the storage unit 20.

Similarly, the category ID is generated in the external device 100 as well. The information providing device 1 according to the modified example acquires a category ID from the external device 100, reads a category ID similar to the acquired category ID, and is related to the provider cluster and the operator cluster based on the read category ID. Estimate sex. When the user of the provider and the user of the provider have similar attributes, the category ID in the provider cluster and the category ID in the provider cluster are similar. Therefore, the information providing device 1 according to the modified example can accurately estimate the operator cluster related to the providing destination cluster by using the category ID.

As a result, the information providing device 1 according to the modified example can accurately estimate the operator cluster related to the providing destination cluster. Therefore, the information providing device 1 according to the modified example can provide the statistical information requested by the providing destination to the providing destination.

The information providing device 1 according to the modified example acquires the relationship information (for example, the correspondence table showing the relationship between each cluster) indicating the relationship between the operator cluster and the providing destination cluster by the various processes described above. You may. For example, the information providing device 1 according to the modified example can acquire the relationship information by determining the relationship with the operator cluster for each providing destination cluster. Using such relationship information, the information providing device 1 according to the modified example may predict various relationships.

For example, as a modification, the information providing device 1 generates relationship information for each providing destination by executing the above-mentioned processing for a plurality of providing destinations. Then, the information providing device 1 according to the modified example learns the characteristics of the labels and statistical information given to the providing destination cluster presumed to be highly related to the predetermined business cluster, and thus the predetermined business cluster. Generate a model that learns the characteristics of the destination cluster that is highly related to. After that, when the information providing device 1 according to the modified example receives the label or statistical information of the providing destination cluster from a new providing destination, for example, the information providing device 1 uses the label or statistical information and the generated model to determine a predetermined value. The relationship with the operator cluster may be determined.

<Hardware configuration>
Further, the external device 100, the information providing device 1, and the terminal device 200 according to the above-described embodiment are realized by, for example, a computer 1000 having a configuration as shown in FIG. Hereinafter, the information providing device 1 will be described as an example. FIG. 6 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 such as a RAM (Random Access Memory) that temporarily stores data used by the arithmetic unit 1030 for various calculations. 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 an internal storage or an 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), 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. Further, the input I / F 1070 is an interface for receiving information from various input devices 1020 such as a mouse, a keyboard, a keypad, a button, a scanner, and the like, 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 providing device 1, the arithmetic unit 1030 of the computer 1000 realizes the function of the control unit 30 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.

<Others>
Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. Further, 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, information including processing procedures, specific names, various data and parameters shown in the above documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown in the figure.

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

For example, the above-mentioned information providing device 1 may be realized by a plurality of server computers, or may be realized by calling an external framework or the like by API (Application Programming Interface), network computing, or the like depending on the function. The configuration can be changed flexibly.

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

Further, the above-mentioned "section, module, unit" can be read as "means", "circuit" and the like. For example, the acquisition unit 31 can be read as an acquisition means or an acquisition circuit.

1 Information providing device 10 Communication unit 20 Storage unit 30 Control unit 31 Acquisition unit 32 Generation unit 33 Estimating unit 34 Specific unit 35 Providing unit 100 External device 200 Terminal device 300 Information processing system

Claims (8)

An acquisition unit that acquires information about a clustered destination cluster based on the data of the destination user as the destination cluster data, and an acquisition unit.
The provider cluster based on the similarity between the provider cluster data, which is information about the operator cluster in which the user of the predetermined operator is clustered based on the user data of the predetermined operator, and the provider cluster data. An information providing device including an estimation unit that estimates the relationship with the operator cluster. The information providing device according to claim 1, further comprising a providing unit that provides the providing destination with statistical information about the business operator cluster estimated to be related to the providing destination cluster by the estimating unit. The estimation unit estimates the relationship between the provider cluster and the operator cluster based on the similarity between the label of the provider cluster and the label of the operator cluster, claim 1 or 2. Information providing device described in. The label of the destination cluster is estimated based on the attributes of the destination user included in the destination cluster.
The information providing device according to claim 3, wherein the label of the business operator cluster is estimated based on the attributes of the user of the predetermined business operator included in the business operator cluster. The estimation unit sets the provision destination cluster and the above-mentioned thing based on the similarity between the attribute of the provision destination user belonging to the provision destination cluster and the attribute of the user of the predetermined business operator belonging to the business operator cluster. The information providing device according to claim 1 or 2, which estimates the relevance to the vendor cluster. The information providing device according to any one of claims 1 to 5, wherein the estimation unit estimates the relationship between the providing destination cluster and the operator cluster using a similarity determination model. It is an information processing method executed by the information providing device.
An acquisition process of acquiring information about a clustered destination cluster based on the data of the destination user as the destination cluster data, and an acquisition process.
The provider cluster based on the similarity between the provider cluster data, which is information about the operator cluster in which the user of the predetermined operator is clustered based on the user data of the predetermined operator, and the provider cluster data. And an information processing method having an estimation process for estimating the relationship with the operator cluster. An acquisition procedure for acquiring information about a clustered destination cluster based on the data of the destination user as the destination cluster data, and an acquisition procedure.
The provider cluster based on the similarity between the provider cluster data, which is information about the operator cluster in which the user of the predetermined operator is clustered based on the user data of the predetermined operator, and the provider cluster data. A program that causes a computer to execute an estimation procedure for estimating the relationship with the operator cluster.

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