JP7633207B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

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

Conventionally, there are known techniques for providing various information to users. For example, there is known a technique for providing users with ranking information in which trading objects, such as product information corresponding to a search query, are used as ranking elements.

Patent No. 6776072

However, the above conventional techniques leave room for improvement in terms of providing users with more appropriate ranking information tailored to their needs.

The present application has been made in consideration of the above, and aims to provide an information processing device, an information processing method, and an information processing program that can provide more appropriate ranking information to users.

The information processing device according to the present application includes a determination unit and a provision unit. The determination unit determines the granularity of the ranking based on the behavioral history of the user to whom the ranking information, which is ranking information, is to be provided. The provision unit provides the ranking information of the granularity determined by the determination unit as ranking information to the user.

According to one aspect of the embodiment, it is possible to provide more appropriate ranking information to the user.

FIG. 1 is a diagram for explaining an information processing method performed by an information processing device according to an embodiment. FIG. 2 is a diagram illustrating an example of the configuration of an information processing system including an information processing apparatus according to the embodiment. FIG. 3 is a diagram illustrating an example of the configuration of the information processing apparatus according to the embodiment. FIG. 4 is a diagram illustrating an example of a user information table stored in a user information storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of a trading object information table stored in the trading object information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of a content table stored in the content storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of content provided by the providing unit of the information processing device according to the embodiment. FIG. 8 is a flowchart showing an example of the flow of information processing executed by the information processing device. FIG. 9 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to the embodiment.

Below, the information processing device, information processing method, and information processing program according to the present application will be described in detail with reference to the drawings. Note that the information processing device, information processing method, and information processing program according to the present application are not limited to these embodiments. Furthermore, the embodiments can be appropriately combined as long as they do not cause inconsistencies in the processing content. Furthermore, the same parts in the following embodiments will be given the same reference numerals, and duplicated explanations will be omitted.

[1. An example of information processing]
First, an information processing method performed by an information processing device according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram for explaining an information processing method performed by an information processing device according to an embodiment.

The information processing device 1 executes information processing on trading objects traded in electronic commerce (EC) services. Here, a trading object is the use of a product or a service. Note that trading objects traded in electronic commerce services include trading objects traded in various ways, such as online shopping malls, online shopping sites, auction sites, and flea market sites.

In FIG. 1, information processing for a transaction object traded in an online shopping mall is shown as an example. Also, although only one terminal device 2 is shown in FIG. 1, the information processing device 1 provides each of the multiple terminal devices 2 with content including information corresponding to a content transmission request or the like transmitted from each of the multiple terminal devices 2.

First, the information processing device 1 acquires user information, which is information about a user, from the external device 3 (step S1). The external device 3 transmits the user information to the information processing device 1. For example, the external device 3 transmits user information including information indicating user attributes to the information processing device 1. The user attributes are, for example, demographic attributes and psychographic attributes.

The user information also includes information on the user's behavioral history. The user's behavioral history includes, for example, a browsing history of information on transaction objects in an e-commerce service such as an online shopping mall, a purchasing history of transaction objects, and the user's movement history. The user's movement history includes, for example, information detected by a sensor such as a position sensor mounted on the terminal device 2.

The information processing device 1 also acquires information on the trading object (hereinafter, sometimes referred to as trading object information) from the external device 3 (step S2). For example, the information processing device 1 acquires trading object information on trading objects traded in the online shopping mall from the external device 3. For example, the information processing device 1 acquires trading object information on trading objects being sold (listed) in the online shopping mall from the external device 3. For example, the information processing device 1 acquires trading object information on trading objects from the external device 3 at predetermined intervals.

Here, the transaction object information includes various information related to the transaction object. For example, the transaction object information includes the seller, sales price, image of the transaction object, category, title, description, payment method, sales period, number of items sold, etc.

For example, the transaction target information includes the sales history of the transaction target. For example, the transaction target information includes information such as the number of reservations, orders, sales, and order amount (sales) of the transaction target. For example, the transaction target information includes information such as the period that has passed since the date and time the transaction target was sold, the number of views of the information indicating the transaction target (number of PV (Page Views)), the number of reviews for the transaction target, and the evaluation of the transaction target.

The transaction target information may also include information on external factors related to the transaction target. For example, the transaction target information may include information on posts related to the transaction target on a social networking service (SNS). For example, the transaction target information may include posted information posted by a user on the SNS. For example, the posted information may be text information posted by a user, or image information such as images or videos. For example, the transaction target information may include information on the number of searches related to the transaction target. Note that the above is merely an example, and the transaction target information may include any information related to the transaction target.

In the above example, the information processing device 1 performs the acquisition process in two steps, step S1 and step S2. However, the information processing device 1 may simultaneously acquire user information and transaction target information from the external device 3.

Next, the information processing device 1 receives a content transmission request from the terminal device 2 (step S3). The content transmission request includes, for example, a user ID (identifier). The content transmission request is, for example, a search content transmission request, a specific content transmission request, or a top content transmission request.

The search content transmission request is, for example, a search query for searching for a transaction object, and includes search keywords. The specific content transmission request includes specification information that specifies a specific transaction object or a specific category. The top content transmission request includes, for example, information indicating the address of the top content of a shopping site.

Next, the information processing device 1 determines the granularity of the ranking based on the information of the target user who is the user with the user ID included in the content transmission request (step S4). For example, the information processing device 1 determines the granularity of the ranking based on at least one of the attributes and behavioral history of the target user.

The granularity of the ranking is the size of the target range of the ranking, and is the extraction range of the trading objects to be ranked. The size of the target range of the ranking is, for example, the size of the category of trading objects, the size of the attribute range of users who acted on the trading objects, the size of the range of users' actions on the trading objects, etc.

The "size of the category of the transaction object" mentioned above is the size of the category of the transaction object to be ranked. The categories of the transaction object are defined, for example, in a hierarchical structure, and the size of the category of the transaction object is larger the higher in the hierarchical structure, and smaller the lower in the hierarchical structure.

The above-mentioned "size of the attribute range of users who acted on the transaction target" is the size of the attribute range of users who acted on the transaction target to be ranked. The user attributes are defined by one or more attribute items. The attribute items are, for example, items related to the user's demographic attributes or psychographic attributes.

Demographic attribute items include, for example, gender, age, place of residence, and occupation, while psychographic attribute items include interests such as travel, clothes, cars, and religion, lifestyle, thoughts, and ideological tendencies. Note that some or all of the user's attributes may be unknown.

The attribute ranges of users who acted on the transaction target are, for example, male, male in his 20s, male student in his 20s, male student in his 20s living in Fukuoka City, etc. In this case, the size of the user attribute range decreases in the order of male, male in his 20s, male student in his 20s, and male student in his 20s living in Fukuoka City.

The above-mentioned "size of the range of user behavior with respect to the trading target" refers to the size of the range of user behavior (hereinafter sometimes referred to as user behavior) performed with respect to the trading target that is used as an extraction condition for ranking.

Examples of user behavior with respect to a trading object include, but are not limited to, user behavior with respect to a trading object in an online service, and user behavior with respect to an offline trading object.

The information processing device 1 determines, for example, at least one of the above-mentioned "size of the category of the transaction object," "size of the attribute range of the user who acted on the transaction object," and "size of the user's behavior range on the transaction object" as the ranking granularity based on at least one of the attributes of the target user and the behavioral history of the target user.

When it is desirable to provide more detailed ranking information, for example, as the "size of the category of the transaction object," the information processing device 1 uses the lower-level categories in the hierarchical structure as the ranking granularity, and when it is desirable to provide a rough ranking, the information processing device 1 uses the upper-level categories in the hierarchical structure as the ranking granularity.

In addition, the information processing device 1 uses, for example, the "size of the attribute range of users who acted on the transaction target" as the ranking granularity, where it is desirable to provide more detailed ranking information to the target user, a more narrowly-narrowed attribute range, and where it is desirable to provide a rough ranking, the ranking granularity is a less narrowly-narrowed or less narrowly-narrowed attribute range.

In addition, when it is desirable to provide more detailed ranking information to the target user, for example, as the "size of the user's range of behavior with respect to the transaction target," the information processing device 1 uses a more narrowed range of behavior as the ranking granularity, and when it is desirable to provide a rough ranking, the information processing device 1 uses a less narrowed or less narrowed range of behavior as the ranking granularity.

In addition, the information processing device 1 can estimate the situation of the target user based on the behavioral history of the target user. The situation of the target user is the context of the target user, and may be the situation of the target user himself/herself, the user's surrounding environment, or the intention of the target user.

In this case, the information processing device 1 can determine at least one of the above-mentioned "size of the category of the transaction object," "size of the attribute range of the user who acted on the transaction object," and "size of the range of the user's actions on the transaction object" as the ranking granularity based on the estimated situation of the target user.

Then, the information processing device 1 generates ranking information, which is information on the ranking of the granularity determined in step S4 (step S5). If there are multiple granularities determined in step S4, the information processing device 1 generates ranking information for each granularity.

The information processing device 1, for example, extracts or determines trading objects that will be ranking elements at the granularity determined in step S4, and calculates a score for each extracted or determined trading object. The information processing device 1 ranks the ranking elements using the calculated scores, and generates ranking information in which multiple ranking elements are arranged.

For example, the information processing device 1 generates ranking information by assigning higher ranks to ranking elements in descending order of score, and arranging multiple ranking elements from highest to lowest, up to a pre-set top rank.

Then, the information processing device 1 generates content including the ranking information generated in step S5 (step S6). If there are multiple pieces of ranking information generated in step S5, the content generated in step S6 includes the multiple pieces of ranking information.

Next, the information processing device 1 transmits the content generated in step S6 to the terminal device 2, thereby providing the user of the terminal device 2 with content including ranking information with granularity according to the user's attributes and behavioral history (step S7).

In the example shown in FIG. 1, the content displayed on the terminal device 2 includes three pieces of ranking information. The first piece of ranking information (ranking A) ranks products A1, A2, A3, A4, etc., the second piece of ranking information (ranking B) ranks products B1, B2, B3, B4, etc., and the third piece of ranking information (ranking C) ranks products C1, C2, C3, C4, etc. In the ranking information, the product on the far left is the top-ranked product, and products further to the right are ranked lower.

The information processing device 1 can also determine whether or not to provide ranking information based on, for example, the user's attributes or situation. For example, the information processing device 1 determines to provide ranking information when the user's attributes or situation are subject to the provision of ranking information, and determines not to provide ranking information when the user's attributes or situation are not subject to the provision of ranking information.

Examples of cases in which a user's attributes or situation are not subject to the provision of ranking information include, but are not limited to, cases in which the user's age is under 10 years old or the user's situation is in a meeting.

When the information processing device 1 determines that the ranking information should be provided, it transmits the ranking information to the terminal device 2 that has made the content transmission request, and when it determines that the ranking information should not be provided, it does not transmit the ranking information to the terminal device 2 that has made the content transmission request.

In this way, the information processing device 1 determines the granularity of the rankings based on the attributes and behavioral history of the users to whom the ranking information, which is ranking information, is to be provided, and provides the user with ranking information of the determined granularity as ranking information. This allows the information processing device 1 to provide the user with more appropriate ranking information.

The following provides a detailed explanation of the configuration of an information processing system that includes an information processing device 1, a terminal device 2, and an external device 3 that perform such processing.

2. Configuration of Information Processing System
Fig. 2 is a diagram showing an example of a configuration of an information processing system including an information processing device 1 according to an embodiment. As shown in Fig. 2, an information processing system 100 according to an embodiment includes the information processing device 1, a plurality of terminal devices 2, and an external device 3.

The information processing device 1, the terminal device 2, and the external device 3 are connected to a network N, and can transmit and receive information to and from each other via the network N. The network N is, for example, a network including a WAN (Wide Area Network) such as the Internet. Note that the network N is, for example, a configuration including a mobile communication system such as 4G (4th Generation) or 5G (5th Generation), but is not limited to such examples.

Each terminal device 2 is a terminal device operated by a user who uses the service provided by the information processing device 1, and is, for example, a personal computer, a tablet, a PDA (Personal Digital Assistant), or a smartphone. Note that the terminal device 2 is not limited to the above examples, and may be, for example, a smart watch or a wearable device.

The external device 3 is an information processing device that provides various information, and is realized, for example, by a server device or a cloud system. For example, the external device 3 provides user information as various information. As another example, the external device 3 provides information on the subject of a transaction in an online shopping mall, an internet shopping site, a flea market site, an auction site, a reservation site for travel or restaurants, a credit card contract site, a financial product provision site, etc.

The information processing device 1 is an information processing device capable of communicating with various devices via a predetermined network N such as the Internet, and is realized, for example, by a server device or a cloud system. For example, the information processing device 1 is connected to various other devices via the network N so as to be able to communicate with them.

3. Configuration of information processing device 1
Hereinafter, an example of the functional configuration of the above-mentioned information processing device 1 will be described. Fig. 3 is a diagram showing an example of the configuration of the information processing device 1 according to the embodiment. As shown in Fig. 3, the information processing device 1 has a communication unit 10, a storage unit 11, and a processing unit 12.

3.1. Communication unit 10
The communication unit 10 is realized by, for example, a network interface card (NIC). The communication unit 10 is connected to a network N by wire or wirelessly, and transmits and receives information to and from various other devices. For example, the communication unit 10 transmits and receives information to and from each of the terminal device 2 and the external device 3 via the network N.

[3.2. Storage unit 11]
The storage unit 11 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 11 also has a user information storage unit 20, a transaction object information storage unit 21, and a content storage unit 22.

[3.2.1. User information storage unit 20]
The user information storage unit 20 stores various user information related to the user. Fig. 4 is a diagram showing an example of a user information table stored in the user information storage unit 20 according to the embodiment. In the example shown in Fig. 4, the user information table stored in the user information storage unit 20 includes information in which a "user ID" and "user information" are associated with each other for each user. The "user information" includes "attribute information" and "behavioral history", etc.

"User ID" is an identifier that identifies a user. "Attribute information" is information that indicates the attributes of a user associated with a user ID. User attributes include, but are not limited to, demographic attributes such as gender, age, place of residence, and occupation, and psychographic attributes such as interests and concerns such as travel, clothing, cars, and religion, lifestyle, thoughts, and ideological tendencies.

"Behavioral history" is information that indicates the behavioral history of a user, and includes, for example, information such as purchase history, browsing history, and movement history. Purchase history is a history of trading objects purchased by a user. For example, purchase history includes information on trading objects purchased by a user, the type of trading object purchased by a user, the number of times the trading object was purchased by a user, and information on the date and time when the trading object was purchased by a user.

The browsing history is a history of trading objects whose content was viewed by the user, and includes information indicating the viewed trading objects, information indicating the category of the viewed trading objects, and information indicating the date and time when the trading objects were viewed. For example, the browsing history is a history of the content of the trading objects displayed on the user's terminal device 2. The content of the trading object may be content explaining the details of the trading object, content for purchasing the trading object, advertising content for the trading object, etc.

The movement history is, for example, information indicating the movement history of the user, and includes information detected by a sensor such as a position sensor mounted on the terminal device 2. For example, the movement history includes information indicating the user's position per unit time, information on stores visited by the user, etc.

"Behavioral history" also includes information indicating the rental of the transaction object, information indicating a trial use of the transaction object, information indicating the acquisition of a brochure for the transaction object, information indicating a search history for the transaction object, information indicating a history of adding the transaction object to a shopping cart, and information indicating a history of adding the transaction object to favorites.

In FIG. 4, the user "U1" identified by the user ID has attribute information "CH1" and behavior history "PH1." Note that in the example shown in FIG. 4, the attribute information, etc. is expressed as an abstract code such as "CH1," but the attribute information, etc. is data in a file format that includes various information such as specific numerical values and specific character strings, or information indicating the storage location of such data. The attribute information, etc. may be information in a format other than the above.

[3.2.2. Trading object information storage unit 21]
The transaction object information storage unit 21 stores various transaction object information related to transaction objects. Fig. 5 is a diagram showing an example of a transaction object information table stored in the transaction object information storage unit 21 according to the embodiment.

In the example shown in FIG. 5, the transaction object information table stored in the transaction object information storage unit 21 includes information in which "transaction object ID", "transaction object information", and "category" are associated with each other for each transaction object. Note that in FIG. 5, in order to explain "category", it is described as information separate from "transaction object information", but category information may be included in the transaction object information.

The "trading object ID" is an identifier that identifies the trading object. The "trading object information" is the trading object information of the trading object identified by the trading object ID. The trading object information includes, for example, the sales history of the trading object, the number of reservations, the number of orders, the number of sales, the order amount (sales), the period that has elapsed since the date and time when the trading object was sold, the number of views (PV (number)) of the information indicating the trading object, the number of reviews for the trading object, and the evaluation of the trading object, for example, for each user attribute. In addition, the trading object information may include information on external factors related to the trading object, as described above.

"Category" is information about the category to which the transaction object identified by the transaction object ID belongs. For example, if categories are defined in a hierarchical structure (e.g., a tree-like hierarchical structure), "Category" includes information about the lowest-level category to which the transaction object belongs, or information about each category from the highest-level category to the lowest-level category to which the transaction object belongs.

In FIG. 5, the transaction object "M1" identified by the transaction object ID has transaction object information "MD1" and a category "CT1". Note that in the example shown in FIG. 5, the transaction object information and category are expressed as abstract codes such as "MD1" and "CT1", but these are, for example, data in a file format that includes various information such as specific character strings and specific numerical values, or information indicating the storage location of such data. The transaction object information and category may be information in a format other than those mentioned above.

The transaction target information storage unit 21 may store various information according to the purpose, not limited to the above. For example, the transaction target information storage unit 21 may store information such as the score or the number of times displayed corresponding to each transaction target.

[3.2.3. Content storage unit 22]
The content storage unit 22 stores information related to content. Fig. 6 is a diagram showing an example of a content table stored in the content storage unit 22 according to the embodiment. In the example shown in Fig. 6, the content table stored in the content storage unit 22 includes information in which a "content ID" and a "content" are associated with each other for each piece of content.

"Content ID" is an identifier that identifies the content. "Content" is information about the content associated with the content ID. Specifically, content may indicate information about the content.

For example, the content is content related to a portal site. As another example, the content may be content related to a web search site, a news site, an auction site, a weather forecast site, a shopping site, a finance (stock price) site, etc. The content may also be content related to a route search site, a map providing site, a travel site, a restaurant introduction site, a web blog site, a SNS site, etc.

In FIG. 6, "C1" identified by the content ID has content "CO1". Note that in the example shown in FIG. 6, the content is expressed by an abstract code such as "CO1", but the content is data in a file format that includes various information such as specific numerical values and specific character strings, or information indicating the storage location of such data. The content may be information in a format other than the above. The content storage unit 22 is not limited to the above, and may store various information according to the purpose.

3.3. Processing section 12
The processing unit 12 is a controller, and is realized by a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) executing various programs (an example of an information processing program) stored in a storage device inside the information processing device 1 using a RAM as a working area. The processing unit 12 is also a controller, and is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 3, the processing unit 12 has an acquisition unit 30, a reception unit 31, a learning unit 32, a determination unit 33, a judgment unit 34, a generation unit 35, and a provision unit 36, and realizes or executes the functions and actions of the information processing described below. Note that the internal configuration of the processing unit 12 is not limited to the configuration shown in FIG. 3, and may be other configurations as long as they perform the information processing described below.

[3.3.1. Acquisition unit 30]
The acquisition unit 30 acquires various pieces of information. The acquisition unit 30 acquires various pieces of information from the storage unit 11. The acquisition unit 30 acquires various pieces of information from the user information storage unit 20, the transaction target information storage unit 21, the content storage unit 22, etc.

The acquisition unit 30 receives various information from an external information processing device via the communication unit 10. The acquisition unit 30 receives various information from the terminal device 2 or the external device 3. For example, the acquisition unit 30 acquires user information related to the user from the external device 3.

The acquisition unit 30 acquires user information, such as information indicating the user's attributes and information indicating the user's behavior history, from the external device 3. The acquisition unit 30 then stores the user information in the user information storage unit 20. For example, the acquisition unit 30 acquires user information from the external device 3 at predetermined intervals and updates the user information stored in the user information storage unit 20.

The acquisition unit 30 also acquires transaction target information from the external device 3. The acquisition unit 30 stores the transaction target information related to the transaction target in the transaction target information storage unit 21. For example, the acquisition unit 30 acquires the transaction target information from the external device 3 at predetermined intervals and updates the transaction target information stored in the transaction target information storage unit 21.

3.3.2. Reception unit 31
The reception unit 31 receives various requests. The reception unit 31 receives various requests from an external information processing device. The reception unit 31 receives information indicating the various requests from the external information processing device via the communication unit 10. For example, the reception unit 31 receives a request from the terminal device 2 or the external device 3.

The reception unit 31 receives, for example, a content transmission request from the terminal device 2. The content transmission request includes, for example, a user ID. The content transmission request is, for example, a search content transmission request, a specific content transmission request, or a top content transmission request.

The search content transmission request is, for example, a search query for searching for a transaction target, and includes search keywords, etc. The search content transmission request may also be any query, such as a search query for searching for news, a search query for searching for a person, or a search query for searching for a paper.

The specific content transmission request includes designation information that designates a specific transaction subject or a specific category. The specific content transmission request may also include designation information that designates, for example, a subject other than the transaction subject or a category of subjects other than the transaction subject.

The top content transmission request includes, for example, information indicating the address of the top content of a shopping site. The top content transmission request may also include information indicating the address of a site other than a shopping site, such as a web search site, a news site, or an auction site.

3.3.3. Learning unit 32
The learning unit 32 uses the learning information to generate a learning model that receives user information as input and outputs a score for each ranking granularity. For example, the learning unit 32 uses the learning information to generate a learning model that receives user information as input and outputs a score for each ranking target range size.

For example, the learning unit 32 generates a learning model that estimates the granularity of rankings that will result in high conversions, impressions, and the like for a target user. For example, the learning unit 32 generates a learning model using learning information that includes information on a providing user who is a user to whom ranking information has been provided, and information on the size of the target range of ranking information provided to the providing user in which the providing user acted on a ranked trading target.

The user information is one or more of information indicating the user's attributes, information indicating the user's behavior history, and information indicating the user's situation. The size of the target range of the ranking is, for example, at least one of the size of the category of the transaction object, the size of the attribute range of the user who acted on the transaction object, and the size of the user's behavior range on the transaction object, and may be referred to as the target range size below.

The behavior of the providing user with respect to a trading object ranked in the ranking information may be, for example, purchasing or viewing the trading object ranked in the ranking information, or may be other behavior.

The learning model is generated by machine learning using a neural network such as a convolutional neural network. However, without being limited to this example, the learning model may be generated using machine learning using a learning algorithm such as linear regression, nonlinear regression, logistic regression, or a support vector machine instead of a neural network.

3.3.4. Determination unit 33
The determination unit 33 determines the granularity of the ranking based on at least one of the attributes and the behavioral history of the users to whom the ranking information, which is information on the ranking, is to be provided.

For example, when a content transmission request is received by the reception unit 31, the determination unit 33 determines the granularity of the ranking based on at least one of the attributes and behavior history of the target user, who is the user of the user ID included in the content transmission request. Note that the information included in the content transmission request is not limited to the user ID as long as it can identify the target user.

The determination unit 33 includes an estimation unit 40 that estimates the target user's situation based on the target user's behavioral history, and a determination processing unit 41 that determines the granularity of the ranking based on at least one of the target user's attributes, the target user's behavioral history, and the target user's situation estimated by the estimation unit 40.

[3.3.4.1. Estimation unit 40]
The estimation unit 40 estimates the situation of the target user based on the user's behavior history. The situation of the target user is the context of the target user, and is the situation of the target user himself, the user's surrounding environment, or the intention of the target user, and for example, the types of the situation of the target user include the situation of the target user over a relatively long period of time, the situation of the target user over a relatively short period of time, and the momentary situation of the target user.

Examples of the target user's status over a relatively long period of time include, but are not limited to, a daily life state, which is a state of daily living, a travel state, a business trip state, a hospitalization state, etc.

Examples of the target user's status for a relatively short period of time include, but are not limited to, a working state, a commuting state, a eating state, a bathing state, a shopping state, or a sleeping state.

The momentary state of the target user is, for example, the motion state of the target user, the operation state of the terminal device 2 by the target user, the state of the target user's surrounding environment, etc. The motion state of the target user is, for example, a stationary state, a moving state, etc.

The estimation unit 40 has, for example, condition information that specifies behavioral conditions for each user situation, and estimates the user situation based on the user's behavior history and the condition information. For example, the estimation unit 40 determines, based on the user's behavior history and the condition information, a situation that satisfies the behavioral conditions indicated in the condition information as the user situation.

The estimation unit 40 can also determine the user's situation using a learning model instead of condition information that specifies the behavioral conditions. The learning model is a user situation estimation model that estimates the user's situation, and is a model that inputs the behavioral history of the target user and outputs a score for each situation. The estimation unit 40 can generate a user situation estimation model using learning information that includes, for example, the user's behavioral history and a situation that indicates the user's situation.

The user situation estimation model is generated, for example, by machine learning using a neural network such as a convolutional neural network. Note that, without being limited to this example, the user situation estimation model may be generated using machine learning using a learning algorithm such as linear regression, nonlinear regression, logistic regression, or a support vector machine instead of a neural network.

[3.3.4.2. Decision Processing Unit 41]
The determination processing unit 41 determines the granularity of the ranking based on at least one of the attributes of the target user, the behavioral history of the target user, and the situation of the target user estimated by the estimation unit 40.

As described above, the granularity of the ranking is the size of the target range of the ranking, and is the extraction range of trading objects that are the subject of the ranking. As described above, the size of the target range of the ranking is, for example, the size of the category of trading objects, the size of the attribute range of users who acted on trading objects, the size of the range of users' actions on trading objects, etc.

The determination processing unit 41 determines at least one of the above-mentioned "size of the category of the transaction object," "size of the attribute range of the user who acted on the transaction object," and "size of the user's behavior range on the transaction object" as the ranking granularity based on, for example, at least one of the target user's attributes, the target user's behavior history, and the target user's situation.

For example, when it is desirable to provide more detailed ranking information as the "size of the category of the transaction object," the decision processing unit 41 uses the lower-level categories in the hierarchical structure as the ranking granularity, and when it is desirable to provide a rough ranking, the decision processing unit 41 uses the upper-level categories in the hierarchical structure as the ranking granularity.

In addition, the decision processing unit 41, for example, uses a highly narrowed attribute range as the ranking granularity when it is desirable to provide more detailed ranking information to the target user as the "size of the attribute range of users who acted on the transaction target," and uses a narrowed or less narrowed attribute range as the ranking granularity when it is desirable to provide a rough ranking.

In addition, the decision processing unit 41, for example, uses a highly narrowed range of behavior as the ranking granularity when it is desirable to provide more detailed ranking information to the target user as the "size of the user's range of behavior with respect to the transaction target," and uses a narrowed or less narrowed range of behavior as the ranking granularity when it is desirable to provide a rough ranking.

The process of determining the ranking granularity in the determination processing unit 41 will be described in more detail below. First, a method of determining the ranking granularity based on the attributes of the target user will be described. The determination processing unit 41 determines the above-mentioned "size of the category of the transaction object" as the ranking granularity based on the attributes of the target user.

For example, the decision processing unit 41 decides whether the granularity of the ranking should be the first level category, the second level category, or the third level category, depending on the attributes of the user. The categories of the objects of trade are defined, for example, in a tree-like hierarchical structure, and the first level categories are, for example, "fashion," "home appliances," "television, audio, cameras," and "furniture, interiors."

If the target user is a user who has an interest in fashion, the determination processing unit 41 determines that a category related to fashion is to be ranked. Also, if the target user is a user who has an interest in home appliances, the determination processing unit 41 determines that a category related to home appliances is to be ranked. The user's interests are indicated, for example, by the psychographic attributes described above.

Here, the target users are users who have an interest in fashion, and the first-level category "fashion" includes, for example, second-level categories "men's fashion" and "women's fashion." In addition, the second-level category "men's fashion" includes third-level categories "T-shirts," "casual shirts," "chinos," "jeans," "parkas," and "loafers."

For example, if it is not known whether the target user is interested in "men's fashion" or "women's fashion," the decision processing unit 41 determines that the ranking granularity will be the first-level category, "fashion." For example, if the target user is a user whose gender, age, occupation, etc. are unknown, the decision processing unit 41 determines that it is not known whether the target user is interested in "men's fashion" or "women's fashion."

Furthermore, when gender is known as an attribute of the target user, the decision processing unit 41 determines either "men's fashion" or "women's fashion" as the ranking granularity based on the attribute of the target user (gender in this case). For example, when the target user is male, the decision processing unit 41 determines "men's fashion" as the ranking granularity, and when the target user is female, the decision processing unit 41 determines "women's fashion" as the ranking granularity.

In addition, when the attributes of the target user, such as gender, age, place of residence, and occupation, are known, the determination processing unit 41 determines the ranking granularity to be the third-level category that is most popular among other users with attributes that match or are similar to the attributes possessed by the user.

For example, assume that the most popular lower-level category among users who are male, in their 20s, from Kyoto City, and students is the third-level category "casual shirts." In this case, when the attributes of the target user are male, in their 20s, from Kyoto City, and students, the determination processing unit 41 determines the third-level category "casual shirts" as the ranking granularity.

The determination processing unit 41 also determines the above-mentioned "size of the attribute range of users who acted on the transaction target" as the ranking granularity in addition to or instead of the above-mentioned "size of category" based on the user attributes.

As described above, the user's behavior with respect to the transaction target includes the user's behavior with respect to the transaction target in online services, the user's behavior with respect to the transaction target offline, etc. In the following, the attribute range of the size determined by the determination processing unit 41 may be referred to as the target attribute range.

Examples of user actions regarding a transaction target in an online service include, but are not limited to, purchasing the transaction target, searching for the transaction target, viewing the sales page for the transaction target, adding the transaction target to a shopping cart, registering the transaction target as a favorite, and selecting (clicking) on an advertisement for the transaction target.

A user's action with respect to a transaction object in an offline service may be, for example, purchasing the transaction object, renting the transaction object, trying out the transaction object, obtaining a pamphlet for the transaction object, etc., but is not limited to such examples. A user's action with respect to a transaction object may be one of the actions described above (e.g., purchasing), or may include two actions (e.g., purchasing and viewing), or two or more actions.

The determination processing unit 41, for example, determines an attribute range that matches or is similar to the attributes of the target user as the target attribute range. For example, if the attribute of the target user is male and other attributes are unknown, the determination processing unit 41 determines male as the target attribute range. Furthermore, if the attribute of the target user is male students in their twenties, the determination processing unit 41 determines male students in their twenties, students in their twenties, etc. as the target attribute range.

The determination processing unit 41 may also have attribute range information, which is information in which an attribute range is associated with each attribute. In this case, the determination processing unit 41 determines the target attribute range based on the attribute range information. Specifically, the determination processing unit 41 extracts an attribute range that corresponds to the attribute of the target user from the attribute range information, and determines the extracted attribute range as the target attribute range.

For example, in the attribute range information, an attribute range including attributes dissimilar to the target user's attributes can be predefined for the target user's attributes. This allows the determination processing unit 41 to determine the attribute range including attributes dissimilar to the target user's attributes as the target attribute range.

The determination processing unit 41 also determines the granularity of the ranking based on the above-mentioned "size of the user's range of behavior with respect to the transaction target based on the user's attributes" in addition to or instead of at least one of the above-mentioned "size of the category" and "size of the attribute range of the user who acted with respect to the transaction target" based on the user's attributes.

The range of user behavior may be, for example, a range that includes all of the user behaviors described above, a range of behaviors regarding transaction objects in online services, a range of user behaviors regarding transaction objects offline, a range of behaviors that include searching for transaction objects in online services, a range of behaviors that include purchasing transaction objects in online services, etc.

The size of a user's range of behavior decreases in the following order: the range of user behavior with respect to transaction objects in online services, the range of behavior involving searching for and purchasing transaction objects online, and the range of behavior involving purchasing transaction objects online. In what follows, the range of user behavior may be referred to as the target behavior range.

The determination processing unit 41 has, for example, activity range information, which is information in which activity ranges are associated with each attribute, and determines the target activity range corresponding to the attributes of the target user as the size of the user's activity range. Specifically, the determination processing unit 41 extracts the activity range corresponding to the attributes of the target user from the activity range information, and determines the extracted activity range as the target activity range.

For example, when the gender of the target user is unknown, the determination processing unit 41 determines the size of the user's range of behavior to be the range of the user's behavior with respect to transaction objects in online services. Also, when the gender of the target user is male, the determination processing unit 41 determines the size of the user's range of behavior to be the range of behavior involving searching for or purchasing transaction objects online. Also, when the gender of the target user is female, the determination processing unit 41 determines the size of the user's range of behavior to be the range of behavior involving purchasing transaction objects online.

In addition, when the content transmission request is a search query (searched content transmission request), the determination processing unit 41 can determine the size of the category based on the search keywords included in the search query instead of the attributes of the target user.

For example, if the keyword included in the search query is "fashion", the determination processing unit 41 determines the category "fashion" as the ranking granularity. Also, if the keywords included in the search query are "men's fashion" and "fashion men's", the determination processing unit 41 determines the category "men's fashion" as the ranking granularity.

Furthermore, when the content transmission request is a specific content transmission request, the determination processing unit 41 can determine the size of the category based on the designation information included in the specific content transmission request instead of the attributes of the target user. For example, when the designation information is information designating a specific trading object, the determination processing unit 41 determines the category that includes the specific trading object as the size of the category. Furthermore, when the designation information is information designating a specific category, the determination processing unit 41 determines the specific category as the size of the category.

Next, we will explain how to determine the granularity of the ranking based on the behavioral history of the target user. The determination processing unit 41 determines the above-mentioned "size of the category of the transaction object" as the granularity of the ranking based on the behavioral history of the target user.

When the content transmission request is a search query and the search keyword included in the search query is information that identifies a trading object, the determination processing unit 41 determines, based on the target user's behavioral history, a category or specific trading object that includes the specific trading object that is the trading object identified by the search keyword, as the ranking granularity.

For example, based on the behavioral history of the target user, the determination processing unit 41 determines the ranking granularity to be the lower-level category or specific transaction object, the greater the number of times the transaction object is viewed or purchased in the lowest-level category (hereinafter, sometimes referred to as the target category) that includes the specific transaction object.

For example, if the number of times a transaction object included in the target category has been viewed or purchased is less than a first threshold, the determination processing unit 41 determines a category higher than the target category as the ranking granularity. Note that the smaller the number of times a transaction object included in the target category has been viewed or purchased is less than the first threshold, the more the determination processing unit 41 determines a higher category as the ranking granularity.

In addition, the determination processing unit 41 determines the target category as the ranking granularity, for example, if the number of views or purchases of a trading object included in the target category is equal to or greater than the first threshold and less than the second threshold. In addition, the determination processing unit 41 determines a specific trading object as the ranking granularity, for example, if the number of views or purchases of a trading object included in the target category is equal to or greater than the second threshold. The first threshold is, for example, 2, and the second threshold is, for example, 10.

In addition, the determination processing unit 41 can also determine the target category as the ranking granularity, for example, if the number of views or the number of purchases of a transaction object included in the target category is equal to or greater than the second threshold, but the number of views or the number of purchases of a specific transaction object is equal to or less than the third threshold.

In addition, when the content transmission request is a search query and the search keyword included in the search query is information that identifies a category, the decision processing unit 41 determines the granularity of the ranking by using the category identified by the search keyword as the reference category.

For example, if the number of times a transaction object included in the reference category is viewed or purchased is within a threshold range, the determination processing unit 41 determines the reference category as the ranking granularity. Also, for example, the more the number of times a transaction object included in the reference category is viewed or purchased than the threshold range, the more the determination processing unit 41 determines a category lower than the reference category as the ranking granularity. Also, for example, the more the number of times a transaction object included in the reference category is viewed or purchased than the threshold range, the more the determination processing unit 41 determines a category higher than the reference category as the ranking granularity.

The determination processing unit 41 also determines the above-mentioned "size of the attribute range of users who acted on the transaction target" as the ranking granularity in addition to or instead of the above-mentioned "size of category" based on the behavioral history of the target user.

For example, the determination processing unit 41 determines the range of attributes of users whose behavior toward a specific transaction object or a transaction object included in the reference category matches the behavior of the target user as the target attribute range. Hereinafter, a transaction object included in the specific transaction object or the reference category may be referred to as a target transaction object.

For example, when the target user's behavior with respect to the target transaction object is a purchase, the determination processing unit 41 determines the attribute range including the attributes of users who have a history of purchasing the target transaction object as the target attribute range. The target attribute range does not include attributes other than the attributes of users who have a history of purchasing the target transaction object.

For example, if the attribute of a user who has a history of purchasing the target transaction object is a male student in his 20s, then the target attribute range is determined to be male students in their 20s and students in their 20s. Also, for example, if the attribute of a user who has a history of purchasing the target transaction object is a female office worker in her 30s, then the target attribute range can be determined to be women in their 30s and office workers in their 30s.

The determination processing unit 41 may also have attribute range information, which is information in which an attribute range is associated with each behavior. In this case, the determination processing unit 41 determines the attribute range corresponding to the target user's behavior with respect to the target transaction object as the target attribute range.

The determination processing unit 41 also determines the granularity of the ranking to be the above-mentioned "size of the user's range of behavior with respect to the transaction target based on the user's attributes" in addition to or instead of at least one of the above-mentioned "size of the category" and the above-mentioned "size of the attribute range of the user who acted with respect to the transaction target" based on the user's behavior history.

For example, the determination processing unit 41 determines a target behavior range for each behavior in advance, and determines the target behavior range corresponding to the behavior history of the target user as the size of the user's behavior range.

The determination processing unit 41 can determine the granularity of the ranking based on the target user's situation estimated by the estimation unit 40, instead of the target user's behavioral history. For example, the determination processing unit 41 has information in which the "size of the category of the transaction object" is previously associated with each user's situation, and determines the size of the category of the transaction object according to the target user's situation estimated by the estimation unit 40 as the granularity of the ranking.

The determination processing unit 41 also determines the above-mentioned "size of the attribute range of users who acted on the transaction target" as the ranking granularity in addition to or instead of the above-mentioned "size of the category" based on the situation of the target user estimated by the estimation unit 40.

For example, the determination processing unit 41 has information that pre-associates the "size of the attribute range of the user who acted on the transaction target" with each user's situation, and determines the "size of the attribute range of the user who acted on the transaction target" according to the target user's situation as the ranking granularity.

The determination processing unit 41 also determines the above-mentioned "size of the user's range of behavior with respect to the transaction target based on the user's attributes" as the ranking granularity in addition to or instead of at least one of the above-mentioned "size of the category" and the above-mentioned "size of the attribute range of the user who acted with respect to the transaction target" based on the user's situation estimated by the estimation unit 40.

For example, the determination processing unit 41 has information that pre-associates the "size of the user's range of action with respect to the transaction target" with each user's situation, and determines the "size of the user's range of action with respect to the transaction target" according to the target user's situation as the ranking granularity.

The determination processing unit 41 can also determine the granularity of the rankings based on, for example, two or more of the attributes of the target user, the behavioral history of the target user, and the user's situation.

In this case, the decision processing unit 41 has correspondence information in which the "size of the ranking target range" is pre-associated with each combination of two or more of the target user's attributes, the target user's behavioral history, and the user's situation, for example.

Based on the corresponding information, the determination processing unit 41 determines the "size of the ranking target range" as the ranking granularity according to a combination of two or more of the target user's attributes, the target user's behavioral history, and the user's situation.

The "size of the ranking target range" associated with each of the above combinations is, for example, at least one of the following: "size of the category of the trading object," "size of the attribute range of users who acted on the trading object," "size of the range of user actions on the trading object," etc.

In addition, instead of determining the ranking granularity on a rule basis, the determination processing unit 41 can also determine the ranking granularity using a learning model generated by the learning unit 32. The determination processing unit 41 can also determine the ranking granularity by using both the rule base and the learning model.

For example, the determination processing unit 41 inputs the target user's information into the learning model generated by the learning unit 32, and determines the target range size to be used as the ranking granularity based on the score for each target range size output from the learning model. The target user's information is one or more of information indicating the attributes of the target user, information indicating the target user's behavioral history, and information indicating the target user's situation.

The determination processing unit 41, for example, determines the highest target range size among the scores for each target range size output from the learning model as the ranking granularity. The target range size includes at least one of the size of the category of the transaction object, the size of the attribute range of the user who acted on the transaction object, and the size of the user's action range on the transaction object.

The determination processing unit 41 can also determine, for example, one or more target range sizes for which the score for each target range size output from the learning model is equal to or greater than a threshold value as the ranking granularity. The determination processing unit 41 can also determine, for example, a predetermined number of target range sizes in descending order of the scores for each target range size output from the learning model as the ranking granularity.

The decision processing unit 41 can select a category randomly or based on preset rules, for example, when the content transmission request is not a search query or is not a request to specify a specific transaction target. In this case, the decision processing unit 41 can select, for example, two or more categories randomly or based on preset rules.

The decision processing unit 41 can also select a category randomly or based on preset rules even when the content transmission request is a search query or a request to identify a specific transaction object.

The determination processing unit 41 can also determine a display format according to the determined granularity. The display format is the display format of the ranking information. The display format of the ranking information is the arrangement format of multiple ranking elements that make up the ranking information, and can be a list format, a grid format, or the like.

The list format is a display format in which multiple ranking elements that make up the ranking information are arranged in a row, and the grid format is a display format in which multiple ranking elements are arranged in a matrix of n rows and m columns. n and m are integers of 2 or greater.

When the determination processing unit 41 determines that there are multiple granularities and that specific transaction objects and categories are mixed, the determination processing unit 41 can use the category including the specific transaction object as the ranking granularity instead of the specific transaction object. This allows the determination processing unit 41 to smooth out the ranking granularity.

[3.3.5. Judgment unit 34]
The determination unit 34 determines whether or not to provide ranking information based on the attributes or situation of the target user.

For example, the determination unit 34 determines to provide ranking information when the user's attributes or situation are subject to the provision of ranking information, and determines not to provide ranking information when the user's attributes or situation are not subject to the provision of ranking information.

Examples of cases in which a user's attributes or situation are not subject to the provision of ranking information include, but are not limited to, cases in which the user's age is under 10 years old or the user's situation is in a meeting.

The determination unit 34 can also determine, for example, whether or not to provide ranking information for each ranking granularity determined by the determination unit 33.

[3.3.6. Generation unit 35]
The generating unit 35 executes a generating process for generating various information. For example, the generating unit 35 executes the generating process based on various information acquired by the acquiring unit 30, various information stored in the storage unit 11, or various information received from an external information processing device. When the determining unit 34 determines that ranking information should be provided, the generating unit 35 generates ranking information of the granularity determined by the determining unit 33.

The generation unit 35, for example, determines or identifies a trading object that corresponds to the granularity determined by the determination unit 33, and calculates the score of the trading object using a function (score function) that receives trading object information of the determined or identified trading object as input and outputs a score of the trading object.

For example, the generation unit 35 calculates the score of the ranking target using a value (hereinafter, sometimes referred to as a "partial score") obtained by multiplying each of multiple elements of the ranking target, such as the number of orders, order amount, number of views (PV count), by a weight (coefficient) for each element. For example, the generation unit 35 calculates the sum of the partial scores of each of the multiple elements of the ranking target as the score of the ranking target. The generation unit 35 can also calculate the average of the partial scores of each of the multiple elements of the ranking target as the score of the ranking target.

Note that the above is merely an example, and the generation unit 35 can use any information to calculate the scores of the ranking elements. The generation unit 35 can also correct the calculated scores of the ranking elements by using a coefficient based on the evaluation of the seller (store) selling the transaction object. The generation unit 35 can also correct the scores of the ranking elements by multiplying the calculated scores of the ranking elements by a coefficient based on the evaluation of the seller selling the transaction object.

When the granularity determined by the determination unit 33 is one, the generation unit 35 can also generate multiple types of ranking information each ranked at the same granularity. For example, the multiple types of ranking information have different top-level categories (the first hierarchical level described above) and the same hierarchy (e.g., the second or third hierarchical level).

When the granularity determined by the determination unit 33 is a specific transaction target, the generation unit 35 calculates a store score, which is a score for each store, based on the sales volume, evaluation value, number of views, etc. of the specific transaction target at each store. The generation unit 35 generates ranking information that ranks the specific transaction targets in descending order of store score, for example.

The generation unit 35 ranks the trading objects using the calculated scores and generates ranking information in which the multiple trading objects are arranged in the display format determined by the determination unit 33. When there are multiple pieces of ranking information determined by the determination unit 34 to be provided, the generation unit 35 generates multiple pieces of ranking information.

The generation unit 35 generates content that includes the generated one or more pieces of ranking information. For example, the generation unit 35 generates content that includes the one or more pieces of ranking information by appropriately using various techniques related to image generation, image processing, and the like.

The generation unit 35 generates content to be provided to the user of the terminal device 2 by appropriately using various technologies such as Java (registered trademark). The generation unit 35 can generate content to be provided to the user of the terminal device 2 based on the format of CSS, JavaScript (registered trademark), or HTML. The generation unit 35 can also generate content in various formats such as JPEG (Joint Photographic Experts Group), GIF (Graphics Interchange Format), and PNG (Portable Network Graphics).

[3.3.7. Providing Department 36]
The providing unit 36 provides various information. The providing unit 36 transmits various information to an external information processing device via the communication unit 10 and the network N. The providing unit 36 transmits various information to the terminal device 2 or the external device 3. The providing unit 36 transmits content to the terminal device 2. The providing unit 36 transmits information related to the object of transaction to the terminal device 2.

For example, when the determination unit 34 determines that ranking information should be provided, the provision unit 36 transmits the content generated by the generation unit 35 to the terminal device 2 via the communication unit 10 and the network N, thereby providing the content generated by the generation unit 35 and including one or more pieces of ranking information to the user.

When the determination unit 34 determines that ranking information should not be provided, the information processing device 1 does not transmit content including ranking information to the terminal device 2 that has requested the content to be transmitted, but transmits content not including ranking information to the terminal device 2.

FIG. 7 is a diagram showing an example of content provided by the providing unit 36 of the information processing device 1 according to the embodiment. Content 50 shown in FIG. 7 includes a search frame 51 and ranking information 52a, 52b, and 52c. Search frame 51 includes a frame for inputting search keywords and a button for submitting a search query.

Ranking information 52a is ranking information for the third hierarchical level category "men's sneakers," ranking information 52b is ranking information for the third hierarchical level category "red wine," and ranking information 52c is ranking information for the third hierarchical level category "washing machine."

In this way, the content 50 shown in FIG. 7 contains multiple types of ranking information that are each ranked in categories of the same granularity (categories in the same third hierarchical level). Therefore, the user can understand the multiple ranking information 52a, 52b, and 52c without feeling uncomfortable.

4. Processing Procedure
Next, the procedure of information processing executed by the information processing device 1 according to the embodiment will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of the flow of information processing executed by the information processing device 1.

As shown in FIG. 8, the processing unit 12 of the information processing device 1 accepts a content transmission request from the terminal device 2 via the network N and the communication unit 10 (step S10). The processing unit 12 determines the granularity of ranking based on the information of the target user who is the user with the user ID included in the content transmission request accepted in step S10 (step S11).

Next, the processing unit 12 generates ranking information with the granularity determined in step S11 (step S12). Then, the processing unit 12 generates content including the ranking information generated in step S12 (step S13).

Next, the processing unit 12 provides the content generated in step S13 to the user of the terminal device 2 by transmitting the content generated in step S13 to the terminal device 2 (step S14), and ends the process shown in FIG. 8.

5. Modifications
In the above example, trading objects are used as ranking targets, but the ranking targets are not limited to trading objects. For example, the ranking targets may be objects other than trading objects.

6. Hardware Configuration
The information processing device 1 or the terminal device 2 according to the embodiment described above is realized by a computer 80 having a configuration as shown in Fig. 9, for example. The information processing device 1 will be described below as an example. Fig. 9 is a hardware configuration diagram showing an example of a computer 80 that realizes the functions of the information processing device 1 according to the embodiment. The computer 80 has a CPU 81, a RAM 82, a ROM (Read Only Memory) 83, a HDD (Hard Disk Drive) 84, a communication interface (I/F) 85, an input/output interface (I/F) 86, and a media interface (I/F) 87.

The CPU 81 operates based on the programs stored in the ROM 83 or the HDD 84, and controls each part. The ROM 83 stores a boot program executed by the CPU 81 when the computer 80 starts up, and programs that depend on the hardware of the computer 80.

The HDD 84 stores programs executed by the CPU 81 and data used by such programs. The communication interface 85 receives data from other devices via the network N (see FIG. 2) and sends it to the CPU 81, and transmits data generated by the CPU 81 to other devices via the network N.

The CPU 81 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse, via the input/output interface 86. The CPU 81 acquires data from the input devices via the input/output interface 86. The CPU 81 also outputs data generated via the input/output interface 86 to the output devices.

The media interface 87 reads a program or data stored in the recording medium 88 and provides it to the CPU 81 via the RAM 82. The CPU 81 loads the program from the recording medium 88 onto the RAM 82 via the media interface 87 and executes the loaded program. The recording medium 88 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

For example, when the computer 80 functions as the information processing device 1 according to the embodiment, the CPU 81 of the computer 80 executes a program loaded onto the RAM 82 to realize the functions of the processing unit 12. In addition, the data in the storage unit 11 is stored in the HDD 84. The CPU 81 of the computer 80 reads and executes these programs from the recording medium 88, but as another example, these programs may be obtained from another device via the network N.

[7. Other]
In addition, among the processes described in the above embodiments, some of the processes described as being performed automatically can be performed manually. Alternatively, all or some of the processes described as being performed manually can be performed automatically using a known method. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

For example, the information processing device 1 described above may be realized by multiple server computers, and depending on the functions, the configuration can be flexibly changed, such as by calling an external platform using an API or network computing. Also, some of the above-mentioned processing of the information processing device 1 may be performed by the terminal device 2. In this case, some of the functions of the terminal device 2 function as an information processing device together with the information processing device 1.

Also, for example, a part or all of the memory unit 11 shown in FIG. 3 may be stored in a storage server or the like, rather than being stored in each device. In this case, each device obtains various pieces of information by accessing the storage server.

8. Effects
As described above, the information processing device 1 according to the embodiment includes the determination unit 33 and the provision unit 36. The determination unit 33 determines the granularity of the ranking based on the behavior history of the user to whom the ranking information, which is ranking information, is to be provided. The provision unit 36 provides the user with the ranking information of the granularity determined by the determination unit 33 as ranking information. This allows the information processing device 1 to provide the user with ranking information of a more appropriate category.

The determination unit 33 also includes an estimation unit 40 that estimates the user's situation based on the user's behavior history, and a determination processing unit 41 that determines the granularity of the ranking based on the user's situation estimated by the estimation unit 40. This allows the information processing device 1 to provide the user with ranking information of more appropriate categories.

The determination unit 33 also determines the size of the ranking target range as the granularity. This allows the information processing device 1 to provide the user with ranking information of more appropriate categories.

The ranking targets are trading objects that are the subject of a transaction, and the determination unit 33 determines the size of the category of trading objects as the granularity. This allows the information processing device 1 to provide the user with ranking information for more appropriate categories.

The ranking targets are trading objects that are the subject of a transaction, and the determination unit 33 determines the size of the attribute range of users who acted on the trading object as the granularity. This allows the information processing device 1 to provide the user with ranking information of a more appropriate category.

The ranking targets are trading objects that are the subject of a transaction, and the determination unit 33 determines the size of the user's range of movement with respect to the trading object as the granularity. This allows the information processing device 1 to provide the user with ranking information of more appropriate categories.

The information processing device 1 also includes a learning unit 32. The learning unit 32 generates a learning model that takes user information as input and outputs a score for each size of the ranking target range, based on information of the providing user who is the user to whom the ranking information is provided and information on the size of the target range of the ranking of the ranking information provided to the providing user when the providing user purchases the trading target. The determination unit 33 determines the granularity using the learning model. This enables the information processing device 1 to provide the user with ranking information of a more appropriate category.

The information processing device 1 also includes a determination unit 34 that determines whether or not to provide ranking information based on the attributes or situation of the user. The provision unit 36 provides the ranking information when the determination unit 34 determines that the ranking information should be provided. This enables the information processing device 1 to provide the user with ranking information of a more appropriate category.

The providing unit 36 also provides multiple types of ranking information, each ranked at the same granularity determined by the determining unit 33. This allows the information processing device 1 to provide the user with ranking information of a more appropriate category.

The above describes the embodiments of the present application in detail with reference to the drawings, but this is merely an example, and the present invention can be embodied in other forms with various modifications and improvements based on the knowledge of those skilled in the art, including the forms described in the disclosure section of the invention.

The above-mentioned "section, module, unit" can be read as "means" or "circuit." For example, an acquisition unit can be read as an acquisition means or an acquisition circuit.

REFERENCE SIGNS LIST 1 Information processing device 2 Terminal device 3 External device 10 Communication unit 11 Storage unit 12 Processing unit 20 User information storage unit 21 Trading object information storage unit 22 Content storage unit 30 Acquisition unit 31 Reception unit 32 Learning unit 33 Determination unit 34 Judgment unit 35 Generation unit 36 Provision unit 40 Estimation unit 41 Decision processing unit 50 Content 51 Search frame 52a, 52b, 52c Ranking information 100 Information processing system N Network

Claims (13)

A determination unit that determines the granularity of the ranking based on a behavior history of a user who is a target of providing ranking information, which is information on the ranking;
a providing unit that provides the user with information on the ranking of the granularity determined by the determining unit as the ranking information ,
The ranking is based on:
It is a trading subject that is the subject of a transaction;
The determination unit is
The size of the category of the transaction object is determined as the granularity.
23. An information processing apparatus comprising: A determination unit that determines the granularity of the ranking based on a behavior history of a user who is a target of providing ranking information, which is information on the ranking;
a providing unit that provides the user with information on the ranking of the granularity determined by the determining unit as the ranking information,
The ranking is based on:
It is a trading subject that is the subject of a transaction;
The determination unit is
An information processing device characterized in that the size of an attribute range of a user who has acted on the transaction object is determined as the granularity . A determination unit that determines the granularity of the ranking based on a behavior history of a user who is a target of providing ranking information, which is information on the ranking;
a providing unit that provides the user with information on the ranking of the granularity determined by the determining unit as the ranking information,
The ranking is based on:
It is a trading subject that is the subject of a transaction;
The determination unit is
An information processing device characterized in that the size of the user's range of action with respect to the transaction object is determined as the granularity . The determination unit is
An estimation unit that estimates a state of the user based on a behavior history of the user;
The information processing device according to any one of claims 1 to 3, further comprising: a determination processing unit that determines the granularity based on the user's situation estimated by the estimation unit. a learning unit that generates a learning model that uses user information as an input and outputs a score for each granularity based on learning information including information on a target user who is a user to whom the ranking information has been provided and information on the size of a target range of ranking information in which the target user has acted on a ranked trading object that is provided to the target user, and
The determination unit is
The information processing apparatus according to claim 1 , wherein the granularity is determined by using the learning model. a determination unit that determines whether or not to provide the ranking information based on an attribute or a situation of the user;
The providing unit is
4. The information processing apparatus according to claim 1 , further comprising: providing the ranking information when the determining unit determines that the ranking information should be provided. The providing unit is
4. The information processing apparatus according to claim 1 , further comprising: providing a plurality of types of the rankings, each of which is ranked at the same granularity determined by the determination unit. 1. A computer-implemented information processing method, comprising:
A determination step of determining the granularity of the ranking based on a behavior history of a user who is to be provided with ranking information, which is information on the ranking;
a providing step of providing information on the ranking of the granularity determined by the determining step as the ranking information to the user ,
The ranking is based on:
It is a trading subject that is the subject of a transaction;
The determining step includes:
The size of the category of the transaction object is determined as the granularity.
23. An information processing method comprising: 1. A computer-implemented information processing method, comprising:
A determination step of determining the granularity of the ranking based on a behavior history of a user who is to be provided with ranking information, which is information on the ranking;
a providing step of providing information on the ranking of the granularity determined by the determining step as the ranking information to the user,
The ranking is based on:
It is a trading subject that is the subject of a transaction;
The determining step includes:
The size of the attribute range of the user who acted on the transaction object is determined as the granularity.
23. An information processing method comprising: 1. A computer-implemented information processing method, comprising:
A determination step of determining the granularity of the ranking based on a behavior history of a user who is to be provided with ranking information, which is information on the ranking;
a providing step of providing information on the ranking of the granularity determined by the determining step as the ranking information to the user,
The ranking is based on:
It is a trading subject that is the subject of a transaction;
The determining step includes:
The size of the user's range of action with respect to the transaction object is determined as the granularity.
23. An information processing method comprising: A determination step of determining the granularity of the ranking based on a behavior history of a user who is a target of providing ranking information, which is information on the ranking;
a providing step of providing information on the ranking of the granularity determined by the determining step as the ranking information to the user ;
The ranking is based on:
It is a trading subject that is the subject of a transaction;
The determination procedure includes:
The size of the category of the transaction object is determined as the granularity.
2. An information processing program comprising: A determination step of determining the granularity of the ranking based on a behavior history of a user who is a target of providing ranking information, which is information on the ranking;
a providing step of providing information on the ranking of the granularity determined by the determining step as the ranking information to the user;
The ranking is based on:
It is a trading subject that is the subject of a transaction;
The determination procedure includes:
The size of the attribute range of the user who acted on the transaction object is determined as the granularity.
2. An information processing program comprising: A determination step of determining the granularity of the ranking based on a behavior history of a user who is a target of providing ranking information, which is information on the ranking;
a providing step of providing information on the ranking of the granularity determined by the determining step as the ranking information to the user;
The ranking is based on:
It is a trading subject that is the subject of a transaction;
The determination procedure includes:
The size of the user's range of action with respect to the transaction object is determined as the granularity.
2. An information processing program comprising:

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