JP2020046881A - Device, method, and program for processing information - Google Patents

Abstract

To allow for appropriately generating information indicative of a relationship between context and content associated with content delivery.SOLUTION: An information processing device of the present invention comprises an acquisition unit and a generation unit. The acquisition unit acquires context information on delivery of content and content information on the content. The generation unit generates relationship information indicative of a combination of a first element of the context information on delivery of the content and a second element of the content information using the context information and the content information acquired by the acquisition unit.SELECTED DRAWING: Figure 3

Description

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

  2. Description of the Related Art Conventionally, a technology for generating information indicating whether a user performs a predetermined action has been provided. For example, there has been provided a technique for visually displaying a portion of a content that is easily clicked by a user.

JP 2014-174805 A

  However, in the above-described related art, it is not always possible to appropriately generate information indicating a relationship between a context related to content distribution and the content. For example, it is difficult to appropriately generate information indicating a relationship between a context related to content distribution and the content only by displaying a portion that is likely to be clicked by the user in a visible manner.

  The present application has been made in view of the above, and has as its object to provide an information processing apparatus, an information processing method, and an information processing program that appropriately generate information indicating a relationship between a context related to content distribution and content. And

  An information processing apparatus according to the present application, an acquisition unit that acquires context information related to distribution of content and content information related to the content, and the content based on the context information and the content information acquired by the acquisition unit. And a generation unit that generates relation information indicating a combination of the first element of the context information related to the distribution of the content information and the second element of the content information.

  According to an aspect of the embodiment, it is possible to appropriately generate information indicating a relationship between a context related to content distribution and content.

FIG. 1 is a diagram illustrating an example of information processing according to the embodiment. FIG. 2 is a diagram illustrating a configuration example of the information processing system according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the information processing apparatus according to the embodiment. FIG. 4 is a diagram illustrating an example of a user information storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of the behavior information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of the distribution information storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of the model information storage unit according to the embodiment. FIG. 8 is a diagram illustrating an example of a prediction information storage unit according to the embodiment. FIG. 9 is a diagram illustrating an example of a related information storage unit according to the embodiment. FIG. 10 is a flowchart illustrating an example of information processing according to the embodiment. FIG. 11 is a diagram illustrating an example of information processing according to the embodiment. FIG. 12 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device.

  Hereinafter, an information processing apparatus, an information processing method, and an embodiment (hereinafter, referred to as “embodiment”) for implementing an information processing program according to the present application will be described in detail with reference to the drawings. The information processing apparatus, the information processing method, and the information processing program according to the present application are not limited by the embodiment. In the following embodiments, the same portions are denoted by the same reference numerals, and redundant description will be omitted.

(Embodiment)
[1. Information processing)
An example of information processing according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of information processing according to the embodiment. In the example of FIG. 1, the information processing apparatus 100 (see FIG. 3) uses a model generated based on the behavior information of the first user located in the predetermined area AR11 (A area) to use the other area AR21 (B area). 4) shows a case where information about the second user located at (2) is generated. In the example of FIG. 1, push notification is shown as an example of one form of content distribution, but content distribution may be in various forms. For example, the content delivery in the information processing system 1 is not limited to the push notification, and may be a so-called pull-type content delivery that requests the content from the terminal device 10. For example, the push notification may be information output on the terminal device 10 without depending on the operation of the terminal device 10 by the user. For example, the push notification may be to actively notify the user of predetermined information. In the following, an operation of displaying information about the push notification on the terminal device 10 by selecting or specifying the push notification may be an opening operation. Further, the size and shape of the area referred to here may be appropriately set according to the purpose of the processing or the like. For example, areas of various sizes such as “prefecture”, “city”, “ward”, and “town” may be appropriately set.

[Configuration of information processing system]
Prior to the description of FIG. 1, the configuration of the information processing system 1 will be described with reference to FIG. FIG. 2 is a diagram illustrating a configuration example of the information processing system according to the embodiment. As shown in FIG. 2, the information processing system 1 includes a terminal device 10, an information providing device 50, and an information processing device 100. The terminal device 10, the information providing device 50, and the information processing device 100 are communicably connected via a predetermined network N by wire or wirelessly. The information processing system 1 illustrated in FIG. 2 may include a plurality of terminal devices 10, a plurality of information providing devices 50, and a plurality of information processing devices 100.

  The terminal device 10 is an information processing device used by a user. The terminal device 10 receives various operations by the user. In the following, the terminal device 10 may be referred to as a user. That is, in the following, a user can be read as the terminal device 10. The terminal device 10 described above is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. FIG. 1 shows a case where the terminal device 10 is a smartphone.

  The terminal device 10 has a function such as a GPS (Global Positioning System) sensor, and can detect and acquire a position. For example, the terminal device 10 transmits action information such as position information to the information processing device 100. Note that the terminal device 10 is not limited to the above, and may have various sensors. For example, the terminal device 10 acquires biological information such as a voice sensor (microphone), an acceleration sensor, a gyro sensor, a temperature sensor, a humidity sensor, an illuminance sensor, a pressure sensor, a proximity sensor, odor, sweat, heartbeat, pulse, brain wave, and the like. May be provided. Then, the terminal device 10 may transmit various sensor information detected by various sensors to the information processing device 100. For example, the terminal device 10 may detect the utterance information (sensor information) of the user with the voice sensor, and transmit the detected utterance information (sensor information) to the information processing device 100.

  For example, the terminal device 10 may have various functions such as a temperature sensor and an atmospheric pressure sensor, and may be capable of detecting and acquiring peripheral information such as a temperature and an atmospheric pressure where the user is located. In addition, the terminal device 10 may have various functions such as a heart rate sensor and may be capable of detecting and acquiring biological information of the user. For example, a user using the terminal device 10 may be able to acquire the user's own biological information by using the terminal device 10 by wearing a wearable device that can communicate with the terminal device 10. For example, a user who uses the terminal device 10 wears a wristband-type wearable device that can communicate with the terminal device 10, so that the terminal device 10 obtains information on the user's own heartbeat (pulse) by the terminal device 10. It may be possible.

  The context information related to the distribution of the content (hereinafter, also simply referred to as “context information”) includes information indicating various contexts related to the distribution of the content. For example, the context information includes information indicating the context of the user to which the content is to be distributed and information indicating the context of the location where the user is located. For example, the context information may include information on a user attribute. For example, the context information may include information indicating what kind of user the user is. For example, the context information may include information about various users, such as age, profile, interest, and the like. For example, the context information may include information on the context of time or place. For example, the context information may include information indicating the situation at the time or place when the push notification is performed. For example, the context information may include information about a time zone. For example, the context information may include information indicating which of the 24 time zones the time zone corresponds to from 0 to 23:00. For example, the context information includes four time zones of morning (4-10 o'clock, etc.), daytime (10-16 o'clock, etc.), night (16-22 o'clock, etc.), and midnight (22-4 o'clock, etc.). May be included. For example, the context information may include information on a POI (Point of Interest) indicating a facility name such as a store or a stadium corresponding to the position of the user. For example, the context information includes various times and places such as a stay (residence) time at which the user is located at the place, attributes of the place (commercial and residential areas, etc.) and attributes of the time (morning and night, etc.). Information may be included.

  For example, the context information may include information about the context of the environment. For example, the context information may include information indicating what environment is around the user when the push notification is performed. For example, the context information may include various information (peripheral information) such as weather, user's emotion (mood), temperature, and atmospheric pressure. For example, the context information may include information indicating whether the weather corresponds to sunny, cloudy, rain, or snow. For example, the context information may include information indicating whether the user's mood is a positive emotion or a negative emotion. For example, the context information may include information indicating whether the user's mood is a positive emotion such as joy or pleasure or a negative emotion such as anger or sadness.

  For example, the information processing apparatus 100 estimates a user's emotion (mood) based on various information about the user. For example, the information processing device 100 estimates the user's emotion based on the user's behavior information. The information processing device 100 estimates a user's emotion by appropriately using various conventional techniques. For example, the information processing apparatus 100 estimates a user's emotion using various input information input by the user, such as content browsed by the user (browse content) and a query used by the user for a search. For example, the information processing apparatus 100 may estimate the emotion of the user by appropriately analyzing character information and input information in the browsed content using various conventional techniques such as syntax analysis. For example, the information processing apparatus 100 stores a list of keywords (emotion keyword list) for various emotions in the storage unit 120 (see FIG. 3), and the keyword included in the character information or input information in the browsed content is most relevant. The emotion to do may be estimated as the emotion of the user. For example, the information processing apparatus 100 uses the emotion keyword list of the keyword corresponding to each of the positive emotion and the negative emotion to include the positive emotion and the negative emotion in the text information and the input information in the browsed content. The emotion of the keyword with more keywords may be estimated as the emotion of the user. Further, the information processing apparatus 100 may estimate the user's emotion using the character information obtained by converting the utterance of the user and the above-described emotion keyword list.

  For example, the information processing device 100 estimates a user's emotion based on sensor information detected by a terminal device used by the user. For example, the information processing apparatus 100 may estimate the user's emotion based on sensor information on the user's stride, amplitude, and the like. Further, for example, the information processing apparatus 100 may estimate the emotion of the user using biological information such as the heart rate and blood pressure of the user, image information obtained by imaging the face of the user, and the like. Note that the above is an example, and the information processing apparatus 100 may use any method as long as the user's emotion can be estimated. For example, the information processing apparatus 100 may estimate the emotion of the user based on the tone (tone of sound) of the utterance of the user. For example, when the tone (tone of sound) of the utterance of the user is high, the information processing apparatus 100 may estimate the emotion of the user as a positive emotion of pleasure or enjoyment. For example, when the tone (tone of sound) of the user's utterance is weak, the information processing apparatus 100 may estimate the user's emotion as a negative emotion of anger or sadness. For example, the information processing apparatus 100 may estimate the emotion of the user as a positive emotion of pleasure or enjoyment when the number or the amount of the products purchased by the user is equal to or more than a predetermined threshold. For example, the information processing apparatus 100 may estimate the emotion of the user as a negative emotion of anger or sadness when the number or the amount of the products purchased by the user is less than a predetermined threshold.

  For example, the context information may include information indicating whether the temperature corresponds to high, medium, or low. For example, the context information includes information indicating whether the temperature corresponds to the high (+ 5 ° C. or more), the medium (less than ± 5 ° C.), or the low (−5 ° C. or less) with respect to the average temperature. May be included. For example, the context information may include information indicating whether the atmospheric pressure corresponds to high, medium, or low. For example, in the context information, the air pressure is higher (more than +1 hPa), medium (less than ± 1 hPa), lower (less than -1 hPa) or less than the air pressure just before (for example, 10 minutes before or 1 hour before). ) May be included. For example, the context information may include information on the operation content. For example, the context information may include various information such as information indicating what kind of operation (push notification), information indicating a category of notification content, and information indicating whether or not an applicator. Note that the above is only an example, and the context information is not limited to the above and may include various information.

  For example, the context information may include information on the status of the user or the terminal device 10 or the status of the user estimated based on the environment (background) of the user or the terminal device 10. Also, the context information includes the content of the content provided to the user, the content of the content to which the user has responded, the attribute of the user, the current position of the user, the current time, the physical environment in which the user is placed, and the society in which the user is placed. Various information such as the environment, the exercise state of the user, and the estimated emotion of the user may be included. Further, the context information may include various information such as the situation of the user or the terminal device 10 or the user's psychological state estimated based on the environment (background) of the user or the terminal device 10.

  The information providing device 50 is an information processing device having various types of open data. For example, the information providing device 50 provides open data. The information providing device 50 is an information processing device that provides open data, which is data that is permitted to be widely opened to the user, to the information processing device 100. For example, the information providing apparatus 50 may be an information processing apparatus that is managed by each local government and provides data held by the local government to the outside as open data. Further, for example, the information providing device 50 may be various information processing devices such as a server managed by the Meteorological Agency. The information providing apparatus 50 is not limited to the above, as long as the data (information) is permitted to be used. Various open data such as disaster mitigation information, procurement information, and statistical information may be provided.

  For example, the information providing apparatus 50 provides the open data of the A area, the B area, and the like to the information processing apparatus 100. For example, the information providing device 50 provides the information processing device 100 with environmental information regarding the A area, the B area, and the like. For example, the information providing device 50 provides the information processing device 100 with various types of environmental information such as traffic information on the area A. For example, the information providing device 50 provides the information processing device 100 with various types of environmental information such as traffic information on the B area and information on the population structure. As described above, the environment information may include various information such as road information and weather information around the user.

  The information processing apparatus 100, based on the context information regarding the distribution of the content and the content information regarding the content, includes an element of the context information related to the distribution of the content (also referred to as a “first element”) and an element of the content information (“ This is an information processing apparatus that generates relationship information indicating a combination with the second element. The information processing device 100 is a generation device that generates relationship information indicating a combination of a first element and a second element based on context information and content information. In the example of FIG. 1, the information processing apparatus 100 generates relationship information indicating a combination with the first element and the second element, using a model including a feature amount regarding the first element and the second element. The information processing device 100 generates relationship information indicating a combination with the first element and the second element, using a model that predicts profitability in content distribution. Here, the profitability in the distribution of the content may be various as long as the distribution of the content may bring a profit (profit) to a predetermined entity. Profitability in content distribution may be profitability of any entity as long as the entity is related to content distribution. For example, the profitability in the distribution of the content may indicate the profit obtained by the provider of the product when the user purchases the product related to the distributed content. For example, it is presumed that the longer a user browses content, the higher the possibility that an action that brings profit (profit) to a predetermined entity such as the user's purchase related to the content is likely to occur. May indicate how long the user views the distributed content. That is, the information processing apparatus 100 generates the relationship information indicating the combination of the first element and the second element using the model that predicts the behavior of the user with respect to the content. Further, the information processing apparatus 100 generates a model for predicting the content opening probability as in a model M11 illustrated in FIG. 11 and uses the model to generate relation information indicating a combination with the first element and the second element. May be generated.

  The information processing apparatus 100 is different from the predetermined area based on operation information indicating whether or not the user has performed a predetermined operation on the content notified by push in the terminal device 10 used by the user located in the predetermined area. This is an information processing apparatus that generates prediction information for predicting whether or not a user has performed a predetermined operation on the content when the content is pushed to the terminal device 10 used by a user located in the area of the user. For example, the information processing device 100 acquires various information such as behavior information including position information from the terminal device 10. Further, for example, the information processing apparatus 100 acquires various information such as open data from the information providing apparatus 50.

  Hereinafter, an example of information processing by the information processing apparatus 100 (see FIG. 3) will be described with reference to FIG. First, the map information MP1 shown in FIG. 1 will be briefly described. Map information MP1 shown in FIG. 1 is a diagram schematically showing an area or the like included in a predetermined range. For example, the map information MP1 is a diagram for schematically illustrating a status of a push notification to the terminal device 10 to the A area or the B area.

  In the example of FIG. 1, the information processing apparatus 100 acquires information indicating that a predetermined content has been pushed to the terminal device 10 used by a certain user and opened by the user (step S1-1). For example, the information processing apparatus 100 indicates that a predetermined content (for example, the content CT11) is pushed to the terminal device 10 used by the user U1 (see FIG. 4) located in the area A and is opened by the user U1. Get information. As described above, when “user U * (* is an arbitrary numerical value)” is described, it indicates that the user is a user identified by the user ID “U *”. For example, when "user U1" is described, the user is a user identified by the user ID "U1".

  For example, the information processing apparatus 100 acquires information indicating that a predetermined content has been pushed to the terminal device 10 used by the user U1 located in the partial area LC1-1 of the area A and opened by the user U1. . Note that the partial area LC1-1 may be an area within a predetermined range in the A area. The partial area LC1-1 may be one piece of position information representing the area within the partial area LC1-1. For example, the partial area LC1-1 may be position information of the center within the range of the partial area LC1-1. Further, the partial area LC1-1 may be a combination of positional information of a center within the range of the partial area LC1-1 and information indicating a range (for example, within 100 m) from the center. Further, the partial area LC1-1 may be a list of positions within the range of the partial area LC1-1. In the example of FIG. 1, the information processing apparatus 100 acquires information indicating that the content (the content CT11) notified in step S1-1 has been viewed by the user for the time RTM1 after opening (step S2). ). For example, the information processing apparatus 100 may acquire information indicating that the content notified in step S1-1 has been displayed on the user terminal device 10 for the time RTM1 after opening.

  The information processing apparatus 100 is not limited to the content distributed to the user, and may use information indicating the browsing time of the entire content group having a predetermined relationship with the content after the content distribution. For example, the information processing apparatus 100 uses not only the content CT11 delivered to the user U1 but also the information indicating the browsing time of the user U1 of the entire content group having a predetermined association with the content CT11 after the delivery of the content CT11. You may. For example, after distributing the content CT11 to the user U1, the information processing apparatus 100 may use information indicating the browsing time of the user U1 of the entire content group of the site (site STX) to which the content CT11 belongs. That is, the information processing apparatus 100 may set the time (profitability) at which the user browsed the group of contents related to the site STX after the distribution of the contents related to the site STX to the user as the target of the generation of the prediction information. In this case, for example, after the content related to the site STX is distributed to the user, the information processing apparatus 100 determines the profit (profitability) obtained by the business operator that operates the site STX due to the user's behavior as the target of the generation of the prediction information. It may be.

  For example, the information processing device 100 stores information acquired from the terminal device 10 in the behavior information storage unit 123 (see FIG. 5). Note that the provision of the content to the terminal device 10 may be performed by the information processing device 100 or another device as long as the information used by the information processing device 100 to generate the prediction information can be obtained. . For example, the content may be provided to the terminal device 10 by any device as long as the information used by the information processing device 100 to generate the prediction information can be acquired.

  In the example of FIG. 1, the information processing apparatus 100 obtains information indicating that a predetermined content has been pushed to the terminal apparatus 10 used by a certain user and that the content has not been opened by the user (step S1-2). . For example, the information processing apparatus 100 receives a push notification of a predetermined content to the terminal device 10 used by the user U2 (see FIG. 4) located in the area A, and acquires information indicating that the content has not been opened by the user U2. For example, the information processing apparatus 100 obtains information indicating that predetermined content has been pushed to the terminal device 10 used by the user U2 located in the partial area LC1-2 of the A area and has not been opened by the user U2. I do.

  In addition, in the example of FIG. 1, for simplicity of explanation, user operations related to two push notifications in steps S1-1 and S1-2 are illustrated. It is assumed that a push notification (for example, 10 million times) is issued, and the information processing apparatus 100 acquires action information such as operation information indicating whether or not the user has performed an opening operation in response to the notification. Steps S1-1 and S1-2 are for describing the processing, and any of steps S1-1 and S1-2 may be performed first, and steps S1-1 and S1-2 may be performed first. May be performed a plurality of times. Hereinafter, when steps S1-1 and S1-2 are described without distinction, they are collectively referred to as step S1.

  Further, the information processing apparatus 100 generates a model used for generating prediction information based on the user's action information on the push notification acquired in step S1 (step S3). For example, the information processing apparatus 100 generates a model that predicts the behavior of the user with respect to the distributed content based on the behavior information of the first user in the area A. For example, the information processing apparatus 100 generates a model that predicts the behavior of the user to whom the content has been distributed with respect to the content, based on the behavior information of the first user in the area A. For example, the information processing apparatus 100 generates a model that predicts the behavior of the user with respect to the distributed content based on the context information of the first user in the area A. For example, the information processing apparatus 100 generates a model that predicts how much the distributed content is opened and viewed based on the context information. For example, the information processing apparatus 100 generates a model for predicting profitability in a push notification based on various information stored in the open data storage unit 121, the user information storage unit 122, the behavior information storage unit 123, and the like. For example, the information processing apparatus 100 uses a model that predicts the behavior of the user with respect to the content for which push notification has been performed, based on various types of information stored in the open data storage unit 121, the user information storage unit 122, the behavior information storage unit 123, and the like. Generate.

  For example, the information processing apparatus 100 generates a model based on the environment information of the area A stored in the open data storage unit 121. For example, the information processing device 100 generates a model based on the user information of the user located in the area A stored in the user information storage unit 122. For example, the information processing device 100 generates a model based on the behavior information of the user located in the area A stored in the behavior information storage unit 123. The information processing apparatus 100 generates a model in consideration of information about the content that has been pushed to the user. For example, the information processing apparatus 100 generates a model including a feature amount having a category of the content that has been pushed to the user as an element.

  Here, the information processing apparatus 100 generates a model including each feature amount corresponding to each element. That is, the information processing apparatus 100 determines the feature amount of the model, and learns the weight of each feature amount to generate the model. For example, the information processing apparatus 100 may generate a model using a feature amount obtained by combining elements of context information by various conventional techniques such as Kronecker product. For example, the information processing apparatus 100 generates a model including a feature amount obtained by combining two or more elements. For example, the information processing apparatus 100 determines a feature amount obtained by combining elements of context information. For example, the information processing apparatus 100 determines a feature amount obtained by combining elements between the categories of the context information.

  For example, the information processing apparatus 100 determines the model of the model based on each combination of the element group EL1 corresponding to the context related to the distribution of the content and the element group EL2 corresponding to the content of the content such as the category of the content in the context information. Determine the feature value. For example, the information processing apparatus 100 determines each combination of the first element of the element group EL1 and the second element of the element group EL2 as the feature amount of the model. For example, the information processing apparatus 100 determines each combination of “Cafe A” or the like which is the first element of the element group EL1 and “Soccer” or “Entertainment” or the like which is the second element of the element group EL2 by using the feature of the model. Determine the amount. The cafe A may be a cafe of a specific chain store or a franchise store, or a more detailed one of the stores belonging to the cafe category such as “sports cafe”, “dining cafe”, and “cat cafe”. It may be a cafe belonging to a category (type).

  In the example of FIG. 1, the information processing apparatus 100 corresponds to the feature amount # 1 corresponding to the combination of the cafe A and the soccer, the feature amount # 2 corresponding to the combination of the cafe A and the entertainment, and the combination of the cafe A and the economy. The characteristic amount # 3 to be performed is determined as the characteristic amount of the model M1. Although not shown in the example of FIG. 1, the information processing apparatus 100 includes a feature amount # 4 corresponding to a combination of a baseball field and soccer, a feature amount # 5 corresponding to a combination of a convenience store and soccer, and a cafe A Various feature quantities such as feature quantity # 11 corresponding to the combination of baseball and baseball, feature quantity # 12 corresponding to the combination of cafe A and tennis, and feature quantity # 13 corresponding to the combination of cafe A and politics are modeled. The feature amount of M1 is determined.

  In addition, the feature amount included in the model is not limited to a combination of elements, and the information processing apparatus 100 may generate a model including a feature amount corresponding to only one element of context information. The information processing apparatus 100 may generate a model including a feature amount in which three or more various elements are combined. For example, the information processing apparatus 100 may generate a model including a feature amount corresponding to only the element “cafe A” or a feature amount corresponding to only the element “soccer”. The combination of the above-described elements is merely an example, and may be various combinations such as a combination between elements in the element group EL1. For example, the information processing apparatus 100 may include context element list information of elements related to contexts such as “cafe A” and “morning” stored in the storage unit 120 (see FIG. 3), “soccer”, “entertainment”, “economy” , "Baseball" or the like, the combination may be determined using content element list information of elements related to the content. For example, the information processing apparatus 100 may generate information indicating each combination by combining a first element selected from the context element list information and a second element selected from the content element list information. For example, the information processing apparatus 100 may determine tens of millions or hundreds of millions of feature amounts as feature amounts to be included in the model M1.

  For example, the information processing apparatus 100 may generate a model that predicts a user's behavior with respect to the content for which the push notification has been made, using the information when the content for which the push notification has been opened as the correct answer information (correct example). For example, the information processing apparatus 100 pushes various context information such as user information of the user, user location information, user action information, and user environment information when the content for which the push notification was opened is opened as correct information. A model for predicting a user's behavior with respect to the notified content may be generated. For example, when the user U1 opens the content that has been pushed and notified in the partial area LC1-1, the information processing apparatus 100 determines the user information of the user U1, the position information of the user U1, the behavior information of the user U1, and the environment information of the user U1. A model that predicts the user's behavior with respect to the content for which the push notification has been made may be generated using various types of context information as the correct answer information. For example, the information processing apparatus 100 generates a model M1 using context information regarding distribution of content to the user U1. For example, the information processing apparatus 100 generates a model M1 using information on the content distributed to the user U1. For example, the information processing device 100 generates a model M1 using information on the content CT11 delivered to the user U1. For example, the information processing device 100 generates the model M1 using information indicating the category of the content distributed to the user U1. For example, the information processing device 100 generates the model M1 using content information indicating that the category of the content distributed to the user U1 is “soccer”.

  In the example of FIG. 1, when the content is opened, the information processing apparatus 100 selects one of a plurality of levels indicating the degree of the user's behavior regarding profitability based on the viewing time of the content of the user after the content is opened. The level of the user's action in each distribution is determined depending on the condition.

  For example, when the browsing time of the content of the user is less than 5 seconds, the information processing apparatus 100 learns to output the score “1” by setting the level of the user's action in the distribution as the level LV1, and thereby predicts. Generate a model that performs For example, when the time RTM1 that is the viewing time of the user U1 illustrated in FIG. 1 is a predetermined time less than 5 seconds (for example, 3 seconds), the context information in the distribution to the user U1 is input. In this case, a model to be predicted is generated by learning to output a score “1”.

  Further, for example, when the browsing time of the content of the user is 5 seconds or more and less than 1 minute, the information processing apparatus 100 learns that the level of the user's action in the distribution is the level LV2 and outputs the score “2”. By doing so, a model to be predicted is generated. For example, when the time RTM1, which is the browsing time of the user U1 shown in FIG. When input, a model to predict is generated by learning to output a score “2”.

  Further, for example, when the browsing time of the content of the user is 1 minute or more and less than 5 minutes, the information processing apparatus 100 sets the level of the action of the user in the distribution to the level LV3 and outputs the score “3”. By doing so, a model to be predicted is generated. For example, when the time RTM1 that is the browsing time of the user U1 illustrated in FIG. 1 is a predetermined time (for example, 2 minutes) of 1 minute or more and less than 5 minutes, the information processing apparatus 100 determines that the context information in the distribution to the user U1 is When input, a predictive model may be generated by learning to output a score “3”. Note that the above is an example, and the information processing apparatus 100 may use various levels, and the information processing apparatus 100 may generate models for predicting various profitability in addition to browsing time.

  In addition, for example, the information processing apparatus 100 generates a model for predicting a user's action with respect to the content for which the push notification has been made, using information when the content for which the push notification has not been opened as incorrect answer information (negative example). Is also good. For example, if the user has not performed the content opening operation, the information processing apparatus 100 learns to output a score “0” with the level of the user's action in the distribution as the level LV0, thereby predicting the model. Generate For example, the information processing apparatus 100 may generate a predictive model by learning to output a score “0” when context information in a case where the push-notified content has not been opened is input. Good.

  For example, the information processing apparatus 100 uses various context information such as user information of the user, user's location information, user's behavior information, and user's environment information when the push-notified content is not opened as incorrect information. Alternatively, a model that predicts the behavior of the user with respect to the content notified by the push notification may be generated. For example, when the user U2 does not open the content notified of the push notification in the partial area LC1-2, the information processing apparatus 100 determines the user information of the user U2, the position information of the user U2, the behavior information of the user U2, and the user U2. A model that predicts the user's behavior with respect to the content for which push notification has been performed may be generated using various types of context information such as environmental information as incorrect information. For example, the information processing apparatus 100 generates a model M1 using context information regarding distribution of content to the user U2. For example, the information processing device 100 generates the model M1 using information on the content distributed to the user U2. For example, the information processing device 100 generates a model M1 using information on the content CT11 delivered to the user U2. For example, the information processing device 100 generates the model M1 using information indicating the category of the content distributed to the user U2. For example, the information processing apparatus 100 generates the model M1 using content information indicating that the category of the content distributed to the user U2 is “soccer”.

  In the example of FIG. 1, the information processing apparatus 100 generates model information of the model M1 as shown in the model information MD. For example, the information processing device 100 generates a model M1 as shown in the model information storage unit 125 (see FIG. 7).

  The model M1 shown in the model information storage unit 125 in FIG. 7 includes feature amounts (features) such as “feature amount # 1 (cafe A × soccer)” to “feature amount # 3 (cafe A × economy)”. Each weight (value) is stored in association with each other. For example, in the example illustrated in FIG. 7, the model information regarding the model M1 is such that the weight of the feature amount # 1 corresponding to the combination of the cafe A and the soccer is “0.5”, and the feature amount # 1 corresponding to the combination of the cafe A and the entertainment. 2 indicates that the weight of the feature quantity # 3 corresponding to the combination of the cafe A and the economy is “0.2” or the like. The generation of each model may be performed by appropriately using various conventional techniques related to machine learning. For example, the generation of the model may be performed using a technology related to machine learning of supervised learning such as SVM (Support Vector Machine). Note that the description regarding the generation of the model is an example, and the generation of the model may be performed by a learning method appropriately selected according to information that can be obtained. As described above, the information processing apparatus 100 generates the model M1 by learning the weight of each feature amount such as the feature amounts # 1 to # 3 of the model M1.

  For example, the information processing apparatus 100 receives a context information regarding content distribution as an input, and generates a model M1 that outputs a score based on the input. For example, the information processing apparatus 100 receives the context information and generates a model M1 that outputs a score indicating a possibility of an action on the distributed content by the user corresponding to the input context information. For example, the information processing apparatus 100 receives the context information and generates a model M1 that outputs a score indicating the level of the user's action (profitability) in delivering the content corresponding to the input context information. Note that the information processing apparatus 100 may generate, as the model M1, a model that outputs a score indicating the probability that the user to whom the content has been distributed performs an opening operation, as shown in a model M11 in FIG.

  Then, the information processing apparatus 100 generates relation information indicating a combination with the first element and the second element based on the generated model M1 (step S4). In the example of FIG. 1, the information processing apparatus 100 generates, based on the generated model M1, relation information indicating a correspondence between a combination of the first element and the second element and a degree of relation between the content and the user's opening behavior with respect to the content. Generate. The information processing apparatus 100 generates, based on the generated model M1, relation information indicating the degree of relation between the first element corresponding to the context related to the distribution of the content and the second element corresponding to the content, and the user's browsing behavior with respect to the content. Generate.

  For example, the information processing apparatus 100 generates relation information indicating a degree of relation between the content and a predetermined action of the user based on the weight of each feature amount of the model M1. The information processing apparatus 100 generates the relationship information by selecting, from each feature amount of the model M1, a feature amount whose corresponding weight satisfies a predetermined condition. The information processing apparatus 100 generates relationship information by selecting a feature amount of which the absolute value of the corresponding weight is larger than a predetermined threshold from among the feature amounts of the model M1. In the example of FIG. 1, the information processing apparatus 100 generates the relationship information by selecting, from each feature amount of the model M1, a feature amount whose absolute value of the corresponding weight is larger than the threshold “0.3”. The threshold value is not limited to “0.3” and may be set as appropriate. Further, for example, the information processing apparatus 100 converts the relationship information in which the relationship information corresponding to each feature amount is ranked such that the feature amount having a large absolute value of the weight among the feature amounts of the model M1 has a higher rank. Generate. Then, the information processing device 100 stores the generated relation information in the relation information storage unit 127.

  “Target action” in the relation information storage unit 127 in FIG. 1 indicates a predetermined action of the target user. In addition, “rank” in the relation information storage unit 127 in FIG. 1 indicates a rank (ranking) of a degree of a relation between each relation information and a target action. “Related information ID” in the related information storage unit 127 in FIG. 1 indicates information for identifying the related information. “Weight” in the relation information storage unit 127 in FIG. 1 indicates the weight of the feature amount corresponding to the relation information. “Element # 1” to “Element # 3” and the like in the relation information storage unit 127 in FIG. 1 indicate elements of the feature amount corresponding to the relation information.

  The relation information storage unit 127 in FIG. 1 indicates that the target action is an action on the content of the user. For example, it indicates that the target action is an action such as opening the content of the user. Further, the relation information (relation information ST1) identified by the relation information ID “ST1” corresponds to a feature amount having a weight of “0.5”, element # 1 is “cafe A”, and element # 2 is Indicates "soccer". That is, the relationship information ST1 corresponds to the feature amount # 1 of the model M1.

  As described above, in the example of FIG. 1, among the feature quantities of the model M1, the feature quantity # 1 has the largest absolute value of the weight, and therefore the information processing apparatus 100 ranks the relationship information ST1 corresponding to the feature quantity # 1. Is ranked first. The information processing apparatus 100 generates relation information indicating that a combination of the elements “cafe A” and “soccer” of the characteristic amount # 1 among the characteristic amounts of the model M1 is related to the user's behavior such as opening the content. I do. Since the weight of the relation information ST1 of the combination of the elements “cafe A” and “soccer” is a positive value, the information processing apparatus 100 determines that the place is “cafe A” and the content category is “soccer”. It can be estimated that there is a high possibility that the user performs an action on the content.

  In addition, among the feature amounts of the model M1, the feature amount corresponding to the combination of the elements “morning” and “entertainment” has the largest absolute value of the weight next to the feature amount # 1. Is determined as the second place in the relation information ST2 corresponding to. Since the weight of the relation information ST2 of the combination of the elements “morning” and “entertainment” is a negative value, the information processing apparatus 100 determines whether the time is “morning” and the content category is “entertainment”. It can be estimated that the possibility of performing an action on the content is low.

  Note that the information processing apparatus 100 may generate the relationship information by selecting not the absolute value of the feature value weight but the feature value whose feature value weight is larger than the threshold value. In this case, the information processing apparatus 100 generates relation information indicating that the element of the feature amount having a large weight affects the user's behavior such as opening the content.

  Then, the information processing apparatus 100 provides the content to the user based on the generated relation information. First, the information processing apparatus 100 determines a user (target user) to which content is to be provided (step S5). In the example of FIG. 1, the information processing apparatus 100 determines the user U3 located at the shop SP1 that is the cafe A in the area B as the target user. The information processing apparatus 100 determines the user U3, which is the second user, located in the area B, which is another area other than the area A, as the target user.

  Then, the information processing device 100 determines the content to be provided to the user U3, which is the target user, based on the generated relation information. The information processing apparatus 100 selects, from the related information stored in the related information storage unit 127, the related information in which the element relating to the place is “cafe A”, “cafe”, or the like. For example, the information processing apparatus 100 selects a predetermined number (for example, 5 or 10) of pieces of related information in which the place-related element is “cafe A”, “cafe”, or the like, in descending order. In the example of FIG. 1, the information processing apparatus 100 selects the related information ST1 and the related information ST4 including the element “cafe A” in descending order.

  Here, the information processing apparatus 100 distributes the content of the category “soccer” to the user located at the target place “cafe A” because the weight of the selected relation information ST1 is a positive value. Presumed to be highly likely to be opened. In addition, since the weight of the selected relation information ST4 is a negative value, when the content of the category “baseball” is distributed to the user located at the place “cafe A”, the information processing apparatus 100 can open the content. Presumed to be low. Therefore, the information processing device 100 determines that the content related to soccer (soccer-related content CT31) is provided to the user U3 located at the place “cafe A”.

  Then, the information processing device 100 provides the soccer-related content CT31 to the user U3 (Step S6). The information processing device 100 provides the soccer-related content CT31 to the terminal device 10-3 used by the user U3. The information processing device 100 transmits the soccer-related content CT31 to the terminal device 10-3 used by the user U3. The information processing device 100 distributes the soccer-related content CT31 to the terminal device 10-3 used by the user U3.

  As described above, by using the generated model M1, the information processing apparatus 100 can appropriately generate information indicating the relationship between the context related to content distribution and the content. In the example of FIG. 1, the information processing apparatus 100 is a second user of the B area different from the A area by using the relationship information generated using the model M1 generated based on the information of the first user of the A area. Appropriate content can be provided to the user U3.

[1-1. Example of information generation)
FIG. 1 is an example, and the information processing apparatus 100 is not limited to the above, and may generate various types of information. This will be described below with reference to FIG. FIG. 11 is a diagram illustrating an example of information processing according to the embodiment. In FIG. 11, a description of the same points as in FIG. 1 may be appropriately omitted. In the example of FIG. 11, the information processing apparatus 100 (see FIG. 3) pushes to another area AR21 (B area) based on action information such as user operation information related to a push notification in a predetermined area AR11 (A area). The case where prediction information regarding notification is generated will be described. In the example of FIG. 11, a push notification is shown as an example of one mode of content distribution, but content distribution may be in various modes. For example, the content delivery in the information processing system 1 is not limited to the push notification, and may be a so-called pull-type content delivery that requests the content from the terminal device 10.

  For example, in the example of FIG. 11, distribution information of the opening ratio of push notifications in the B area is accurately generated because action information such as actual user operation information regarding push notifications in the B area is insufficient. Indicates when it is difficult. For example, the push notification may be information output on the terminal device 10 without depending on the operation of the terminal device 10 by the user. For example, the push notification may be to actively notify the user of predetermined information. The unsealing rate here indicates, for example, the rate at which the user has performed the unsealing operation on the push notification displayed on the terminal device 10. Further, the opening operation may be a predetermined operation of the user such as selection or designation for the push notification. For example, the opening operation may be a predetermined operation of a user who causes the terminal device 10 to display information regarding the push notification. For example, in the example of FIG. 11, it is assumed that the number of notifications in the B area is small as shown in step S10. Step S10 is for illustrating that the number of notifications of the B area is small, and may not be included in the processing procedure.

  First, the map information MP1-1 to MP1-3 shown in FIG. 11 will be briefly described. Map information MP1-1 to MP1-3 shown in FIG. 11 are diagrams schematically showing areas and the like included in a predetermined range. For example, the map information MP1-1 is a diagram for schematically illustrating a status of a push notification to the terminal device 10 to the A area or the B area. Further, for example, the map information MP1-2 is a diagram for schematically showing distribution information of the opening rate of the push notification to the terminal device 10 in the A area. Further, for example, the map information MP1-3 is a diagram for schematically showing prediction information of the opening rate of the push notification to the terminal device 10 in the B area. The ranges (areas) shown in the map information MP1-1 to MP1-3 are the same area. In the following, the map information MP1-1 to MP1-3 will be referred to as map information MP1 when described without any particular distinction.

  In the example of FIG. 11, the information processing apparatus 100 acquires information indicating that a predetermined content has been pushed to the terminal device 10 used by a certain user and opened by the user (step S11-1). For example, the information processing apparatus 100 indicates that a predetermined content (for example, the content CT11) is pushed to the terminal device 10 used by the user U1 (see FIG. 4) located in the area A and is opened by the user U1. Get information. For example, the information processing apparatus 100 acquires information indicating that a predetermined content has been pushed to the terminal device 10 used by the user U1 located in the partial area LC1-1 of the area A and opened by the user U1. . Note that the partial area LC1-1 may be an area within a predetermined range in the A area. Further, in the example of FIG. 11, the information processing apparatus 100 may acquire information indicating that the content (the content CT11) notified in step S11-1 has been viewed by the user for the time RTM1 after opening. . For example, the information processing apparatus 100 may acquire information indicating that the content notified in step S11-1 has been displayed on the user terminal device 10 for the time RTM1 after opening.

  For example, the information processing device 100 stores information acquired from the terminal device 10 in the behavior information storage unit 123 (see FIG. 5). Note that the provision of the content to the terminal device 10 may be performed by the information processing device 100 or another device as long as the information used by the information processing device 100 to generate the prediction information can be obtained. . For example, the content may be provided to the terminal device 10 by any device as long as the information used by the information processing device 100 to generate the prediction information can be acquired.

  In the example of FIG. 11, the information processing apparatus 100 acquires information indicating that a predetermined content has been pushed to the terminal apparatus 10 used by a certain user and has not been opened by the user (step S11-2). . For example, the information processing apparatus 100 receives a push notification of a predetermined content to the terminal device 10 used by the user U2 (see FIG. 4) located in the area A, and acquires information indicating that the content has not been opened by the user U2. For example, the information processing apparatus 100 obtains information indicating that predetermined content has been pushed to the terminal device 10 used by the user U2 located in the partial area LC1-2 of the A area and has not been opened by the user U2. I do.

  Note that, in the example of FIG. 11, for simplicity of explanation, user operations related to two push notifications in steps S11-1 and S11-2 are illustrated. It is assumed that a push notification (for example, 10 million times) is issued, and the information processing apparatus 100 acquires action information such as operation information indicating whether or not the user has performed an opening operation in response to the notification. Steps S11-1 and S11-2 are for explaining the processing, and any one of steps S11-1 and S11-2 may be performed first. May be performed a plurality of times. Hereinafter, when steps S11-1 and S11-2 are described without distinction, they are collectively referred to as step S11.

  Then, the information processing apparatus 100 generates distribution information based on action information such as operation information relating to the push notification (step S12). For example, the information processing apparatus 100 generates distribution information by calculating a content opening rate based on operation information regarding push notification. For example, the information processing apparatus 100 stores the distribution information generated based on the operation information acquired in step S11 in the distribution information storage unit 124 (see FIG. 6). For example, the information processing apparatus 100 stores the distribution information in which the area A is the aggregation target area in the distribution information storage unit 124.

  “Aggregation target area” shown in the distribution information storage unit 124 in FIG. 6 indicates an area from which distribution information is collected. “Distribution information” shown in the distribution information storage unit 124 in FIG. 6 indicates distribution information collected for the aggregation target area. “Partial area” shown in the distribution information storage unit 124 in FIG. 6 indicates a partial area in the aggregation target area. “Number of pushes” shown in the distribution information storage unit 124 in FIG. 6 indicates the number of times a push notification has been made to the user located in the corresponding partial area. “Number of times of opening” shown in the distribution information storage unit 124 in FIG. 6 indicates the number of times that the user located in the corresponding partial area has opened the push notification. The “opening rate (%)” indicates the rate at which the user located in the corresponding partial area has opened the push notification (= opening times / pushing times × 100). For example, the “opening rate (%)” takes a value of 0 to 100 (%). For example, the information processing device 100 calculates an opening rate.

  For example, in the example illustrated in the distribution information storage unit 124 in FIG. 6, the distribution information for the aggregation target area “A area” is “10000 (times)” for the user located in the partial area LC1-1 in the A area. Indicates that a push notification has been made. Further, for example, it indicates that the user located in the partial area LC1-1 has opened the push notification “2000 (times)”. Further, for example, the opening rate of the user located in the partial area LC1-1 is “20 (%)” because the notification was opened 2,000 times out of 10,000 notifications. For example, the information processing apparatus 100 calculates the opening rate of the user located in the partial area LC1-1 as “20% (= 2000/10000 * 100)”.

  In addition, the information processing apparatus 100 may calculate the opening rate based on various information without being limited to the above. For example, the information processing apparatus 100 may calculate the opening rate of the user located in the partial area LC1-1 using the score itself output by the model M11. For example, the information processing apparatus 100 calculates the average value of the scores output by the model M11 to which the context information of the content distribution in the partial area LC1-1 has been input as the opening rate of the user located in the partial area LC1-1. Is also good. For example, the information processing apparatus 100 calculates the open rate of the user located in the partial area LC1-1 by dividing the total value of the scores output by the model M11 for the content distribution in the partial area LC1-1 by the number of outputs. May be. For example, in the information processing apparatus 100, the total value of the scores output by the model M11 regarding the content distribution in the partial area LC1-1 is “45 (= 0.2 + 0.5 +...)”, And the number of outputs is “100”. In some cases, the opening rate of the user located in the partial area LC1-1 may be calculated as “45% (= 45/100 * 100)”.

  In the example of FIG. 11, the information processing apparatus 100 generates distribution information DM11 in which the area A is the aggregation target area. The distribution information DM11 shown in the area A of the map information MP1-2 in FIG. 11 indicates the level of the opening rate in each partial area in a display form like a heat map. In the example of FIG. 11, the higher the hatched area, the higher the opening rate. In the example of FIG. 11, the distribution information DM11 indicates that the opening rate is high in the partial area LC1-1 and the like in the A area. In the example of FIG. 11, the distribution information DM11 indicates that the opening rate is low in the partial area LC1-2 in the A area. That is, in the area A, the opening rate near the center is high, and the opening rate decreases as the distance from the center increases.

  In addition, the information processing apparatus 100 generates a model used for generating prediction information based on action information such as operation information on the push notification acquired in step S11 (step S13). For example, the information processing apparatus 100 generates a model for predicting whether to open the push notification based on behavior information such as operation information in the area A. For example, the information processing apparatus 100 generates a model that predicts a user's behavior (profitability) in content distribution based on context information. For example, the information processing apparatus 100 generates a model that predicts whether the distributed content will be opened based on the context information. For example, the information processing apparatus 100 generates a model that predicts whether to open the push notification based on various information stored in the open data storage unit 121, the user information storage unit 122, the behavior information storage unit 123, and the like. . For example, the information processing apparatus 100 generates a model based on the environment information of the area A stored in the open data storage unit 121. For example, the information processing device 100 generates a model based on the user information of the user located in the area A stored in the user information storage unit 122. For example, the information processing device 100 generates a model based on the behavior information of the user located in the area A stored in the behavior information storage unit 123. Further, for example, the information processing apparatus 100 may generate a model in consideration of information on the content that has been pushed to the user.

  For example, the information processing apparatus 100 may generate a model that predicts whether or not the push notification will be opened, using the information when the content for which the push notification has been opened as the correct answer information (correct example). For example, the information processing apparatus 100 may generate the model M11 by learning to output a score “1” when context information is input when the content for which the push notification has been made is opened. For example, the information processing apparatus 100 pushes various context information such as user information of the user, user location information, user action information, and user environment information when the content for which the push notification was opened is opened as correct information. A model for predicting whether to open the notification may be generated. For example, when the user U1 opens the content that has been pushed and notified in the partial area LC1-1, the information processing apparatus 100 determines the user information of the user U1, the position information of the user U1, the behavior information of the user U1, and the environment information of the user U1. A model for predicting whether or not to open the push notification may be generated by using various context information such as the correct answer information.

  In addition, for example, the information processing apparatus 100 may generate a model for predicting whether or not to open the push notification, using information when the content for which the push notification has not been opened as incorrect information (negative example). . For example, the information processing apparatus 100 may generate the model M11 by learning to output a score “0” when context information is input when the push-notified content is not opened. . For example, the information processing apparatus 100 uses various context information such as user information of the user, user's location information, user's behavior information, and user's environment information when the push-notified content is not opened as incorrect information. Alternatively, a model for predicting whether to open the push notification may be generated. For example, when the user U2 does not open the content notified by the push notification in the partial area LC1-2, the information processing apparatus 100 determines that the user information of the user U2, the position information of the user U2, the behavior information of the user U2, and the user U2. A model for predicting whether or not to open the push notification may be generated using various context information such as environmental information as incorrect information.

  In the example of FIG. 11, the information processing device 100 generates model information of the model M11 as shown in the model information MD. For example, the information processing device 100 generates a model M11 as shown in the model information storage unit 125 (see FIG. 7). For example, the information processing apparatus 100 receives a context information regarding content distribution as an input, and generates a model M11 that outputs a score based on the input. For example, the information processing apparatus 100 receives the context information and generates a model M11 that outputs a score indicating a probability that the user will perform a predetermined action in distributing the content corresponding to the input context information. In this case, the information processing apparatus 100 may use the score output by the model M11 as a value indicating the probability of the user performing a predetermined action. For example, the information processing apparatus 100 may use the score output by the model M11 as a value indicating the probability of the user opening the content. For example, the information processing apparatus 100 receives the context information and generates a model M11 that outputs a score indicating the possibility of the user performing a predetermined action in delivering the content corresponding to the input context information. For example, the information processing apparatus 100 receives the context information and generates a model M11 that outputs a score indicating a degree (level) at which the user performs a predetermined action in distributing the content corresponding to the input context information.

  For example, if the score output by the model M11 to which context information such as user information and surrounding environment where the user is located is input is “0.7”, the information processing apparatus 100 opens the push notification. May be determined to be “70% (= 0.7 * 100)”. For example, the information processing device 100 may associate the score in the range of 0 to 1 output by the model M11 with 0% to 100%. For example, when the score output by the model M11 to which the context information has been input is “0”, the information processing apparatus 100 has a probability that the user will open the push notification “0% (= 0 * 100)”. May be determined. For example, when the score output by the model M11 to which the context information has been input is “1”, the information processing apparatus 100 has a probability that the user will open the push notification “100% (= 1 * 100)”. May be determined.

  Further, for example, when the model M11 outputs a value less than 0, that is, a negative score, the information processing apparatus 100 may associate the negative score with 0%. For example, when the score output by the model M11 to which the context information is input is “−1”, the information processing apparatus 100 determines that the probability that the user will open the push notification is “0%”. Good. In this case, for example, the information processing apparatus 100 generates the model M11 by learning to output a score of 0 or less when context information is input when the push-notified content is not opened. You may.

  Further, for example, when the model M11 outputs a score having a value greater than 1, the information processing apparatus 100 may associate a score having a value greater than 1 with 100%. For example, when the score output by the model M11 to which the context information has been input is “3”, the information processing apparatus 100 may determine that the probability that the user will open the push notification is “100%”. . As described above, the information processing apparatus 100 receives the context information and generates the model M11 that outputs a score indicating the probability (degree) of the user performing a predetermined action in the distribution of the content corresponding to the input context information. Is also good. In this case, for example, the information processing apparatus 100 generates the model M11 by learning so as to output one or more scores when context information in the case where the push-notified content is opened is input. Is also good. For example, the information processing apparatus 100 may generate the model M11 by learning to output one or more scores when context information is input according to the profit from the opened content.

  For example, the information processing apparatus 100 receives context information such as user information and surrounding environment information where the user is located as an input, and generates a model M11 that outputs a score based on the input. For example, the information processing apparatus 100 receives the context information as input and generates a model M11 that outputs a score based on the input. For example, the information processing apparatus 100 receives context information such as user information and environment information around the user as an input, and outputs a score based on the input by the model M11 as a predetermined threshold (for example, “0.5 , Etc.), the user may be determined to open the content to be pushed. For example, the information processing apparatus 100 may store the generated model M11 in the model information storage unit 125 (see FIG. 7). Note that the model M11 shown in FIG. 11 may be the same model as the model M1 shown in FIG. The generation of each model may be performed by appropriately using various conventional techniques related to machine learning. For example, the generation of the model may be performed using a technology related to machine learning of supervised learning such as SVM (Support Vector Machine).

  Then, the information processing apparatus 100 generates prediction information based on the generated model M11 (Step S14). For example, the information processing apparatus 100 generates prediction information by calculating a content opening rate based on the generated model M11. For example, the information processing apparatus 100 generates prediction information regarding the B area based on the model M11 and various information regarding the B area which is the prediction target area.

  The information processing device 100 acquires the open data of the prediction target area. In the example of FIG. 11, the information processing apparatus 100 acquires the environment information ED of the B area. For example, the information processing apparatus 100 may acquire various information such as environment information of the B area from the open data storage unit 121 (see FIG. 3).

  Further, for example, the information processing apparatus 100 acquires user information located in the prediction target area. In the example of FIG. 11, the information processing apparatus 100 acquires the user information UD of the B area. For example, the information processing apparatus 100 acquires user information as shown in the user information storage unit 122 (see FIG. 4) and user behavior information as shown in the behavior information storage unit 123 (see FIG. 5).

  For example, the information processing apparatus 100 may generate B area prediction information for a user whose position information stored in the user information storage unit 122 (see FIG. 4) corresponds to the B area. For example, the information processing apparatus 100 generates prediction information of the B area for the user U3 whose position information is the partial area LC2-1 in the B area, the user U4 that is the partial area LC2-2 in the B area, and the like. May be.

  For example, the information processing apparatus 100 may predict whether the user U3 will open the push notification by inputting the user information of the user U3 or the context information such as the environment information of the user U3 into the model M11. For example, the information processing apparatus 100 inputs various information including the position information and the action information of the user U3 to the model M11, and when the model M11 outputs a score less than a predetermined threshold, the user U3 opens the push notification. You may decide not to do so. In this case, the information processing apparatus 100 stores information indicating that the user U3 does not open the push notification in the storage unit 120 (see FIG. 3), or determines that the push notification is performed once in the partial area LC2-1. The indicated information may be stored in the prediction information storage unit 126 (see FIG. 8).

  Further, for example, the information processing apparatus 100 may predict whether the user U4 will open the push notification by inputting user information of the user U4 and context information such as environment information of the user U4 into the model M11. For example, the information processing apparatus 100 inputs various information including the position information and the action information of the user U4 to the model M11, and when the model M11 outputs a score equal to or more than a predetermined threshold, the user U4 opens the push notification. Then, it may be determined. In this case, the information processing apparatus 100 stores information indicating that the user U4 opens the push notification in the storage unit 120, or indicates that the push notification is performed once in the partial area LC2-2 and is opened. The information may be stored in the prediction information storage unit 126 (see FIG. 8).

  For example, the information processing apparatus 100 may generate prediction information for a user located in the area B in a predetermined time zone. For example, the information processing apparatus 100 may generate prediction information corresponding to the morning B area for a user located in the B area in the morning time zone (6:00 to 10:00). For example, the information processing apparatus 100 may generate prediction information corresponding to the night B area for a user located in the B area in the night time zone (19:00 to 23:00).

  As described above, the information processing apparatus 100 generates prediction information on the B area based on the model M11 and various information. For example, the information processing apparatus 100 stores the prediction information in which the B area is the prediction target area and stores the prediction information.

  The “prediction target area” shown in the prediction information storage unit 126 in FIG. 8 indicates an area for which prediction information is to be generated. “Prediction information” shown in the prediction information storage unit 126 in FIG. 8 indicates prediction information generated for the prediction target area. “Partial area” shown in the prediction information storage unit 126 in FIG. 8 indicates a partial area in the prediction target area. The “number of pushes” shown in the prediction information storage unit 126 in FIG. 8 indicates the number of times that it is predicted that the push notification is performed to the user located in the corresponding partial area in the generation of the prediction information. The “number of times of opening” shown in the prediction information storage unit 126 in FIG. 8 indicates the number of times that the push notification is predicted to be opened by the user located in the corresponding partial area in the generation of the prediction information. The “opening rate (%)” shown in the prediction information storage unit 126 in FIG. 8 is a ratio (= the number of times of opening) in which the user located in the corresponding partial area is predicted to open the push notification in generating the prediction information. / Push count × 100). For example, the “opening rate (%)” takes a value of 0 to 100 (%). For example, the information processing apparatus 100 calculates the opening rate of the user.

  For example, in the example shown in the prediction information storage unit 126 in FIG. 8, the prediction information for the prediction target area “B area” is “2000 (times)” for the user located in the partial area LC2-1 in the B area. Indicates that a push notification is expected to be made. Also, for example, it indicates that the user located in the partial area LC2-1 in the B area is expected to open the push notification “40 (times)”. Further, for example, the opening rate of the user located in the partial area LC2-1 in the B area is “2 (%)” because it is predicted that the opening is performed 40 times out of 2,000 notifications. Is shown. For example, the information processing apparatus 100 calculates the opening rate of the user located in the partial area LC2-1 as “2% (= 40/2000 * 100)”.

  In the example of FIG. 11, the information processing apparatus 100 generates prediction information PM11 in which the B area is a prediction target area. The prediction information PM11 shown in the B area of the map information MP1-3 in FIG. 11 indicates the level of the opening rate in each partial area in a display form like a heat map. In the example of FIG. 11, the higher the hatched area, the higher the predicted opening rate. In the example of FIG. 11, the prediction information PM11 indicates that the opening rate is high in the lower right partial area in the B area. In the example of FIG. 11, the prediction information PM11 indicates that the opening rate is slightly higher in the partial area LC2-2 and the like in the B area. In the example of FIG. 11, the prediction information PM11 indicates that the opening rate is low in the partial area LC2-1 and the like in the B area. That is, in the area B, the opening rate near the lower right part is high, and the opening rate decreases as the distance from the lower right part increases.

  As described above, the information processing apparatus 100 generates prediction information for predicting the opening rate of the push notification for the B area where the result of the push notification is insufficient. Accordingly, the information processing apparatus 100 can accurately perform various services related to the push notification even in the B area where the result of the push notification is insufficient. For example, the information processing apparatus 100 can generate distribution information for a point where information such as an area with a large population is easily collected using information on the area. On the other hand, it is difficult for the information processing apparatus 100 to generate distribution information using information on the area where the amount of information that can be acquired is insufficient due to a small population or the like. Therefore, the information processing apparatus 100 generates the prediction information of the area with insufficient information using the information of the area with sufficient information, so that a desired prediction can be performed even on the area with insufficient information. As described above, the information processing apparatus 100 makes it possible to predict the presence or absence of a user's predetermined operation in another area where the results are insufficient and which can be obtained based on the information of the predetermined area where the results are sufficiently obtainable. be able to. Further, for example, the information processing apparatus 100 can generate prediction information indicating that the periphery where a specific POI is located tends to have a high opening rate. For example, the information processing apparatus 100 can generate prediction information or the like indicating that an area where there are many users with a specific attribute tends to have a high opening rate. Further, for example, even if the information processing apparatus 100 is at the same position, by generating the prediction information according to the time zone, the prediction information or the like indicating that the opening rate increases at the timing when the predetermined event is performed is generated. Can be generated.

  Various predictions can be performed based on the prediction information generated by the information processing device 100 as described above. For example, when the information processing apparatus 100 generates prediction information based on a position (an area or the like) and a time (a time zone or the like), it becomes possible to predict a timing at which an opening rate becomes high in a certain area by the generated prediction information. . For example, when the information processing apparatus 100 generates prediction information based on a position (area or the like) and time (time zone or the like), the generated prediction information indicates an area near a bus stop and a timing at which students gather. Thus, it is possible to predict that the opening rate near the bus stop will be high. In addition, the information processing apparatus 100 generates prediction information based on the position (area or the like) and time (time zone or the like), and the generated prediction information indicates an area near the bus stop and a timing at which students gather. In addition, it is possible to predict that the opening rate near the bus stop becomes high immediately before the bus arrives.

  Further, for example, the information processing apparatus 100 generates prediction information based on a position (area or the like) and a time (time zone or the like), and the generated prediction information allows the area near the racetrack where an important race is held to be performed. In addition, at the timing when an important race runs, it is possible to predict that the opening rate near the racetrack will be high. Further, for example, the information processing apparatus 100 generates prediction information based on a position (area or the like) and a time (time zone or the like), and the generated prediction information allows the area near the racetrack where an important race is held to be performed. At a predetermined timing (for example, 20 minutes before the start, etc.) before the start of the important race, it is possible to predict that the opening rate near the racetrack will increase. Further, the information processing apparatus 100 may use such prediction information to charge a higher fee for advertisement distribution in an area during a time period when the opening rate is high in the area than in other time periods. Further, for example, the information processing apparatus 100 uses such prediction information to display a unit price for an advertisement space in content distributed to the terminal device 10 located in the area during a time period when the opening rate is high in the area. May be higher than other time zones. The above is an example, and the information processing apparatus 100 may use the prediction information for various services.

  For example, the information processing apparatus 100 may perform a push notification based on the generated prediction information. For example, the information processing apparatus 100 may perform a push notification to a user located in a hatched portion (lower right in the B area) in the prediction information PM11. Further, for example, the information processing apparatus 100 may provide the generated prediction information to an external information processing apparatus. For example, the information processing device 100 may provide the prediction information to an external device that performs a content providing service.

[1-1-1. Generation of prediction information by simulation)
As described above, the information processing apparatus 100 generates prediction information based on the context information such as the behavior information of the user located in the area B as the prediction target area and the surrounding environment information, and the model M11. However, the prediction information may be generated by a predetermined simulation. In this case, the storage unit 120 of the information processing apparatus 100 may include a simulator information storage unit (not shown) in which various types of information on a predetermined simulator such as information on a virtual space are stored. For example, the information processing apparatus 100 may generate the prediction information based on the B area in the virtual space reflecting the flow of the people in the B area and the context information of the features of the B area.

  Further, the model information storage unit 125 of the information processing apparatus 100 may store a model applied to an agent in a simulation (hereinafter, also referred to as an “agent model”). For example, the information processing apparatus 100 may generate a model applied to an agent in a simulation. For example, the information processing device 100 generates a model based on user behavior information. For example, the information processing apparatus 100 may generate an agent model based on user behavior information in the area A. For example, the information processing device 100 generates an agent model corresponding to each attribute based on the behavior information of the user. For example, the information processing apparatus 100 may generate various agent models such as an agent model for a male in his 20s and an agent model for a female in his 20s based on the behavior information of the user. For example, the information processing apparatus 100 may generate an agent model in consideration of various types of environmental information such as open data. For example, the information processing device 100 may store the generated agent model in the model information storage unit 125.

  For example, even if the information processing apparatus 100 generates simulation information (simulation log) on the behavior of an agent in a predetermined simulator using various information on a predetermined simulator such as information on a virtual space stored in a simulator information storage unit. Good. For example, the information processing apparatus 100 may generate simulation information (simulation log) by allocating an agent model stored in the model information storage unit 125 to an agent and performing a push notification and a simulation related to opening the push notification. Further, the information processing apparatus 100 may generate prediction information based on the generated simulation information.

  For example, the information processing apparatus 100 may generate a simulation log by simulating the behavior of a user (human) in the area B that is the prediction target area. For example, the prediction information may be generated based on a simulation in order to predict the opening rate of the push notification in the B area because the number of times of the push notification to the actual user in the B area is insufficient. .

  For example, the information processing device 100 determines an agent model applied to each agent. For example, the information processing apparatus 100 may determine an agent model to be applied to each agent based on information related to the population structure of the B area included in the environmental information ED of the B area. For example, the information processing apparatus 100 may determine an agent model to be applied to each agent based on the population structure of the B area relating to age and gender as attribute information. The attribute information used for the population structure is not limited to age and gender, and various attribute information may be used.

  In the following example, it is assumed that the population in the area B is predominantly male in their 20s. For example, the information processing apparatus 100 generates agents for the number of people (population and the like) in the B area. In the example of FIG. 11, it is assumed that the information processing apparatus 100 has a population of 50,000 in the area B and generates 50,000 agents. For example, the information processing apparatus 100 assigns attributes to 50,000 agents so as to reflect the population structure in the area A. For example, when the ratio of males in their 20s in the B area is 50%, the information processing apparatus 100 assigns the attribute of males in their 20s to 25,000 agents. Further, for example, when the ratio of women in their 20s in the B area is 20%, the information processing apparatus 100 assigns the attribute of women in their 20s to 10,000 agents.

  For example, the information processing apparatus 100 applies an agent model corresponding to a male in his 20s to an agent having a male attribute in his 20s. For example, the information processing apparatus 100 applies an agent model corresponding to a man in his 20s to 25,000 agents to which an attribute of a man in his 20s is assigned. Further, for example, the information processing apparatus 100 applies an agent model corresponding to a woman in her twenties to 10,000 agents to which an attribute of a woman in her twenties is assigned.

  For example, the information processing apparatus 100 performs a simulation using an agent to which an agent model is applied. For example, each agent applied to the virtual space corresponding to the B area performs an action according to an agent model assigned to each agent. For example, an agent to which an agent model corresponding to a man in his 20's applies a push notification at a predetermined timing may open the push notification according to the context such as the position and situation in the B area at that time. Determine whether or not. Also, for example, when a push notification is given at a predetermined timing, an agent to which an agent model corresponding to a woman in her twenties opens the push notification according to the context such as the position and situation in the B area at that time. Decide if you want to.

  Thus, the information processing apparatus 100 generates a simulation log by causing each agent to perform an action in the virtual space corresponding to the B area. For example, the information processing apparatus 100 may store the generated simulation log in the storage unit 120. For example, the information processing apparatus 100 generates prediction information based on the generated simulation log. For example, the information processing apparatus 100 generates prediction information (for example, prediction information PM11 in FIG. 11) in which the B area is a prediction target area based on a simulation log corresponding to the B area.

[1-2. About model generation)
The information processing apparatus 100 may generate the model by various methods other than the above. For example, the information processing apparatus 100 may generate (update) a model using information of the generated model. The information processing apparatus 100 may generate (update) a model by feeding back information of the generated model. For example, the information processing apparatus 100 may generate (update) a model by using information of the generated model as a constraint. For example, the information processing apparatus 100 may generate (update) a model by using, as a constraint, a feature amount that satisfies a predetermined condition in the generated model information.

  For example, the information processing apparatus 100 generates a model including a feature amount excluding a feature amount in which the absolute value of the weight is equal to or less than a predetermined threshold value (for example, 0.01 or 0.001 or the like) from the generated model information. (Update). For example, the information processing apparatus 100 removes the model M1 from the generated model M1 except for a feature amount (feature amount #X) whose absolute value of the weight is 0.00001 and is equal to or less than a predetermined threshold (0.001). May be generated (updated). For example, when the feature amount #X corresponds to the combination of the elements “soccer field” and “cucumber”, the information processing apparatus 100 excludes the feature amount corresponding to the combination of the elements having a low relationship, thereby forming the model M1. It can be generated (updated) more appropriately.

[2. Configuration of information processing device]
Next, the configuration of the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram illustrating a configuration example of the information processing apparatus according to the embodiment. As shown in FIG. 3, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The information processing apparatus 100 includes an input unit (for example, a keyboard and a mouse) for receiving various operations from an administrator or the like of the information processing apparatus 100, and a display unit (for example, a liquid crystal display) for displaying various information. May have.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. The communication unit 110 is connected to a network (for example, the network N in FIG. 2) by wire or wirelessly, and transmits and receives information to and from the terminal device 10.

(Storage unit 120)
The storage unit 120 is realized by 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. As illustrated in FIG. 3, the storage unit 120 according to the embodiment includes an open data storage unit 121, a user information storage unit 122, an action information storage unit 123, a distribution information storage unit 124, and a model information storage unit 125. , A prediction information storage unit 126 and a relation information storage unit 127.

  Although not shown, the open data storage unit 121 stores various open data. For example, the open data storage unit 121 stores open data including information on traffic such as road information such as construction and traffic congestion, information on weather such as weather and temperature, and information on economy such as household budget and consumption. You may.

  Although not shown, the storage unit 120 includes a content information storage unit that stores content information such as the content CT11. The content information storage unit stores tag information (for example, soccer, cucumber, etc.) shown in the content of each content in association with information for identifying the content (content ID, etc.). The content information storage unit stores information relating to various contents such as content itself such as the content CT11 and information indicating a category of the content such as the content CT11 (for example, sports, entertainment, etc.). Although not shown, the storage unit 120 includes a POI information storage unit that stores a facility name of each POI such as a store or a stadium, a location of the facility, an area, and the like. The POI information storage unit stores various information such as the category of each POI in association with information for identifying each POI (eg, the ID of the POI).

(User information storage unit 122)
The user information storage unit 122 according to the embodiment stores various information related to the user. For example, the user information storage unit 122 stores various information on user attributes and various information on user behavior. FIG. 4 is a diagram illustrating an example of a user information storage unit according to the embodiment. The user information storage unit 122 illustrated in FIG. 4 includes items such as “user ID”, “age”, “sex”, “home”, “work place”, “interest”, and “location information”.

  “User ID” indicates identification information for identifying a user. “Age” indicates the age of the user identified by the user ID. The “age” may be a specific age of the user identified by the user ID, for example, 35 years old. “Gender” indicates the gender of the user identified by the user ID.

  “Home” indicates the location information of the home of the user identified by the user ID. In the example illustrated in FIG. 4, “home” indicates an abstract code such as “LC11”, but may be information indicating latitude and longitude. Further, for example, “home” may be an area name or an address.

  “Work location” indicates the location information of the work location of the user identified by the user ID. In the example illustrated in FIG. 4, “work place” indicates an abstract code such as “LC12”, but may be information indicating latitude and longitude. Further, for example, the “work location” may be an area name or an address.

  "Interest" indicates the interest of the user identified by the user ID. That is, “interest” indicates an object of high interest for the user identified by the user ID. In the example illustrated in FIG. 4, “interest” is illustrated one by one for each user, but may be plural.

  “Position information” indicates position information of the user at a predetermined date and time (for example, when prediction information is generated). For example, "position information" indicates the latest user position information acquired before a predetermined date and time (for example, when prediction information is generated). In the example of FIG. 4, “position information” is illustrated by an abstract code “LC1-1” or the like, but specific “latitude / longitude information” may be stored in “position information”.

  For example, in the example shown in FIG. 4, the age of the user identified by the user ID “U1” is “20s”, and the gender is “male”. Further, for example, the user identified by the user ID “U1” indicates that his / her home is “LC11”. Further, for example, the user identified by the user ID “U1” indicates that the work location is “LC12”. Further, for example, the user identified by the user ID “U1” indicates that he is interested in “economy”. For example, for the user U1, the position information indicating that the user U1 is located in the partial area LC1-1 of the area A is the position information acquired last before a predetermined date and time, that is, the latest position information.

  The user information storage unit 122 is not limited to the above, and may store various types of information according to purposes. For example, the user information storage unit 122 may store other demographic attribute information or psychographic attribute information in addition to age and gender as user attribute information. For example, the user information storage unit 122 may store information such as name, family structure, income, and the like.

(Behavior information storage unit 123)
The behavior information storage unit 123 according to the embodiment stores various information related to the behavior of the user. FIG. 5 is a diagram illustrating an example of the behavior information storage unit according to the embodiment. For example, the behavior information storage unit 123 stores the behavior information of the user with respect to the content provided to the terminal device 10 of each user. The action information storage unit 123 illustrated in FIG. 5 includes items such as “user ID”, “action ID”, “type”, “contents”, “date and time”, and “position”. In addition, the action of the user is not limited to the action actively performed by the user, and may include various actions such as an action performed on the user, that is, a passive action of the user.

  “User ID” indicates identification information for identifying a user. For example, the user identified by the user ID “U1” corresponds to the user U1 shown in the example of FIG. “Action ID” indicates information for identifying the action of the user. “Type” indicates information on the type of action of the corresponding user. “Content” indicates the content targeted in the action of the corresponding user. “Date and time” indicates the date and time when the corresponding user action was performed. Although the “date and time” is illustrated in an abstract manner such as “dt11”, a specific date and time such as “September 13, 2018, 22:31:52” may be stored.

  “Position” indicates position information of the user corresponding to the behavior of the user. For example, “position” indicates the position information of the user when the behavior information of the user is acquired. In the example of FIG. 5, “position” is illustrated by an abstract code “LC1-1” or the like, but specific “latitude / longitude information” may be stored in “position information”. Further, the “location information” may include a POI (point of interest) name, an area, or the like indicating a facility name such as a store or a stadium.

  For example, in the example of FIG. 5, the user identified by the user ID “U1” (user U1) is identified by the action ID “AC11” (action AC11) and the action identified by the action ID “AC12” (action AC11). Action AC12) and the like are performed. Specifically, the example of FIG. 5 indicates that the notification of the content CT11 (action AC11) was performed on the user U1 located in the partial area LC1-1 at the date and time dt11. For example, the action AC11 indicates that the content CT11 was provided by the push notification to the terminal device 10 used by the user U1 at the date and time dt11. In the example of FIG. 5, the user U1 has performed an operation of opening the content CT11 in the partial area LC1-1 (action AC12) at the date and time dt12 after the push notification of the content CT11 at the date and time dt11. . Further, in the example of FIG. 5, the user U1 has viewed the content CT11 (behavior AC13) during the time RTM1 (for example, 5 minutes or 10 minutes). For example, the user U1 indicates that the user has browsed the content CT11 (behavior AC13) in the partial area LC1-1 over the time RTM1 from the date and time dt12 to the date and time dt13.

  The behavior information storage unit 123 is not limited to the above, and may store various types of information according to purposes. For example, the behavior information storage unit 123 may store the position information of the user at the time of acquiring each behavior information in association with each behavior information. FIG. 5 shows a case where the behavior information is stored in the behavior information storage unit 123 for each user ID, but the behavior information is not limited to each user ID, and may be stored, for example, in chronological order.

(Distribution information storage unit 124)
The distribution information storage unit 124 according to the embodiment stores various information related to distribution. FIG. 6 is a diagram illustrating an example of the distribution information storage unit according to the embodiment. For example, the distribution information storage unit 124 stores distribution information of each aggregation target area. The distribution information storage unit 124 illustrated in FIG. 6 includes items such as “counting target area” and “distribution information”. The “distribution information” includes items such as “partial area”, “push count”, “open count”, and “open rate (%)”.

  “Aggregation target area” indicates an area from which distribution information is collected. “Distribution information” indicates distribution information collected for the aggregation target area. The “partial area” indicates a partial area in the counting target area. The “number of pushes” indicates the number of times a push notification has been made to the user located in the corresponding partial area. The “number of times of opening” indicates the number of times the user located in the corresponding partial area has opened the push notification. The “opening rate (%)” indicates the rate at which the user located in the corresponding partial area has opened the push notification (= opening times / pushing times × 100). For example, the “opening rate (%)” takes a value of 0 to 100 (%).

  For example, in the example shown in FIG. 6, it indicates that distribution information has been collected for the aggregation target area “A area”. The counting target area “A area” corresponds to the area AR11 in FIG. For example, it indicates that a push notification of “10000 (times)” has been issued to the user located in the partial area LC1-1 in the aggregation target area “A area”. Further, for example, it indicates that the user located in the partial area LC1-1 in the aggregation target area “A area” has opened the push notification “2000 (times)”. Further, for example, the opening rate of the user located in the partial area LC1-1 in the aggregation target area “A area” is “20 (%)” because the notification was opened 2,000 times out of 10,000 notifications. It is shown that.

  Note that the distribution information storage unit 124 is not limited to the above, and may store various types of information according to purposes.

(Model information storage unit 125)
The model information storage unit 125 according to the embodiment stores information on a model. For example, the model information storage unit 125 stores model information used for generating prediction information. FIG. 7 is a diagram illustrating an example of the model information storage unit according to the embodiment. The model information storage unit 125 illustrated in FIG. 7 stores items such as “feature amount # 1 (cafe A × soccer)” to “feature amount # 3 (cafe A × economy)” and the like corresponding to the model M1 and the like as model information. Having.

  For example, in the example illustrated in FIG. 7, the model information regarding the model M1 is such that the weight of the feature amount # 1 corresponding to the combination of the cafe A and the soccer is “0.5”, and the feature amount # 1 corresponding to the combination of the cafe A and the entertainment. 2 indicates that the weight of the feature quantity # 3 corresponding to the combination of the cafe A and the economy is “0.2” or the like. For example, when the features (features) of the model are represented by m-dimensional vectors, the number of features (features) is m, and the weights of the feature quantities ## 1 to #m are stored. For example, the weight of each feature amount is a real number, and may be a negative value.

  The model information storage unit 125 is not limited to the above, and may store various model information according to the purpose. For example, the model information storage unit 125 may store various model information such as a probability model.

(Prediction information storage unit 126)
The prediction information storage unit 126 according to the embodiment stores various information related to prediction. For example, the prediction information storage unit 126 stores various information related to prediction and various information generated by prediction. FIG. 8 is a diagram illustrating an example of a prediction information storage unit according to the embodiment. The prediction information storage unit 126 illustrated in FIG. 8 includes items such as “prediction target area” and “prediction information”. The “prediction information” includes items such as “partial area”, “push count”, “open count”, and “open rate (%)”.

  The “prediction target area” indicates an area for which prediction information is to be generated. The “prediction information” indicates prediction information generated for the prediction target area. “Partial area” indicates a partial area in the prediction target area. The “number of pushes” indicates the number of times that a push notification is predicted to be performed to a user located in a corresponding partial area in the generation of prediction information. The “number of times of opening” indicates the number of times that the user located in the corresponding partial area is expected to open the push notification in the generation of the prediction information. The “opening rate (%)” indicates a ratio (= opening times / pushing times × 100) at which the user located in the corresponding partial area is expected to open the push notification in the generation of the prediction information. For example, the “opening rate (%)” takes a value of 0 to 100 (%).

  For example, in the example illustrated in FIG. 8, it indicates that prediction information has been generated for the prediction target area “B area”. The prediction target area “B area” corresponds to the area AR21 in FIG. For example, it indicates that it is predicted that a push notification of “2000 (times)” will be given to the user located in the partial area LC2-1 in the prediction target area “B area”. Further, for example, it indicates that a user located in the partial area LC2-1 in the prediction target area “B area” is predicted to open the push notification “40 (times)”. Further, for example, since the opening rate of the user located in the partial area LC2-1 in the prediction target area “B area” is predicted to be opened 40 times out of 2,000 notifications, “2 (% ) ".

  The prediction information storage unit 126 is not limited to the above, and may store various types of information according to purposes.

(Relationship information storage unit 127)
The related information storage unit 127 according to the embodiment stores various information related to elements related to a predetermined action of the user. For example, the related information storage unit 127 stores various types of information related to the feature amount related to the predetermined behavior of the user among the feature amounts of the model that predicts the predetermined behavior of the user. FIG. 9 is a diagram illustrating an example of a related information storage unit according to the embodiment. The relation information storage unit 127 illustrated in FIG. 9 includes items such as “target action”, “rank”, “relation information ID”, “weight”, “element # 1”, “element # 2”, and “element # 3”. included. The relation information storage unit 127 includes not only “element # 1” to “element # 3” but also items such as “element # 4” and “element # 5”, which correspond to the number of elements corresponding to the feature amount. It is.

  “Target action” indicates a predetermined action of the target user. The “rank” indicates the rank (ranking) of the degree of the relationship between each piece of related information and the target action. In the example of FIG. 9, “rank” indicates a case where the higher the absolute value of the weight of each piece of related information is, the higher the rank (ranking) is assigned. “Related information ID” indicates information for identifying the related information. “Weight” indicates the weight of the feature amount corresponding to the relationship information. “Element # 1” to “Element # 3” indicate the elements of the feature amount corresponding to the related information.

  The example illustrated in FIG. 9 indicates that the target action is an action on the content of the user. For example, it indicates that the target action is an action such as opening the content of the user. Further, the relation information (relation information ST1) identified by the relation information ID “ST1” corresponds to a feature amount having a weight of “0.5”, element # 1 is “cafe A”, and element # 2 is Indicates "soccer". That is, the relationship information ST1 corresponds to the feature amount # 1 of the model M1.

  The relationship information storage unit 127 is not limited to the above, and may store various types of information according to purposes.

(Control unit 130)
Returning to the description of FIG. 3, the control unit 130 is a controller, and is stored in a storage device inside the information processing apparatus 100 by, for example, a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). Various programs (corresponding to an example of a determination program or an information processing program) are realized by executing the RAM using the RAM as a work area. The control unit 130 is 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 illustrated in FIG. 3, the control unit 130 includes an acquisition unit 131, a determination unit 132, a generation unit 133, and a provision unit 134, and implements or executes information processing functions and operations described below. . Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 3, and may be another configuration as long as it performs information processing to be described later.

(Acquisition unit 131)
The acquisition unit 131 acquires various information. The acquisition unit 131 acquires various information from an external device such as the terminal device 10. The acquisition unit 131 acquires various information from the storage unit 120. The acquisition unit 131 acquires various information from an external information processing device. The acquisition unit 131 obtains various types of information from the open data storage unit 121, the user information storage unit 122, the behavior information storage unit 123, the distribution information storage unit 124, the model information storage unit 125, the prediction information storage unit 126, and the related information storage unit 127. get. The acquisition unit 131 acquires behavior information of a predetermined user. The acquisition unit 131 acquires information on open data. The acquiring unit 131 acquires behavior information such as position information from the terminal device 10.

  The acquisition unit 131 acquires context information related to content distribution and content information related to content. The acquisition unit 131 acquires a plurality of combination information indicating each combination of a plurality of first elements of context information and a plurality of second elements of content information. The acquisition unit 131 acquires a model used for predicting a predetermined behavior of the user with respect to the content, using each combination of the plurality of first elements and the plurality of second elements as each feature amount.

  Acquisition unit 131 acquires a model generated based on correct information, which is behavior information regarding a predetermined behavior of the user, context information, and content information, for a content delivered to a user located in a predetermined area. . The acquisition unit 131 acquires a model generated based on correct information, which is behavior information on a predetermined behavior of the user, with respect to content delivered to the user, context information, and content information. The acquiring unit 131 acquires a model that includes a feature amount corresponding to each element and that is used for predicting a predetermined behavior of a user with respect to content based on context information of a context.

  The acquisition unit 131 acquires other behavior information on a predetermined behavior of another user with respect to content distributed to another user located in another area. The obtaining unit 131 obtains other behavior information on a predetermined behavior of another user with respect to content distributed to another user located in another area different from the predetermined area.

  The acquisition unit 131 acquires behavior information such as operation information indicating whether or not a predetermined operation has been performed by the user on content distributed to the terminal device 10 used by the user located in the predetermined area. The obtaining unit 131 obtains behavior information such as operation information indicating whether or not a predetermined operation has been performed by the user with respect to content that has been pushed by a terminal device used by the user located in the predetermined area. The acquisition unit 131 acquires behavior information such as operation information indicating whether or not there is an opening operation by the user with respect to the content notified by push in the terminal device used by the user located in the predetermined area.

  The acquisition unit 131 acquires behavior information such as operation information associated with the position information of the user when the user performs a predetermined operation on the content that has been subjected to the push notification in the predetermined area. The acquisition unit 131 acquires behavior information such as operation information in which time information when the user performs a predetermined operation is associated with the content for which push notification has been performed in the predetermined area. The acquisition unit 131 acquires behavior information such as operation information in which context information when a user performs a predetermined operation is associated with a content that has been pushed in a predetermined area. The acquisition unit 131 acquires behavior information such as operation information in which environment information about a predetermined area when a user performs a predetermined operation is associated with the content that has been pushed in the predetermined area.

  In the example of FIG. 1, the acquisition unit 131 acquires information indicating that a predetermined content has been pushed to the terminal device 10 used by a certain user and opened by the user. The acquisition unit 131 acquires information indicating that predetermined content has been pushed to the terminal device 10 used by the user U1 located in the area A and has been opened by the user U1. The acquisition unit 131 acquires information indicating that a predetermined content has been pushed to the terminal device 10 used by the user U1 located in the partial area LC1-1 of the A area and has been opened by the user U1.

  The acquisition unit 131 acquires information indicating that the notified content (content CT11) has been browsed by the user for the time RTM1 after being opened. The acquisition unit 131 may acquire information indicating that the content notified in step S1-1 is displayed on the user terminal device 10 during the time RTM1 after the content is opened.

  The acquisition unit 131 acquires information indicating that a predetermined content has been pushed to the terminal device 10 used by a certain user and has not been opened by the user. The obtaining unit 131 obtains information indicating that predetermined content has been pushed to the terminal device 10 used by the user U2 located in the area A and has not been opened by the user U2. The acquisition unit 131 acquires information indicating that predetermined content has been pushed to the terminal device 10 used by the user U2 located in the partial area LC1-2 of the A area and has not been opened by the user U2.

  The acquisition unit 131 acquires the open data of the prediction target area. In the example of FIG. 11, the acquisition unit 131 acquires the environment information ED of the B area. The acquisition unit 131 may acquire various information such as environment information of the B area from the open data storage unit 121 (see FIG. 3).

  Further, the acquisition unit 131 acquires user information located in the prediction target area. In the example of FIG. 11, the acquiring unit 131 acquires the user information UD of the B area. The acquiring unit 131 acquires user information as shown in the user information storage unit 122 (see FIG. 4) and user behavior information as shown in the behavior information storage unit 123 (see FIG. 5).

(Determining unit 132)
The determining unit 132 determines various information. The determining unit 132 determines various information. The determining unit 132 determines various information based on the various information stored in the storage unit 120. The determining unit 132 determines various information based on the various information stored in the storage unit 120. The determination unit 132 is based on the open data storage unit 121, the user information storage unit 122, the behavior information storage unit 123, the distribution information storage unit 124, the model information storage unit 125, the prediction information storage unit 126, the related information storage unit 127, and the like. , Determine various information. The determination unit 132 is based on the open data storage unit 121, the user information storage unit 122, the behavior information storage unit 123, the distribution information storage unit 124, the model information storage unit 125, the prediction information storage unit 126, the related information storage unit 127, and the like. Then, various information is determined. The determining unit 132 determines various information based on the various information acquired by the acquiring unit 131. The determining unit 132 determines various types of information based on the various types of information acquired by the acquiring unit 131. The determining unit 132 determines various information based on the various information generated by the generating unit 133. The determining unit 132 determines various types of information based on the various types of information generated by the generating unit 133. The determining unit 132 determines a model used for generating prediction information. In the example of FIG. 11, the determination unit 132 determines a model used for generating prediction information as the model M11.

  In the example of FIG. 1, the determining unit 132 determines, based on each combination of the element group EL1 corresponding to the context related to the distribution of the content and the element group EL2 corresponding to the content of the content such as the category of the content, of the context information. , And determine the feature amount of the model. For example, the determination unit 132 determines each combination of the first element of the element group EL1 and the second element of the element group EL2 as the feature amount of the model. For example, the determination unit 132 determines each combination of “Cafe A” or the like which is the first element of the element group EL1 and “Soccer” or “Entertainment” or the like which is the second element of the element group EL2 by using the feature amount of the model. To decide.

  The determination unit 132 determines a feature amount # 1 corresponding to a combination of cafe A and soccer, a feature amount # 2 corresponding to a combination of cafe A and entertainment, a feature amount # 3 corresponding to a combination of cafe A and economy, and the like. The feature amount of the model M1 is determined.

  The determining unit 132 provides the content to the user based on the generated related information. First, the determination unit 132 determines a user (target user) to which content is to be provided. The determination unit 132 determines the user U3 located at the shop SP1 that is the cafe A in the area B as the target user. The determination unit 132 determines the user U3, which is the second user, located in the area B, which is another area other than the area A, as the target user. The determining unit 132 determines the content to be provided to the target user, the user U3, based on the generated relation information. The deciding unit 132 decides to provide content related to soccer (soccer-related content CT31) to the user U3 located at the place “cafe A”.

  The determination unit 132 receives user information, environmental information around the user's location, and the like, and outputs a score based on the input from the model M11 that is equal to or greater than a predetermined threshold (for example, “0.5”). In this case, it may be determined that the user opens the content to be pushed. The deciding unit 132 inputs various information including the position information and the behavior information of the user U3 to the model M11, and determines that the user U3 does not open the push notification when the model M11 outputs a score less than a predetermined threshold. May be. The deciding unit 132 inputs various information including the position information and the action information of the user U4 to the model M11, and determines that the user U4 opens the push notification when the model M11 outputs a score equal to or more than a predetermined threshold. You may.

  The determination unit 132 determines an agent model to be applied to each agent. The determination unit 132 may determine an agent model to be applied to each agent based on information about the population structure of the B area included in the environmental information ED of the B area. The determination unit 132 may determine an agent model to be applied to each agent based on the population structure of the B area regarding age and gender as the attribute information.

(Generation unit 133)
The generation unit 133 generates various information. The generation unit 133 estimates various information. The generation unit 133 selects various information. The generation unit 133 extracts various information. The generation unit 133 calculates various information. The generation unit 133 estimates various information based on the various information stored in the storage unit 120. The generation unit 133 selects various information based on the various information stored in the storage unit 120. The generation unit 133 extracts various information based on the various information stored in the storage unit 120. The generation unit 133 calculates various information based on the various information stored in the storage unit 120. The generation unit 133 is based on the open data storage unit 121, the user information storage unit 122, the behavior information storage unit 123, the distribution information storage unit 124, the model information storage unit 125, the prediction information storage unit 126, the relation information storage unit 127, and the like. Estimate various information. The generation unit 133 is based on the open data storage unit 121, the user information storage unit 122, the behavior information storage unit 123, the distribution information storage unit 124, the model information storage unit 125, the prediction information storage unit 126, the relation information storage unit 127, and the like. , Select various information. The generation unit 133 is based on the open data storage unit 121, the user information storage unit 122, the behavior information storage unit 123, the distribution information storage unit 124, the model information storage unit 125, the prediction information storage unit 126, the relation information storage unit 127, and the like. And extract various information. The generation unit 133 estimates various information based on the various information acquired by the acquisition unit 131. The generation unit 133 selects various information based on the various information acquired by the acquisition unit 131. The generation unit 133 extracts various information based on the various information acquired by the acquisition unit 131. The generation unit 133 estimates the context of the user using the information acquired by the acquisition unit 131.

  The generation unit 133 generates various information stored in the storage unit 120. The generation unit 133 generates distribution information stored in the distribution information storage unit 124. The generation unit 133 generates a model stored in the model information storage unit 125. The generation unit 133 generates prediction information stored in the prediction information storage unit 126.

  The generation unit 133 generates, based on the context information and the content information acquired by the acquisition unit 131, relation information indicating a combination of a first element of context information related to content distribution and a second element of content information. Generate. The generation unit 133 generates relation information indicating a combination of the first element and the second element relating to the position of the distribution destination of the content. The generation unit 133 generates relation information indicating a combination of the first element and the second element regarding the time of content distribution. The generation unit 133 generates relation information indicating a combination of the first element related to the position and the second element of the information related to the content of the content.

  The generation unit 133 generates the relationship information indicating the combination with the first element and the second element by selecting the combination information related to the distribution of the content from the plurality of pieces of combination information. The generation unit 133 generates relation information indicating a combination with the first element and the second element by selecting a feature amount related to the distribution of the content from the feature amounts of the model. The generation unit 133 generates the relationship information by selecting, from each feature amount of the model, a feature amount whose corresponding weight satisfies a predetermined condition. The generation unit 133 generates the relationship information by selecting, from each feature amount of the model, a feature amount whose absolute value of the corresponding weight is equal to or greater than a predetermined threshold.

  The generation unit 133 generates relation information indicating a combination of the first element and the second element corresponding to another area different from the predetermined area. The generating unit 133 generates related element information based on the model. The generation unit 133 selects the feature amount related to the distribution of the content from the feature amounts of the model based on the other behavior information, thereby generating the relationship information indicating the combination with the first element and the second element. Generate. When the score based on the other action information is equal to or more than a predetermined threshold, the generation unit 133 selects the first element and the second element by selecting a feature amount related to content distribution from among the feature amounts of the model. And generating relation information indicating a combination with.

  In the example of FIG. 1, the generation unit 133 generates model information of the model M1 as shown in the model information MD. For example, the generation unit 133 generates the model M1 as shown in the model information storage unit 125 (see FIG. 7). The generation unit 133 generates the model M1 by learning the weight of each feature amount such as the feature amounts # 1 to # 3 of the model M1.

  The generation unit 133 generates, based on the generated model M1, relation information indicating a combination with the first element and the second element. The generation unit 133 generates, based on the generated model M1, relation information indicating the correspondence between the combination of the first element and the second element and the degree of relation between the content and the user's opening behavior. The generation unit 133 generates, based on the generated model M1, relation information indicating a degree of relation between the first element corresponding to the context related to the distribution of the content and the second element corresponding to the content, and the user's browsing behavior with respect to the content. I do.

  For example, the generation unit 133 generates relation information indicating a degree of relation between the content and a predetermined action of the user based on the weight of each feature amount of the model M1. The generation unit 133 generates the relationship information by selecting, from each of the feature amounts of the model M1, a feature amount whose corresponding weight satisfies a predetermined condition. The generation unit 133 generates the relationship information by selecting, from each of the feature values of the model M1, a feature value whose absolute value of the corresponding weight is larger than a predetermined threshold. In the example of FIG. 1, the generation unit 133 generates the relationship information by selecting, from each feature amount of the model M1, a feature amount whose absolute value of the corresponding weight is larger than the threshold “0.3”. The threshold value is not limited to “0.3” and may be set as appropriate. Further, for example, the generation unit 133 generates the relationship information in which the relationship information corresponding to each feature amount is ranked such that among the feature amounts of the model M1, a feature amount having a large absolute value of the weight has a higher rank. I do.

  The generating unit 133 selects, from the related information stored in the related information storage unit 127, the related information in which the element related to the place is “cafe A” or “cafe”. For example, the generation unit 133 selects a predetermined number (for example, 5 or 10) of pieces of related information in which the element related to the place is “cafe A”, “cafe”, or the like from a higher rank. In the example of FIG. 1, the generation unit 133 selects the related information ST1 and the related information ST4 including the element “cafe A” in the descending order.

  The generation unit 133, based on the behavior information such as the operation information acquired by the acquisition unit 131, when the content is distributed to the terminal device 10 used by a user located in another area different from the predetermined area, Predictive information for predicting whether the user has performed a predetermined operation on the content is generated. The generation unit 133 is configured to perform push notification of a content to a terminal device used by a user located in another area different from the predetermined area based on behavior information such as operation information acquired by the acquisition unit 131. Predictive information for predicting whether the user has performed a predetermined operation on the content is generated. The generation unit 133, when the content is pushed to a terminal device used by a user located in another area based on the behavior information such as the operation information acquired by the acquisition unit 131, the user of the user with respect to the content. It generates prediction information for predicting the presence or absence of the opening operation.

  The generation unit 133 generates prediction information for predicting a position at which a user performs a predetermined operation on content to be pushed in another area based on action information such as operation information. The generation unit 133 generates prediction information for predicting a time at which the user performs a predetermined operation on the content to be pushed in another area based on the behavior information such as the operation information.

  The generation unit 133 predicts, based on action information such as operation information and context information of a user located in another area, whether or not the user has performed a predetermined operation on the content to be pushed in another area. Generate prediction information. The generation unit 133 generates prediction information for predicting whether or not a user has performed a predetermined operation on content to be notified of a push in another area, based on action information such as operation information and environment information regarding another area. I do.

  The generation unit 133 generates, as prediction information, distribution information indicating a distribution related to users who perform a predetermined operation on content to be pushed in another area based on action information such as operation information. The generation unit 133 generates, as prediction information, a distribution map indicating a distribution in another area of a user who performs a predetermined operation on a content to be pushed in another area based on action information such as operation information. .

  The generation unit 133 generates distribution information based on action information such as operation information on push notification. In the example of FIG. 11, the generation unit 133 generates distribution information DM11 in which the area A is the aggregation target area. The generation unit 133 generates a model used to generate prediction information based on action information such as operation information related to push notification. The generation unit 133 generates a model that predicts whether or not to open the push notification based on action information such as operation information in the area A.

  The generation unit 133 generates a model for predicting whether to open the push notification based on various types of information stored in the open data storage unit 121, the user information storage unit 122, the action information storage unit 123, and the like. The generation unit 133 generates a model based on the environment information of the area A stored in the open data storage unit 121. The generation unit 133 generates a model based on the user information of the user located in the area A stored in the behavior information storage unit 123. The generation unit 133 generates a model based on the behavior information of the user located in the area A stored in the user information storage unit 122. The generation unit 133 may generate the model in consideration of information on the content to be pushed to the user.

  The generation unit 133 may generate a model that predicts whether or not to open the push notification, using the information when the content to be notified of the push is opened as the correct answer information (correct example). The generation unit 133 opens the push notification by using various context information such as the user information of the user, the user's location information, the user's behavior information, and the user's environment information when the content to be pushed is opened as the correct answer information. A model for predicting whether or not to do so may be generated. When the user U1 opens the content to be pushed and notified in the partial area LC1-1, the generation unit 133 performs various operations such as user information of the user U1, positional information of the user U1, behavior information of the user U1, and environmental information of the user U1. A model for predicting whether or not to open the push notification may be generated using the context information as the correct answer information.

  In addition, the generation unit 133 may generate a model that predicts whether or not the push notification is to be opened, using information when the content to be pushed to be opened is not opened as incorrect answer information (negative example). The generation unit 133 performs various types of context information such as user information of the user, user location information, user behavior information, and user environment information when the content to be pushed is not opened as incorrect information. A model for predicting whether to open the file may be generated. When the content to be pushed by the user U2 in the partial area LC1-2 is not opened, the generation unit 133 determines the user information of the user U2, the position information of the user U2, the behavior information of the user U2, the environment information of the user U2, and the like. A model for predicting whether or not to open the push notification may be generated using the various context information as incorrect information.

  In the example of FIG. 11, the generating unit 133 generates model information of the model M11 as shown in the model information MD. The generation unit 133 generates a model M11 as shown in the model information storage unit 125 (see FIG. 7). The generation unit 133 receives context information including user information and environment information around the user, and generates a model M11 that outputs a score based on the input. The generating unit 133 receives the context information and generates a model M11 that outputs a score based on the input. The generation unit 133 generates prediction information based on the generated model M11. The generation unit 133 generates prediction information regarding the B area based on the model M11 and various types of information regarding the B area that is the prediction target area.

  The generation unit 133 may generate prediction information for a user located in the area B in a predetermined time zone. The generation unit 133 may generate prediction information corresponding to the morning B area for a user located in the B area in the morning time zone (6:00 to 10:00). The generation unit 133 may generate prediction information corresponding to the night B area for a user located in the B area during the night time zone (19:00 to 23:00).

  In the example of FIG. 11, the generation unit 133 generates prediction information PM11 in which the area B is the prediction target area. The generation unit 133 generates prediction information for predicting the opening ratio of the push notification for the B area where the result of the push notification is insufficient.

(Provider 134)
The providing unit 134 provides various information. The providing unit 134 provides various information to an external information processing device. The providing unit 134 provides the terminal device 10 with various information. The providing unit 134 provides various information to an external information processing device such as the terminal device 10. The providing unit 134 transmits various types of information to the terminal device 10. The providing unit 134 distributes various types of information to the terminal device 10. The providing unit 134 provides various types of information based on the various types of information acquired by the acquiring unit 131. The providing unit 134 provides various information based on the various information determined by the determining unit 132. The providing unit 134 provides various information based on the various information generated by the generating unit 133. The providing unit 134 may provide various services based on the prediction information generated by the generating unit 133.

  In the example of FIG. 1, the providing unit 134 provides the content to the user based on the generated related information. The providing unit 134 provides the soccer-related content CT31 to the user U3. The providing unit 134 provides the soccer-related content CT31 to the terminal device 10-3 used by the user U3. The providing unit 134 transmits the soccer-related content CT31 to the terminal device 10-3 used by the user U3. The providing unit 134 distributes the soccer-related content CT31 to the terminal device 10-3 used by the user U3.

  The providing unit 134 may perform a push notification based on the generated prediction information. In the example of FIG. 11, the providing unit 134 may perform a push notification to a user located in a hatched portion (lower right in the B area) in the prediction information PM11. The providing unit 134 may provide the generated prediction information to an external information processing device. The providing unit 134 may provide the prediction information to an external device that performs a content providing service.

[3. Information processing flow)
Next, a procedure of information processing by the information processing system 1 according to the embodiment will be described with reference to FIG. FIG. 10 is a flowchart illustrating an example of information processing according to the embodiment.

  As shown in FIG. 10, the information processing apparatus 100 acquires context information related to distribution of content (step S101). Then, the information processing apparatus 100 acquires content information on the content (step S102). Then, the information processing apparatus 100 generates relation information indicating a combination of the first element of the context information and the second element of the content information based on the context information and the content information (step S103).

[4. effect〕
As described above, the information processing device 100 according to the embodiment includes the acquisition unit 131 and the generation unit 133. The acquisition unit 131 acquires context information related to content distribution and content information related to content. The generation unit 133 generates, based on the context information and the content information acquired by the acquisition unit 131, relation information indicating a combination of a first element of context information related to content distribution and a second element of content information. Generate.

  As described above, the information processing apparatus 100 according to the embodiment performs the first element of the context information related to the delivery of the content and the second element of the content information based on the context information related to the delivery of the content and the content information related to the content. By generating the relationship information indicating the combination with the content, it is possible to appropriately generate the information indicating the relationship between the context related to the distribution of the content and the content.

  Further, in the information processing apparatus 100 according to the embodiment, the generation unit 133 generates relation information indicating a combination of the first element and the second element relating to the position of the content distribution destination.

  As described above, the information processing apparatus 100 according to the embodiment generates the relationship information indicating the combination of the first element and the second element related to the position of the distribution destination of the content, thereby enabling the context and the content related to the distribution of the content to be generated. Can appropriately be generated.

  Further, in the information processing apparatus 100 according to the embodiment, the generation unit 133 generates relation information indicating a combination of the first element and the second element regarding the time of content distribution.

  As described above, the information processing apparatus 100 according to the embodiment generates the relationship information indicating the combination of the first element and the second element related to the time of content distribution, thereby enabling the context related to content distribution and the content to be generated. Information indicating the relationship can be appropriately generated.

  In the information processing apparatus 100 according to the embodiment, the generation unit 133 generates relation information indicating a combination of the first element related to the position and the second element of the information related to the content of the content.

  As described above, the information processing apparatus 100 according to the embodiment generates the context information related to the content distribution by generating the relationship information indicating the combination of the first element related to the position and the second element of the information related to the content of the content. Information indicating the relationship between the content and the content can be appropriately generated.

  In the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires a plurality of pieces of combination information indicating each combination of a plurality of first elements of context information and a plurality of second elements of content information. The generation unit 133 generates the relationship information indicating the combination with the first element and the second element by selecting the combination information related to the distribution of the content from the plurality of pieces of combination information.

  As described above, the information processing apparatus 100 according to the embodiment selects the combination information related to the distribution of the content from the plurality of pieces of combination information, thereby changing the relation information indicating the combination with the first element and the second element. By generating the information, it is possible to appropriately generate information indicating the relationship between the context related to the distribution of the content and the content.

  Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 sets a model used for predicting a predetermined behavior of the user with respect to the content by using each combination of the plurality of first elements and the plurality of second elements as each feature amount. get. The generation unit 133 generates relation information indicating a combination with the first element and the second element by selecting a feature amount related to the distribution of the content from the feature amounts of the model.

  As described above, the information processing apparatus 100 according to the embodiment uses each combination of the plurality of first elements and the plurality of second elements as each feature amount, and calculates each feature amount of the model used for predicting a predetermined behavior of the user with respect to the content. Information indicating the relationship between the context and the content related to the content distribution by generating the relationship information indicating the combination of the first element and the second element by selecting the feature amount related to the distribution of the content. Can be generated appropriately.

  In addition, in the information processing apparatus 100 according to the embodiment, the generation unit 133 generates the relationship information by selecting, from each of the feature amounts of the model, a feature amount whose corresponding weight satisfies a predetermined condition.

  As described above, the information processing apparatus 100 according to the embodiment distributes content by generating relation information by selecting a feature amount whose corresponding weight satisfies a predetermined condition from among the feature amounts of the model. It is possible to appropriately generate information indicating the relationship between the context and the content.

  Further, in the information processing apparatus 100 according to the embodiment, the generation unit 133 generates relation information by selecting, from each feature amount of the model, a feature amount whose absolute value of the corresponding weight is equal to or greater than a predetermined threshold. I do.

  As described above, the information processing apparatus 100 according to the embodiment generates relation information by selecting a feature amount whose absolute value of a corresponding weight is equal to or greater than a predetermined threshold value from among the feature amounts of the model. In addition, it is possible to appropriately generate information indicating the relationship between the content distribution context and the content.

  In the information processing apparatus 100 according to the embodiment, the acquisition unit 131 generates the content based on the correct information, which is behavior information on a predetermined behavior of the user, for the content distributed to the user, the context information, and the content information. Get the model that was created.

  As described above, the information processing apparatus 100 according to the embodiment generates a model generated based on correct information, which is behavior information regarding a predetermined behavior of the user, context information, and content information with respect to the content delivered to the user. By generating the related element information based on the information, it is possible to appropriately generate information indicating the relationship between the content and the context related to the distribution of the content.

  Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 corrects the content delivered to the user located in the predetermined area, which is correct information, which is behavior information on a predetermined behavior of the user, context information, and content information. To get the model generated based on The generation unit 133 generates relation information indicating a combination of the first element and the second element corresponding to another area different from the predetermined area.

  As described above, the information processing apparatus 100 according to the embodiment includes, for content delivered to a user located in a predetermined area, correct answer information that is behavior information on a predetermined behavior of the user, context information, and content information. On the basis of the model generated based on the generated model, by generating relation information indicating a combination of the first element and the second element corresponding to another area different from the predetermined area, the context related to the distribution of the content, the content, Can appropriately be generated. For example, the information processing apparatus 100 generates prediction information of a user located in another area (second area) where the information is insufficient, using information of a predetermined area (first area) where the information is sufficient. Accordingly, a desired prediction can be made even for an area having insufficient information.

  In the information processing apparatus 100 according to the embodiment, the acquisition unit 131 is a model including each feature amount corresponding to each element, and predicts a predetermined behavior of a user with respect to content based on context information of a context. Get the model to use. The generating unit 133 generates related element information based on the model.

  As described above, the information processing apparatus 100 according to the embodiment is a model including each feature amount corresponding to each element, and is a model used for predicting a predetermined behavior of the user with respect to the content based on the context information of the context. By generating the related element information based on the information, it is possible to appropriately generate information indicating the relationship between the content and the context related to the distribution of the content.

[5. Hardware configuration)
The information processing apparatus 100 according to the embodiment described above is realized by, for example, a computer 1000 having a configuration as shown in FIG. FIG. 12 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device. The computer 1000 includes a CPU 1100, a RAM 1200, a read only memory (ROM) 1300, a hard disk drive (HDD) 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F). ) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400, and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, a program that depends on hardware of the computer 1000, and the like.

  The HDD 1400 stores a program executed by the CPU 1100, data used by the program, and the like. The communication interface 1500 receives data from another device via the network N, sends the data to the CPU 1100, and transmits the data generated by the CPU 1100 to the other device via the network N.

  The CPU 1100 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 1600. The CPU 1100 obtains data from an input device via the input / output interface 1600. Further, CPU 1100 outputs the generated data to an output device via input / output interface 1600.

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

  For example, when the computer 1000 functions as the information processing apparatus 100 according to the embodiment, the CPU 1100 of the computer 1000 implements the function of the control unit 130 by executing a program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800. However, as another example, these programs may be obtained from another device via the network N.

  As described above, some of the embodiments of the present application have been described in detail with reference to the drawings. However, these are exemplifications, and various modifications based on the knowledge of those skilled in the art, including the aspects described in the disclosure row of the invention, The invention can be implemented in other modified forms.

[6. Others)
Further, of the processes described in the above embodiment, all or a part of the processes described as being performed automatically can be manually performed, or the processes described as being performed manually can be performed. All or part can be performed automatically by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the above documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown.

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

  Further, the processes described in the above-described embodiments can be appropriately combined within a range that does not contradict the processing contents.

  Further, the “section (section, module, unit)” described above can be read as “means”, “circuit”, or the like. For example, the acquiring unit can be replaced with an acquiring unit or an acquiring circuit.

REFERENCE SIGNS LIST 1 information processing system 100 information processing device 121 open data storage unit 122 user information storage unit 123 behavior information storage unit 124 distribution information storage unit 125 model information storage unit 126 prediction information storage unit 127 relational information storage unit 130 control unit 131 acquisition unit 132 Determining unit 133 generating unit 134 providing unit 10 terminal device 50 information providing device N network

Claims (12)

An acquisition unit that acquires context information related to content distribution and content information related to the content;
Based on the context information and the content information acquired by the acquisition unit, a relationship between a first element of the context information related to distribution of the content and a second element of the content information. A generating unit for generating,
An information processing apparatus comprising: The generation unit includes:
The information processing apparatus according to claim 1, wherein the relation information indicating a combination of the first element and the second element relating to a position of the distribution destination of the content is generated. The generation unit includes:
The information processing apparatus according to claim 1, wherein the relation information indicating a combination of the first element and the second element relating to a time of the distribution of the content is generated. The generation unit includes:
The information processing according to any one of claims 1 to 3, wherein relation information indicating a combination of the first element relating to a position and a second element of information related to the content of the content is generated. apparatus. The acquisition unit,
A plurality of combination information indicating each combination of a plurality of first elements of the context information and a plurality of second elements of the content information,
The generation unit includes:
The relation information indicating a combination with the first element and the second element is generated by selecting combination information related to the distribution of the content from the plurality of pieces of combination information. The information processing device according to any one of claims 1 to 4. The acquisition unit,
Each combination of the plurality of first elements and the plurality of second elements is used as a feature amount, and a model used for predicting a predetermined behavior of a user with respect to the content is obtained,
The generation unit includes:
Selecting, from among the feature quantities of the model, a feature quantity related to the distribution of the content, generating the relationship information indicating a combination with the first element and the second element. The information processing device according to claim 5. The generation unit includes:
The information processing apparatus according to claim 6, wherein the relation information is generated by selecting, from each of the feature amounts of the model, the feature amount whose corresponding weight satisfies a predetermined condition. The generation unit includes:
The relationship information is generated by selecting, from among the feature amounts of the model, the feature amount having an absolute value of a corresponding weight equal to or greater than a predetermined threshold value. An information processing apparatus according to claim 1. The acquisition unit,
Acquiring the model generated based on the correct answer information, which is behavior information on the predetermined behavior of the user, with respect to the content delivered to the user, the context information, and the content information. The information processing apparatus according to claim 6. The acquisition unit,
The model generated based on the correct information, which is the behavior information on the predetermined behavior of the user, for the content delivered to the user located in the predetermined area, the context information, and the content information. And get
The generation unit includes:
The information processing apparatus according to claim 9, wherein the relation information indicating a combination of the first element and the second element corresponding to another area different from the predetermined area is generated. An information processing method executed by a computer,
An acquisition step of acquiring context information on content distribution and content information on the content,
Based on the context information and the content information obtained in the obtaining step, a first element of the context information related to distribution of the content, and relation information indicating a combination with a second element of the content information. A generating step to generate;
An information processing method comprising: An acquisition procedure for acquiring context information on content distribution and content information on the content,
Based on the context information and the content information acquired by the acquisition procedure, a first element of the context information related to distribution of the content, and relation information indicating a combination with a second element of the content information. A generation procedure to generate,
An information processing program for causing a computer to execute the following.

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