JP6549675B2 - Learning apparatus, learning method and learning program - Google Patents

Description

  The present invention relates to a learning device, a learning method, and a learning program.

  In recent years, with the rapid spread of the Internet, information provision via the Internet has been actively performed. One example of information provision is the delivery of Internet advertisements. For example, in a mobile terminal such as a smart phone, an advertisement such as a banner is displayed together with the content on a display screen on which an application program (hereinafter also referred to as an “application”) or a browser displays the content. When the user clicks such an advertisement, for example, the download site of the application associated with the advertisement is displayed.

  The user may install the app on the mobile terminal via the advertisement as described above. In addition, the technique which transmits only the information regarding the installed specific application to a server as a technique regarding application installation is known (for example, patent document 1). In addition, when the selection operation by the user is performed at the off-line time, there is known a technology for executing a predetermined process when the on-line state is obtained by storing the selection operation (for example, Patent Document 2) ).

JP, 2014-167688, A JP, 2013-257683, A

  However, in the above-described prior art, matching between the provided information content and the user of the provision destination is not necessarily performed properly. Since delivery of advertisements (an example of information content) is expensive, it is desirable for advertisers to deliver advertisements preferentially to users who are likely to be effective. However, although the above-mentioned prior art can measure the result of the delivered advertisement, it accurately extracts the user who is likely to exhibit the effect of the advertisement, such as a user who can easily install the application newly through the advertisement. Things are difficult.

  The present application has been made in view of the above, and it is an object of the present invention to provide a learning device, a learning method, and a learning program capable of appropriately matching the provided information content with the user of the provision destination. .

  The learning apparatus according to the present application includes an acquisition unit that acquires terminal information that is information related to a terminal device that receives information content, result information indicating whether or not a predetermined action related to the predetermined information content is performed, and the acquisition unit Based on the model generated by the generation unit. The generation unit generates a model indicating a tendency to execute a predetermined action on the predetermined information content by learning a relationship with the terminal information acquired by And an identifying unit that identifies a terminal device to which the predetermined information content is to be provided, and the acquiring unit is an application that is information related to an application installed in the terminal device as the terminal information. Information that is information and includes an introduction of an application set for the application from the provider side providing the application Tokushi, the generating unit, based on the information specified based on the type or element of the application extracted from introduction of the application, to generate the model, characterized in that.

  According to one aspect of the embodiment, it is possible to appropriately match the provided information content with the user of the provision destination.

FIG. 1 is a diagram illustrating an example of learning processing according to the embodiment. FIG. 2 is a diagram (1) illustrating an example of terminal information according to the embodiment. FIG. 3 is a diagram (2) illustrating an example of terminal information according to the embodiment. FIG. 4 is a diagram showing an example of the configuration of a learning processing system according to the embodiment. FIG. 5 is a diagram showing an example of the configuration of a learning device according to the embodiment. FIG. 6 is a diagram illustrating an example of the advertisement information storage unit according to the embodiment. FIG. 7 is a diagram showing an example of the attribute table according to the embodiment. FIG. 8 is a diagram showing an example of an apparatus table according to the embodiment. FIG. 9 is a diagram showing an example of the application table according to the embodiment. FIG. 10 is a diagram (1) illustrating an example of a setting table according to the embodiment. FIG. 11 is a diagram (2) illustrating an example of the setting table according to the embodiment. FIG. 12 is a diagram showing an example of a variable table according to the embodiment. FIG. 13 is a diagram showing an example of a model table according to the embodiment. FIG. 14 is a diagram illustrating an exemplary configuration of a user terminal according to the embodiment. FIG. 15 is a flowchart (1) illustrating a processing procedure according to the embodiment. FIG. 16 is a flowchart (2) illustrating a processing procedure according to the embodiment. FIG. 17 is a hardware configuration diagram showing an example of a computer that implements the function of the learning device.

  Hereinafter, a learning apparatus, a learning method, and a mode for implementing a learning program according to the present application (hereinafter, referred to as “embodiment”) will be described in detail with reference to the drawings. Note that the learning device, the learning method, and the learning program according to the present application are not limited by this embodiment. Moreover, it is possible to combine each embodiment suitably in the range which does not make process contents contradictory. Moreover, the same code | symbol is attached | subjected to the same site | part in the following each embodiment, and the overlapping description is abbreviate | omitted.

[1. Example of learning process]
First, an example of the learning process according to the embodiment will be described using FIGS. 1 to 3. FIG. 1 is a diagram illustrating an example of learning processing according to the embodiment. In Figure 1, the learning apparatus 100 according to the present application, the result information indicating whether or not perform the predetermined action on predetermined information content, and the terminal information that the user terminal 10 ₁ as a providing destination of the predetermined information content has An example is shown in which the process of learning the relationship of is performed. In the embodiment, the information content is an advertisement displayed on a web page, and an advertisement for promoting a predetermined application is described as an example. In this case, the information provider is an advertiser. In the embodiment, as a predetermined action related to predetermined information content, an action of installing an application corresponding to a predetermined advertisement on a terminal device will be described as an example.

  The learning device 100 illustrated in FIG. 1 is a server device that holds an advertisement submitted from an advertiser. When the learning device 100 receives a request for distributing an advertisement from a terminal device operated by the user, the learning device 100 selects an advertisement to be distributed to the terminal device from among the held advertisements. Then, the learning device 100 distributes the selected advertisement to the terminal device.

The user terminal 10 ₁ and 10 ₂ shown in FIG. 1 is an information processing terminal such as a smartphone. In embodiments, the user terminal 10 ₁ is utilized by the user U01. The user terminal 10 ₂ is used by the user U02. The user terminal 10 ₁ and 10 ₂ are obtained content (e.g., web pages) when there is the advertisement display area is to serve ads to be displayed on the advertisement display area, it requests the learning apparatus 100. Then, the user terminal 10 ₁ and 10 ₂ displays the acquired advertisement to the advertisement display area. In the following, when it is not necessary to distinguish the user terminals 10 ₁ and 10 ₂ are referred to as "user terminal 10".

  An advertiser terminal 20 shown in FIG. 1 is a terminal device used by an advertiser. For example, the advertiser terminal 20 submits an advertisement to the learning device 100 according to an operation by the advertiser. In the embodiment, an advertisement is associated with an advertisement submitted by an advertiser as a target of advertisement. For example, when the user clicks an advertisement displayed on the user terminal 10, the user terminal 10 installs an application associated with the advertisement. Alternatively, the user terminal 10 transitions the screen display to the download page of the application associated with the advertisement.

  In FIG. 1, when receiving a request for advertisement distribution from the user terminal 10, the learning apparatus 100 determines an appropriate advertisement to be distributed to the user terminal 10, that is, an advertisement assumed to have a high advertisement effect. For example, in the case where an advertisement associated with an application is delivered, is the learning device 100 likely to install the application associated with the advertisement in the user terminal 10 at the user terminal 10 that is the delivery destination? It is determined whether or not. The learning device 100 generates a model for evaluating the possibility of the application being installed in the user terminal 10 for the above determination process. Then, the learning device 100 specifies the user terminal 10 as the distribution destination of the advertisement using the generated model. For example, the learning device 100 specifies the user terminal 10 of the distribution destination by calculating an index value such as the probability of the application being installed when the advertisement is distributed to the user terminal 10 based on the model. Hereinafter, an example of the learning process performed by the learning device 100 will be described along the flow using FIG. 1.

First, the learning device 100 receives submission of an advertisement related to an application from the advertiser via the advertiser terminal 20 (step S11). The learning device 100 stores the received advertisement in the advertisement information storage unit 121. In addition, the learning device 100 identifies an application associated with an advertisement. Then, the learning device 100 performs model generation processing on the application as a processing target. That is, the learning device 100 generates a model for each application. This can be reworded as a model is generated for each advertisement when the application and the advertisement are associated on a one-to-one basis. Note that the learning apparatus 100 distributes the advertisement submitted from the advertiser to the user terminal 10 without using the model until the information required for generating the model is stored (step S12). In the example of FIG. 1, a user terminal 10 which ads were served without the model it is used, and be referred to as user terminal 10 _1.

Subsequently, the learning device 100, advertisements are processed app is correlated with the user terminal 10 ₁ delivered, it acquires the terminal information which is information relating to the user terminal 10 ₁ (step S13). Incidentally, although not shown in FIG. 1, _one user terminal 10 to which ads were served shall present enough considerable number to perform the learning process. For details on terminal information will be described later, the terminal information includes characteristics information indicating each user's identity utilizing user terminal 10 _1. Therefore, the learning apparatus 100, by acquiring the terminal information, it is possible to catch a tendency or has been utilized by the user U01 with what characteristics the user terminal 10 _1. The learning device 100 stores the acquired terminal information in the terminal information storage unit 122.

Further, the learning apparatus 100, the user terminal 10 ₁ ads associated with the application has been delivered to be processed, actually app acquires such result information whether it is installed. That is, the learning apparatus 100, for each user terminal 10 ₁ ad is served, acquires the result information such whether application is installed, and acquires the terminal information from the user terminal 10 _1.

Then, the learning apparatus 100 includes a result information indicating whether or not to install the application that has been processed by learning the relationship between the obtained terminal information from the user terminal 10 ₁ which ads were served, processes Generate a model that shows the trend of installing targeted apps. In other words, the learning device 100 performs machine learning on the user U01 who tends to install an application (step S14). More specifically, it is assumed that the learning apparatus 100 uses result information indicating whether or not the application to be processed has been installed as a target variable, is information extracted from the terminal information, and indicates the feature of the user. Generate a model with the feature information to be used as an explanatory variable. The learning device 100 stores the generated model in the learning model storage unit 126.

Learning apparatus 100 uses the generated model, based on the results of the machine learning, to deliver advertisements for application to the user terminal 10 ₂ (step S15). By using the generated model, the learning apparatus 100 can accurately identify a user who tends to install the application to be processed. For example, the learning apparatus 100, when receiving a request for advertisements distributed from the user terminal 10 _2, and acquires the terminal information of the user terminal 10 _2. Then, the learning apparatus 100 inputs the terminal information obtained from the user terminal 10 ₂ is ad delivery destination candidates in the model. Learning apparatus 100 determines based on the information output from the model, whether or not the user terminal 10 ₂ is being utilized by the user U02 likely to install the app. Learning apparatus 100, when the user terminal 10 ₂ is determined to have been utilized by the user U02 likely to install application identifies the user terminal 10 ₂ as the delivery destination ad. Then, the learning apparatus 100 delivers the advertisement to which the application is associated with the user terminal 10 _2.

  Thus, the learning device 100 acquires terminal information which is information related to the user terminal 10 that receives an advertisement. In addition, the learning device 100 is an application associated with an advertisement by learning a relationship between result information indicating whether an application associated with the advertisement has been installed and the acquired terminal information. Generate a model that shows the tendency to be installed. Furthermore, the learning device 100 specifies the user terminal 10 as a distribution destination to which the advertisement is distributed, based on the generated model.

  That is, the learning apparatus 100 generates a model that determines a user who easily performs a predetermined action, such as installing an application associated with an advertisement, based on terminal information. Then, the learning device 100 can distribute an advertisement to a user who tends to be similar to a user who has installed an application in the past by specifying the distribution destination of the advertisement based on the model. For this reason, the learning device 100 can increase the possibility of installing the application associated with the advertisement, as compared to the case where the advertisement is distributed without using a model. In other words, the learning device 100 can improve the advertising effect exhibited by the distributed advertisement. In this manner, the learning device 100 appropriately matches the distributed advertisement with the user of the distribution destination.

Incidentally, in FIG. 1, the learning device 100, advertisement showing an example of generating a model by using the terminal information of the user terminal 10 ₁ delivered. In this case, in order to increase the accuracy of the model, it is desirable for the learning apparatus 100 to extract a large amount of information that accurately characterizes the user U01 from the terminal information. Here, an example of the terminal information acquired by the learning device 100 will be described using FIGS. 2 and 3.

  FIG. 2 is a diagram (1) illustrating an example of terminal information according to the embodiment. In FIG. 2, information (hereinafter referred to as “device information”) that the device itself of the user terminal 10 has will be described as an example of the terminal information.

  For example, when receiving a request for advertisement distribution from the user terminal 10, the learning device 100 acquires terminal information of the user terminal 10. The device information included in the terminal information is, for example, the model number 40 set in the user terminal 10. The learning device 100 acquires the information of the model number 40 by acquiring identification information unique to the device transmitted from the user terminal 10, for example. Furthermore, the learning device 100 also acquires the feature information 42 extracted from the model number 40 as an example of the device information. For example, in the case where a brand name is attached to the user terminal 10 by the manufacturer who manufactured the user terminal 10, the learning device 100 extracts the brand name as the feature information based on the model number 40. In the example of FIG. 2, it is assumed that the brand name "AAA" is attached to the user terminal 10 by the manufacturer. The learning apparatus 100 can perform the above extraction processing by referring to a predetermined database in which the association between the model number 40 and the brand name “AAA” is stored.

  In addition, the learning device 100 extracts, for example, feature information that “336 days” has passed since the user terminal 10 has been released based on the model number 40. Further, the learning device 100 extracts feature information indicating that the communication carrier is “BBB” based on the model number 40. Also, the learning device 100 extracts feature information that the resolution of the screen of the user terminal 10 is “1280 × 720” based on the model number 40.

  The learning device 100 uses the model number 40 extracted from the terminal information, the feature information 42, and the like as elements characterizing the user terminal 10. In other words, the learning device 100 extracts the feature of the user who uses the user terminal 10 based on the terminal information. This indicates that the model number 40 of the user terminal 10 and the feature information 42 function as an element characterizing the user.

  For example, the brand name “AAA” of the user terminal 10 is a brand generally preferred by men and has a sophisticated image. In this case, it is assumed that the user who is using the user terminal 10 given the brand name "AAA" is a male image and a person who likes a refined image. Thus, the learning device 100 can use the brand name of the user terminal 10 as an element characterizing the person of the user U01. In addition, the learning device 100 handles “the number of days elapsed from the release” as a characterization element indicating whether the user using the user terminal 10 prefers a relatively new type. In addition, the learning device 100 treats the “communication carrier” as an element of characterization, such as whether the user using the user terminal 10 desires a stable communication line or desires a low charge. Also, the learning device 100 handles “resolution” as an element of characterization such as whether the user prefers a relatively large screen.

  Then, the learning device 100 generates a model reflecting the user's characteristics by using the acquired device information as one of the explanatory variables in the model. For example, the learning device 100 learns whether the user tends to install the application to be processed and whether the brand name of the user terminal 10 used by the user is “AAA” or not, which has a predetermined relationship. Do. Similarly, the learning device 100 learns the relationship between the tendency of the user to install the processing target application, the number of days after the release of the user terminal 10, the communication carrier, and the resolution. Thus, the learning device 100 can use the information of the device itself of the user terminal 10 as an example of the element characterizing the user of the user terminal 10.

  In addition, the learning device 100 can use different information as learning as the terminal information. This point will be described with reference to FIG. FIG. 3 is a diagram (2) illustrating an example of terminal information according to the embodiment. In FIG. 3, information on an application installed in the user terminal 10 (hereinafter referred to as “application information”) will be described as an example of the terminal information. FIG. 3 shows a plurality of applications installed in the user terminal 10.

  In the example illustrated in FIG. 3, the user of the user terminal 10 installs the communication application 50 or 52 performing communication with another user in the user terminal 10. In addition, the user installs a traffic application 54, a finance application 56, a shopping application 58 or 60, an exercise application 62 or 64, a rental search application 66, a parenting application 68, a strategy game application 70, and a voice actor system game application 72. ing.

  Here, the learning apparatus 100 refers to a predetermined database in which an association between an application installed in the user terminal 10 and feature information which is an element characterizing the user is stored. For example, in the predetermined database, it is assumed that the communication applications 50 and 52 are associated with feature information such as “lifestyle” or “SNS (Social Networking Service)”. This is because users who use applications of the type that have communication with other users as their main function, such as communication applications 50 and 52, tend to be interested in elements such as "lifestyle" and "SNS" It is presumed that it is a user. Similarly, the traffic application 54 is associated with feature information such as “transfer guidance”, and the finance application 56 is associated with feature information such as “stock price / forex”.

  Then, based on the application installed in the user terminal 10, the learning device 100 extracts feature information that is an element that characterizes the user. For example, the learning device 100 acquires terminal information that the communication application 50 or 52 is installed in the user terminal 10. In this case, the learning device 100 characterizes the user using the user terminal 10 as having an interest in “lifestyle” or “SNS”.

  Specifically, when the communication application 50 or 52 is installed in the user terminal 10, the learning apparatus 100 uses variables such as “interest_lifestyle” and “interest_SNS” as explanatory variables for describing the user terminal 10. Is assigned to the user terminal 10. In addition, when the traffic application 54 or the finance application 56 is installed, the learning device 100 appropriately assigns variables such as “interest_transfer guidance” and “interest_stock price / forex” to the user terminal 10.

  In addition, when the application regarding a game is installed in the user terminal 10, you may make it the learning apparatus 100 assign a variable different from the variable used by applications other than a game. This is because, in the case of an application relating to a game, subdividing the characterization of the user for each type of game can be an element that more clearly indicates the characteristics of the user.

  For example, when the game application is installed in the user terminal 10, the learning apparatus 100 assigns, to the user terminal 10, a variable classified by the type of “game preference” as an explanatory variable for describing the user terminal 10. Specifically, when the strategic game application 70 is installed on the user terminal 10, the learning device 100 assigns variables such as “game preference_strategy” and “game preference_simulation” to the user terminal 10. Further, when the voice actor system game application 72 is installed, the learning device 100 assigns variables such as “game preference_voice actor” and “game preference_training” to the user terminal 10. Feature information 80, which is a set of variables extracted from the application installed in the user terminal 10, is generated for each user terminal 10 (that is, for each user).

  As described above, by using the acquired application information as an element of the explanatory variable in the model, the learning apparatus 100 can generate a model reflecting the user's feature. That is, the learning apparatus 100 learns how the user is installing the application to be processed has a relationship with the application used by the user. For example, the learning device 100 learns the relationship between a user who has installed an application to be processed and an application other than the processing target used by the user. As a result, the learning apparatus 100 can generate a model capable of evaluating the possibility that a user using any application is likely to install the application to be processed in the future. As described above, the learning device 100 uses the application information of the application installed in the user terminal 10 as an example of the element characterizing the user of the user terminal 10. Although details will be described later, the learning device 100 can also be used for learning as application information regarding the total number of applications installed in the user terminal 10, the number of game applications and applications other than games, and the like.

  As described above, by using the terminal information that can be acquired from the user terminal 10, the learning apparatus 100 generates a model indicating a tendency of the application to be processed being installed on the user terminal 10. Hereinafter, the learning device 100 that performs such processing, and the configuration of the learning processing system 1 including the learning device 100 will be described in detail.

[2. Configuration of learning processing system]
The configuration of the learning processing system 1 including the learning device 100 according to the embodiment will be described using FIG. 4. FIG. 4 is a view showing a configuration example of the learning processing system 1 according to the embodiment. As illustrated in FIG. 4, the learning processing system 1 according to the embodiment includes the user terminal 10, the advertiser terminal 20, the web server 30, and the learning device 100. These various devices are communicably connected via a network N by wire or wirelessly. The learning processing system 1 illustrated in FIG. 4 may include a plurality of user terminals 10, a plurality of advertiser terminals 20, and a plurality of web servers 30.

  The user terminal 10 is, for example, an information processing apparatus such as a smartphone, a desktop PC (Personal Computer), a notebook PC, a tablet terminal, a mobile phone, a PDA (Personal Digital Assistant), a wearable device (Wearable Device), etc. It is. The user terminal 10 acquires the web page from the web site provided from the web server 30 by accessing the web server 30 according to the operation by the user. Then, the user terminal 10 displays the acquired web page on a display device (for example, a liquid crystal display). In the present specification, the user and the user terminal 10 may be regarded as identical. For example, “providing information content to the user” may actually mean “providing information content to the user terminal 10 used by the user”.

  The advertiser terminal 20 is an information processing device used by an advertiser who requests the learning device 100 to distribute an advertisement. The advertiser terminal 20 submits an advertisement relating to an application to the learning device 100 according to the operation by the advertiser.

  The advertiser may use the advertiser terminal 20 to request an agency for such submission without sending an advertisement to the learning device 100. In this case, it is an agent to submit an advertisement to the learning device 100 or the like. In the following, the expression "advertiser" is a concept that includes not only the advertiser but also the agency, and the expression "advertiser terminal" includes the agency device used by the agency as well as the advertiser terminal. It is a concept.

  The web server 30 is a server device that provides various web pages when accessed from the user terminal 10. The web server 30 provides various web pages regarding, for example, a news site, a weather forecast site, a shopping site, a finance (stock price) site, a route search site, a map providing site, a travel site, a restaurant introduction site, a web blog and the like.

  As described above, the web page provided by the web server 30 includes an advertisement space, which is a display area for displaying an advertisement. The web page including the advertisement space includes an acquisition command for acquiring information content to be displayed in the advertisement space. For example, the URL or the like of the learning apparatus 100 is described as an acquisition command in an HTML (Hyper Text Markup Language) file or the like forming a web page. The user terminal 10 that has acquired the web page receives the distribution of the advertisement from the learning device 100 by accessing the URL described in the HTML file or the like.

  The learning device 100 is a server device that appropriately specifies the user terminal 10 as a distribution destination for the advertisement received from the advertiser terminal 20. As described above, the learning device 100 generates a model based on result information indicating whether an application has been installed in the user terminal 10 and the terminal information of the user terminal 10. Then, using the generated model, the learning device 100 specifies the user terminal 10 as the distribution destination.

  In addition, as described above, the learning device 100 identifies the user terminal 10 or acquires terminal information of the user terminal 10 in distributing the advertisement. For example, acquisition of the terminal information of the user terminal 10 can be performed by including information in the cookie exchanged between the web browser or browser application of the user terminal 10 and the learning device 100. However, the method of acquiring terminal information is not limited to the above. For example, a dedicated program may be set in the user terminal 10, and terminal information may be transmitted to the learning device 100 from the dedicated program. The learning apparatus 100 may also acquire terminal information of the user terminal 10 from the web server 30 accessed from the user terminal 10.

[3. Configuration of learning device]
Next, the configuration of the learning device 100 according to the embodiment will be described with reference to FIG. FIG. 5 is a diagram showing a configuration example of the learning device 100 according to the embodiment. As illustrated in FIG. 5, the learning device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The learning apparatus 100 includes an input unit (for example, a keyboard or a mouse) that receives various operations from a manager or the like who uses the learning apparatus 100, and a display unit (for example, a liquid crystal display or the like) for displaying various information. You may have.

(About communication unit 110)
The communication unit 110 is realized by, for example, a network interface card (NIC). The communication unit 110 is connected to the network N in a wired or wireless manner, and transmits and receives information to and from the user terminal 10, the advertiser terminal 20, and the web server 30 via the network N.

(About storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 includes an advertisement information storage unit 121, a terminal information storage unit 122, and a learning model storage unit 126.

(About advertisement information storage unit 121)
The advertisement information storage unit 121 stores information on an advertisement submitted from the advertiser terminal 20. Here, FIG. 6 illustrates an example of the advertisement information storage unit 121 according to the embodiment. FIG. 6 is a diagram illustrating an example of the advertisement information storage unit 121 according to the embodiment. In the example illustrated in FIG. 6, the advertisement information storage unit 121 has items such as "advertiser ID", "advertisement ID", and "corresponding application ID".

  The “advertiser ID” indicates identification information for identifying the advertiser or the advertiser terminal 20. "Ad ID" indicates identification information for identifying an advertisement submitted by an advertiser. "Corresponding application ID" indicates identification information for identifying an application associated with an advertisement.

  In the present specification, identification information as shown in FIG. 6 may be used as a reference code. For example, the advertiser identified by the advertiser ID "B10" is identified by "advertiser B10", the advertisement identified by the advertisement ID "C10" by "advertisement C10", and the (corresponding) application ID "A10". Application may be described as "application A10".

  That is, an example of the data illustrated in FIG. 6 indicates that the advertiser B10 identified by the advertiser ID “B10” submits an advertisement identified by the advertisement IDs “C10” and “C11”. . Further, the application associated with the advertisement C10 indicates that the application A10 is identified by the corresponding application ID “A10”.

  Note that the application may be associated with a plurality of advertisements instead of being associated with an advertisement on a one-to-one basis. For example, as shown in FIG. 6, the application A20 is associated with the advertisement C20 and the advertisement C21. This indicates that the advertisement C20 and the advertisement C21 are targeted for advertising the same application A20, although the contents such as the image data of the advertisement are different.

  The content data (text data, moving image content, still image content) of the advertisement actually distributed to the user terminal 10 may be stored in a predetermined storage server provided separately from the learning device 100. In this case, the learning apparatus 100 specifies an advertisement stored in an external storage server based on the advertisement ID stored in the advertisement information storage unit 121. Then, the learning device 100 controls the storage server to distribute the identified advertisement to the user terminal 10.

  Also, the advertisement information storage unit 121 may store other information related to the advertisement. For example, the advertisement information storage unit 121 may store conditions of a delivery destination designated for each advertisement, the number of distributions designated for each advertisement (designated impression number), and the like. Further, the advertisement information storage unit 121 may store an index value indicating an advertisement effect. For example, in the advertisement information storage unit 121, index values such as CPI (Cost Per Install) and CTR (Click Through Rate) may be stored for each advertisement.

(About the terminal information storage unit 122)
The terminal information storage unit 122 stores information on the user terminal 10 that is an advertisement distribution target. As shown in FIG. 5, the terminal information storage unit 122 includes an attribute table 123, an apparatus table 124, and an application table 125 as data tables for storing terminal information.

(About attribute table 123)
Here, FIG. 7 shows an example of the attribute table 123 according to the embodiment. FIG. 7 is a diagram showing an example of the attribute table 123 according to the embodiment. The attribute table 123 mainly stores information on the attributes of the user who uses the user terminal 10. In the example illustrated in FIG. 7, the attribute table 123 has items such as “terminal ID”, “sex”, “age”, and “region”.

  The “terminal ID” is identification information for identifying the user terminal 10. “Gender” indicates the gender of the user who uses the user terminal 10. “Age” indicates the age of the user who uses the user terminal 10. “Area” indicates the residence of the user who uses the user terminal 10. The “area” may store not a specific address but an area name (such as the Kanto region) indicating a certain range corresponding to the residence of the user, a country name, and the like.

  That is, as an example of the data shown in FIG. 7, the gender of the user of the user terminal 10 identified by the terminal ID "F11" is "male", the age is "30's", and the resident area is " Indicates A prefecture.

  The attribute information stored in the attribute table 123 may not necessarily be accurate information. For example, the learning device 100 may store, for example, “estimated gender” or “estimated age” estimated from the device information in the attribute table 123. Further, in addition to the “area”, the attribute table 123 may store information such as “city degree rank” indicating the city degree of the area.

(About device table 124)
Subsequently, FIG. 8 illustrates an example of the device table 124 according to the embodiment. FIG. 8 is a diagram showing an example of the device table 124 according to the embodiment. The device table 124 mainly stores device information indicating information of the user terminal 10 itself. In the example illustrated in FIG. 8, the device table 124 includes items such as “terminal ID”, “model number”, “brand name”, “elapsed days after release”, “communication carrier”, “maker name”, and “resolution”. Have.

  The “terminal ID” corresponds to the same item as shown in FIG. “Model number” indicates the model number of the user terminal 10. “Brand name” indicates a brand name given to the user terminal 10. “Elapsed days after release” indicates the number of days elapsed since the user terminal 10 was released. “Communication carrier” indicates the company name of the communication carrier that provides the communication line of the user terminal 10. “Resolution” indicates the resolution of the screen of the user terminal 10.

  That is, in the example of the data illustrated in FIG. 8, the user terminal 10 identified by the terminal ID “F11” indicates that the model number is “XX-YY01” and the brand name is “AAA”. In addition, “336 days” has passed since the user terminal 10 identified by the terminal ID “F11” has been released, the communication carrier is “BBB company”, and the manufacturer is “CCC company”, and the resolution is Indicates that "1280 x 720".

(About the app table 125)
Subsequently, FIG. 9 illustrates an example of the application table 125 according to the embodiment. FIG. 9 is a diagram showing an example of the application table 125 according to the embodiment. The application table 125 mainly stores information on applications installed in the user terminal 10. In the example shown in FIG. 9, the application table 125 includes "terminal ID", "number of installed applications", "number of non-game applications", "number of game applications", "number of new applications", "number of old applications", " It has an item such as “Installed application ID”.

  The “terminal ID” corresponds to the same item as shown in FIG. The “number of installed applications” indicates the total number of applications installed in the user terminal 10. The “non-game application number” indicates the number of applications other than the game application among the installed applications. The “number of game applications” indicates the number of game applications among the installed applications. The “number of new applications” indicates the number of applications that have been provided relatively recently (for example, within one year) among the installed applications. The “old production application number” indicates the number of applications other than the new application among the installed applications. “Installed application ID” indicates identification information of each application installed in the user terminal 10.

  That is, the example of the data illustrated in FIG. 9 indicates that “35” applications are installed in the user terminal 10 identified by the terminal ID “F11”. Further, among the applications installed in the user terminal 10 identified by the terminal ID “F11”, the number of non-game applications is “22” and the number of game applications is “13”. Further, among the applications installed in the user terminal 10 identified by the terminal ID “F11”, the number of new application applications is “15” and the number of old application applications is “20”. The application installed in the user terminal 10 identified by the terminal ID "F11" is identified by identification information such as "A101", "A103", "A107", "A108", "A122", etc. Indicates that the application is

(About the learning model storage unit 126)
The learning model storage unit 126 stores information related to the learning process of the learning device 100. As shown in FIG. 5, the learning model storage unit 126 includes a setting table 127, a variable table 128, and a model table 129 as data tables for storing information related to learning processing.

(About setting table 127)
The setting table 127 mainly stores information on settings of terminal information and variables extracted from the terminal information. The setting table 127 further includes a plurality of tables for each type of stored information. The setting content stored in the setting table 127 is, for example, input by the administrator of the learning apparatus 100 or automatically set based on information extracted from a site introducing an application or the like as described later. Be done.

  Here, FIG. 10 shows an example of the setting table 127 according to the embodiment. FIG. 10 is a diagram (1) illustrating an example of the setting table 127 according to the embodiment. FIG. 10 shows, as an example of the setting table 127, a variable setting table 127A in which correspondence between identification information of a variable used for learning and the type and content of the variable is set. In the example illustrated in FIG. 10, the variable setting table 127A has items such as “variable ID”, “type”, and “variable name”.

  "Variable ID" indicates identification information that identifies a variable. “Type” indicates the type corresponding to the variable ID. Types are used to classify variables. "Variable name" indicates the name of each variable obtained by dividing the type.

  That is, in the example of the data shown in FIG. 10, the variable identified by the variable ID "E26" is a variable relating to the type of "interest interest", and in particular, a variable indicating an interest in "net shopping" Show. Also, the variable identified by the variable ID “E58” is a variable relating to the type “game preference”, and particularly indicates a variable indicating an interest in “shooting”.

  Next, FIG. 11 shows another example of the setting table 127 according to the embodiment. FIG. 11 is a diagram (2) illustrating an example of the setting table 127 according to the embodiment. FIG. 11 shows, as an example of the setting table 127, an application setting table 127B in which variables associated with the application are set. In the example illustrated in FIG. 11, the application setting table 127B has items such as “application ID”, “type”, “element”, and “variable name”.

  The “application ID” indicates identification information that identifies an application. "Type" indicates the type of app. "Element" indicates an element that the app has. "Variable name" indicates a variable set based on the type or element of the application.

  That is, in the example of the data shown in FIG. 11, the application A51 identified by the application ID “A51” is an application belonging to the type “communication system”, and “communication”, “contact”, “chat” Indicates that the application has an element indicated by a keyword such as "SNS". Therefore, the application A 51 indicates that a variable identified by a name such as “interest_lifestyle”, “interest_SNS”, or “interest_encounter” is set.

(About variable table 128)
Next, FIG. 12 shows an example of the variable table 128 according to the embodiment. FIG. 12 is a diagram showing an example of the variable table 128 according to the embodiment. As shown in FIG. 12, the variable table 128 has items such as “processing target application ID”, “terminal ID”, “target application installation (target variable)”, and “feature information data (explanatory variable)”.

  The “processing target application ID” indicates identification information that identifies an application for which a model is to be generated. The “terminal ID” indicates identification information for identifying the user terminal 10.

  “Target application installation (target variable)” is information serving as a target variable in a model generated by the learning device 100. Specifically, “target application installation (target variable)” is information indicating whether the application to be processed is installed in the user terminal 10 or not. In the example of FIG. 12, if the item of “target application installation (target variable)” is “1”, it indicates that the processing target application is installed in the user terminal 10. On the other hand, if the item of “target application installation (target variable)” is “0”, it indicates that the processing target application is not installed in the user terminal 10.

  “Characteristic information data (explanatory variable)” is information serving as an explanatory variable in a model generated by the learning device 100. In the example of FIG. 12, it is conceptually described as “G11”, but in fact, “feature information data (explanatory variable)” is data in which a plurality of explanatory variables possessed by the user terminal 10 are listed. It is. That is, the feature information data is a collection of feature information extracted from the terminal information of the user terminal 10, and is data indicating the feature of the user terminal 10 (in other words, the user who uses the user terminal 10).

  That is, in the example of the data shown in FIG. 12, the application A100 identified by the process target application ID “A100” is identified by the user ID 10 identified by the terminal ID “F11” and the terminal ID “F13”. It indicates that it has been installed in the user terminal 10. On the other hand, the application A100 indicates that the application A100 is not installed in the user terminal 10 identified by the terminal ID "F12". Also, the feature information data of the user terminal 10 identified by the terminal ID "F11" is "G11", and the feature information data of the user terminal 10 identified by the terminal ID "F12" is "G12", the terminal ID The feature information data of the user terminal 10 identified by "F13" indicates that it is "G13".

(About model table 129)
Next, FIG. 13 shows an example of the model table 129 according to the embodiment. FIG. 13 is a diagram showing an example of the model table 129 according to the embodiment. As shown in FIG. 13, the model table 129 has items such as “model ID” and “corresponding application ID”.

  “Model ID” indicates identification information that identifies a model. "Corresponding application ID" indicates identification information for identifying an application corresponding to a model.

  That is, the example of the data illustrated in FIG. 13 indicates that the model M100 identified by the model ID “M100” is a model corresponding to the application ID “A100”.

(About the control unit 130)
In the control unit 130, for example, various programs (corresponding to an example of a learning program) stored in a storage device in the learning device 100 by a central processing unit (CPU), a micro processing unit (MPU), etc. It is realized by being executed as Further, the control unit 130 is realized by, for example, an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

  As shown in FIG. 5, the control unit 130 distributes the submission reception unit 131, the acquisition unit 132, the learning unit 133, the extraction unit 134, the generation unit 135, the reception unit 136, the identification unit 137, and And a unit 138 that implements or executes the functions and actions of the information processing described below. The internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 5, and may be another configuration as long as the configuration performs information processing described later. Moreover, the connection relation of each processing unit included in the control unit 130 is not limited to the connection relation illustrated in FIG. 5, and may be another connection relation.

(About the submission acceptance unit 131)
The submission acceptance unit 131 accepts submission of an advertisement from the advertiser terminal 20. Then, the submission reception unit 131 associates the advertisement information identifying the advertiser of the submission source, the advertisement ID identifying the advertisement, and the corresponding application ID identifying the application corresponding to the advertisement, thereby the advertisement information It is stored in the storage unit 121.

  The submission accepting unit 131 may accept from the advertiser the number of distributions designated for each advertisement, the condition regarding the user of the delivery destination, and the like. In addition, the submission acceptance unit 131 is a targeting element such as, for example, specification of an attribute of a user serving as a delivery destination of the advertisement, in the case where the advertisement is delivered without using a model corresponding to the advertisement (ie, application). May be accepted.

(About acquisition unit 132)
The acquisition unit 132 acquires various information. For example, the acquisition unit 132 acquires terminal information that is information related to the user terminal 10 that receives an advertisement. Specifically, the acquisition unit 132 acquires attribute information of the user who uses the user terminal 10 as the terminal information. In addition, the acquisition unit 132 acquires, as the terminal information, device information which is information of the device itself of the user terminal 10. In addition, the acquisition unit 132 acquires, as terminal information, application information that is information on an application installed in the user terminal 10.

  In addition, the acquisition unit 132 may acquire, as the terminal information, information related to the received advertisement and information related to the user's action on the advertisement. For example, when the user selects (for example, clicks) an advertisement, the acquisition unit 132 acquires information on the web page on which the advertisement was displayed, and the location of the advertisement space. Further, the acquisition unit 132 may acquire time information such as how long it has elapsed since the user selected an arbitrary advertisement at the end. In addition, the acquisition unit 132 may acquire information such as an advertisement contact time zone, such as which time zone of the day the user often selects an advertisement. The information acquired by the acquisition unit 132 as described above may be used as an explanatory variable of the user terminal 10 by the learning unit 133 described later.

  In addition, the acquisition unit 132 may acquire result information indicating whether the user terminal 10 that has received the advertisement has performed a predetermined action regarding the advertisement. Specifically, the acquisition unit 132 acquires result information indicating whether the user terminal 10 has installed an application to be subjected to learning processing by the learning unit 133 described later.

  The acquisition unit 132 may acquire terminal information and result information at any timing. For example, the acquisition unit 132 may acquire terminal information and the like when receiving a request for advertisement distribution from the user terminal 10. In addition, when the application associated with the advertisement is installed in the user terminal 10, the acquisition unit 132 is configured to send a predetermined communication from the user terminal 10 to the learning apparatus 100. When receiving such communication, terminal information etc. are acquired. Alternatively, the acquiring unit 132 may acquire terminal information and the like of the user terminal 10 at an arbitrary timing from an external server, instead of acquiring terminal information and the like from the user terminal 10.

  Then, the acquisition unit 132 stores the acquired information in a predetermined storage unit. For example, when acquiring the terminal information, the acquiring unit 132 stores the acquired information in the terminal information storage unit 122. Alternatively, the acquisition unit 132 may send the acquired information to a processing unit such as the learning unit 133.

  The acquiring unit 132 may be realized by various known methods for acquiring information related to the advertisement, such as whether the advertisement is clicked or not, and whether an application corresponding to the advertisement has been installed. For example, the acquisition unit 132 may acquire information on an advertisement using a notification function realized by a web beacon or the like.

(About the learning unit 133)
The learning unit 133 learns the relationship between the terminal information and the result information indicating whether the predetermined action related to the predetermined information content has been performed. Then, the learning unit 133 generates a model indicating a tendency to execute a predetermined action related to a predetermined information content based on the learning result. Further, the learning unit 133 acquires terminal information even after generating a model, and optimizes the model by continuing learning of the model. The learning unit 133 includes an extraction unit 134 and a generation unit 135, and implements the above-described processing.

(About the extraction unit 134)
The extraction unit 134 extracts feature information characterizing the user terminal 10 from the terminal information acquired by the acquisition unit 132. For example, the extraction unit 134 extracts information such as the brand name of the user terminal 10 and the number of days elapsed since the release, based on the device information.

  The extraction unit 134 also extracts feature information characterizing the user terminal 10 (that is, the user of the user terminal 10) based on the application information. For example, the extraction unit 134 specifies an application installed in the user terminal 10. Then, the extraction unit 134 identifies the type and element of the application set in the application. Further, the extraction unit 134 identifies the set variable based on the type and element of the application. That is, the extraction unit 134 specifies a variable used in a model to be described later as the feature information extracted from the terminal information.

  The extraction unit 134 extracts the feature information from the terminal information based on the setting input by the administrator of the learning device 100, for example. In the example illustrated in FIG. 11, the extraction unit 134 identifies that the application A 51 is associated with variables such as “interest_lifestyle”, “interest_SNS”, and “interest_encounter”. And the extraction part 134 extracts said variable as information which characterizes the user terminal 10, when application A51 is specified as an application installed in the user terminal 10 used as a process target.

  The extraction unit 134 may extract feature information using information other than the settings input by the administrator of the learning device 100. For example, in the download site of the application, text data that is an introduction sentence regarding the application may be associated with each application. In this case, the extraction unit 134 analyzes the text data to extract keywords serving as types and elements of the application. For example, it is assumed that the extraction unit 134 extracts nouns such as “communication” and “contact” by morphologically analyzing the text data associated with the application A51. In this case, the extraction unit 134 sets the extracted noun as a keyword as an element of the application A51. Further, the extraction unit 134 sets a variable linked to the word set as an element as a variable corresponding to the application A51. As a result, the extraction unit 134 can associate a variable (feature information) with the application without using an artificial process, such as the administrator of the learning device 100.

(About the generation unit 135)
The generation unit 135 learns the relationship between the result information indicating whether or not the predetermined action related to the predetermined information content has been executed, and the terminal information acquired by the acquisition unit 132, and thereby, the predetermined information content relating to the predetermined information content Generate a model that shows the tendency of the action of Specifically, the generation unit 135 generates a model indicating a tendency to install a predetermined application by learning the relationship between the terminal information and the result information indicating whether the predetermined application is installed. .

  As described above, the generation unit 135 generates a model for each application (in the case where the application and the advertisement correspond to each other one by one, it can be rephrased as “per advertisement”). That is, the generation unit 135 sets result information indicating whether a predetermined application has been installed as a target variable in machine learning. And generation part 135 makes feature information in user terminal 10 which received an advertisement matched with a predetermined application as an explanatory variable in machine learning. Then, the generation unit 135 generates a model related to a predetermined application using the target variable and the explanatory variable. Thereby, the generation unit 135 generates a model capable of accurately specifying the user terminal 10 which tends to install a predetermined application.

  The generation unit 135 generates a model using various explanatory variables. For example, the generation unit 135 uses the feature information of the user extracted from the application installed in the user terminal 10 as an explanatory variable. As an example, the generation unit 135 is information specified based on a type or an element set to an application installed in the user terminal 10 as the feature information extracted from the application information, and the interest of the user of the user terminal 10 The model is generated using the interest information that is information indicating. In addition, the generation unit 135 is information specified based on a type or an element set in the game application installed in the user terminal 10 as the feature information extracted from the application information, and the game of the user of the user terminal 10 The model may be generated using preference information which is information indicating preference. Note that, as described above, the type or element of an application may be set for each application by the extraction unit 134 analyzing an introduction sentence such as a download site. That is, the generation unit 135 can set the type or element of the application installed in the user terminal 10 based on the introduction of the application set for the application from the provider providing the application.

  Below, an example of a model which generation part 135 generates is explained. In addition, the learning method regarding the model which the production | generation part 135 produces | generates is not restricted to the following example, The method of various known machine learning may be employ | adopted.

  For example, the generation unit 135 generates an expression indicating a relationship between whether or not a predetermined application is installed in the user terminal 10, and the terminal information of the user terminal 10. Furthermore, the generation unit 135 calculates what weight each piece of feature information extracted from the terminal information of the user terminal 10 has an event that a predetermined application is installed. As a result, the generation unit 135 can obtain information such as how much each feature information contributes to an event of installation of a predetermined application. For example, the generation unit 135 generates the following equation (1) when generating a model indicating the tendency of the application A 100 shown in FIG. 12 to be installed.

y _{(A 100)} = ω ₁ · x ₁ + ω ₂ · x ₂ + ω ₃ · x ₃ ... + ω _N · x _N (1) (N is an arbitrary number)

The formula (1) is created, for example, for each user terminal 10 to which the advertisement associated with the application A 100 is distributed. In the above equation (1), “y _(A100) ” indicates an event “whether or not the application A 100 is installed in the user terminal 10 to which the advertisement has been distributed”.

Furthermore, in the above equation (1), “x” corresponds to each explanatory variable of the user terminal 10. Further, the explanatory variables “x ₁ , x ₂ , x ₃ ,..., X _N ” in the above equation (1) correspond to the variables shown in FIG. Specifically, if it contains "x _26" in the above formula (1), "x _26" corresponds to the variable of the variable ID "E26" shown in FIG. 10, the contents of "interest _ Net It is shopping. That is, the right side of the above equation (1) corresponds to the feature information data extracted from the terminal information of the user terminal 10 as shown in FIG.

Moreover, in said Formula (1), "(omega)" is a coefficient of "x" and shows a predetermined | prescribed weight value. Specifically, "ω ₁ " is a weight value of "x ₁ ", "ω ₂ " is a weight value of "x ₂ ", and "ω ₃ " is a weight value of "x ₃ " It is. Thus, the above equation (1) is a variable including an explanatory variable “x” corresponding to the feature information extracted from the terminal information and a predetermined weight value “ω” (for example, “ω ₁ · x _1” It is created by combining).

A specific example will be described with reference to FIGS. 7 and 11. In the following description, for the sake of convenience, the terminal ID is used as a reference code for the user. For example, the user of the user terminal 10 identified by the terminal ID "F11" is described as "user F11". Also, in the above equation (1), temporarily, "x ₁ " corresponds to "sex", "x ₂ " corresponds to "age", and "x ₃ " is an explanatory variable corresponding to "region" I assume. In this case, the above equation (1) corresponding to the user F11 can be expressed as the following equation (2).

y _{(A100, F11)} (= 1) = ω ₁ · (male) + ω ₂ · (30 years old) + ω ₃ · (prefecture A) + ω _N · x _N (2)

  Similarly, the user F12 and the user F13 can be expressed as the following formulas (3) and (4), respectively.

y _{(A100, F12)} (= 0) = ω ₁ · (male) + ω ₂ · (40's) + ω ₃ · (prefecture B) + ω _N · x _N (3)
y _{(A100, F13)} (= 1) = ω ₁ · (female) + ω ₂ · (20 years old) + ω ₃ · (C prefecture) + ω _N · x _N (4)

The generation unit 135 generates an equation for each user (for each user terminal 10) as in Equations (2), (3), and (4), and sets the generated equation as a sample of machine learning. Then, the generation unit 135 derives a value corresponding to a predetermined weight value “ω” by performing arithmetic processing of an expression serving as a sample. That is, the generation unit 135 determines a predetermined weight value “ω” which satisfies the above formulas (2), (3) and (4). In other words, the generation unit 135 can determine the weight value “ω” that indicates the influence of the predetermined explanatory variable on the target variable “y”. For example, as information indicating the characteristics of the user installing the application A100, if "gender" contributes significantly compared to other variables, the value of the weight value "ω ₁ " corresponding to "gender" is , A large value is calculated compared with other variables. In the above example, the explanatory variable is an example of user attribute information such as "sex", "age", "area", etc. However, in actuality, the user terminal is not Various explanatory variables such as 10 device information and application information are included. That is, the generation unit 135 can generate a model using terminal information or various pieces of feature information extracted from the terminal information as illustrated in FIGS. 7 to 12 as explanatory variables.

  As described above, the generation unit 135 generates a model that associates the event that the application A 100 is likely to be installed with the terminal information acquired from the user terminal 10. In the calculation process using the above equations (2), (3) and (4), the left side is not made “1” or “0” itself, but a predetermined error is assumed, and the difference from the error is assumed. The optimal solution of “ω” may be calculated using a method such as the least squares method of approximating so that the value obtained by squaring 2 becomes the minimum value.

  In addition, when substituting the feature information extracted from the terminal information of the user terminal 10 into the generated model, the generation unit 135 determines whether it is “present” or “absent” such as “interest_net shopping”. Substitute the numerical value of “1” or “0”. Further, the generation unit 135 may apply various known methods, such as normalizing an event indicated as an explanatory variable so as to be able to be treated by a model according to a known method regarding variables such as resolution. .

  In addition, after the generation unit 135 generates a model, the generation unit 135 may update the model as needed when the acquisition unit 132 acquires new terminal information. Thereby, the generation unit 135 can optimize the model indicating the feature of the user terminal 10 that tends to install the application.

(About the receiver 136)
The receiving unit 136 receives an advertisement distribution request. Specifically, the receiving unit 136 is a request transmitted from the user terminal 10 for displaying a web page, and receives a request for distribution of an advertisement displayed with an advertisement space included in the web page.

  The receiving unit 136 may also receive terminal information from the user terminal 10 while receiving a request for providing information transmitted from the user terminal 10. For example, the receiving unit 136 receives, as an example of the terminal information of the user terminal 10, identification information for identifying the user terminal 10. In this case, the reception unit 136 sends the received information to the acquisition unit 132 or the identification unit 137.

(About the identification unit 137)
The identifying unit 137 identifies the user terminal 10 serving as the provision destination to provide the predetermined information content based on the model generated by the generating unit 135. Specifically, the identifying unit 137 determines the advertisement associated with the application based on the tendency (that is, the output value) indicated by the model indicating the tendency of the application associated with the advertisement to be installed. The user terminal 10 serving as the distribution destination to be distributed is specified.

  For example, when the request for advertisement distribution is received by the receiving unit 136, the specifying unit 137 acquires terminal information of the user terminal 10 that has transmitted the request. Then, the identifying unit 137 inputs the acquired terminal information into the model generated by the generating unit 135. Then, the specifying unit 137 determines whether to distribute the advertisement corresponding to the model based on the result output from the model. For example, the specifying unit 137 calculates the probability of the user installing the application based on the result output from the model. Then, when the calculated probability indicates a predetermined value or more, the specifying unit 137 determines that the advertisement corresponding to the model is to be distributed, and specifies the user terminal 10 serving as the distribution destination.

  As another example, when the value output from the model exceeds the predetermined threshold, the identifying unit 137 determines that the application corresponding to the model is highly likely to be installed in the user terminal 10 of the distribution destination. Do. Then, the specifying unit 137 specifies a distribution destination to which the advertisement corresponding to the application is to be distributed. On the other hand, when the value output from the model does not exceed the predetermined threshold, the identifying unit 137 determines that the possibility that the application corresponding to the model is installed in the distribution-destination user terminal 10 is low. In this case, the specifying unit 137 may repeat, for example, the terminal information being input to another model until the value output from the model exceeds a predetermined threshold. Alternatively, the specifying unit 137 may determine to distribute the advertisement without using a model. The specifying unit 137 may also determine to distribute the advertisement without using the model even when there is no model corresponding to the advertisement to be distributed. In the case of the above example, the value output by the model is not limited to the probability of installing the application, and may be any index value.

  In addition, even after the generation of the model, the specifying unit 137 may specify the user terminal 10 for distributing the advertisement without using the model, in a predetermined case. For example, when the learning process is excessively performed, the specifying unit 137 may determine to always distribute the same advertisement to a certain user terminal 10. In such a case, the identifying unit 137 may perform identification processing without using a model in order to ensure a certain degree of randomness in advertisement distribution.

(About the delivery unit 138)
The distribution unit 138 distributes the advertisement to the user terminal 10 determined as the distribution destination of the advertisement based on the result determined by the identification unit 137.

  Note that, as described above, the data itself of the advertisement actually distributed to the user terminal 10 may not be stored in the advertisement information storage unit 121 related to the learning device 100. For example, the distribution unit 138 may distribute the advertisement to the user terminal 10 by transmitting a control command to a predetermined storage server provided outside.

[4. Configuration of terminal device]
Next, the configuration of the user terminal 10 according to the embodiment will be described with reference to FIG. FIG. 14 is a diagram showing an exemplary configuration of the user terminal 10 according to the embodiment. As shown in FIG. 14, the user terminal 10 includes a communication unit 11, an input unit 12, a display unit 13, a detection unit 14, a storage unit 15, and a control unit 16. In addition, the connection of each process part which the user terminal 10 has is not restricted to the connection shown in FIG. 14, and may be another connection.

  The communication unit 11 is connected to the network N in a wired or wireless manner, and transmits and receives information to and from the web server 30 and the learning device 100. For example, the communication unit 11 is realized by an NIC or the like.

  The input unit 12 is an input device that receives various operations from the user. For example, the input unit 12 is realized by an operation key or the like provided in the user terminal 10. The input unit 12 may also include an imaging device (such as a camera) for capturing an image, and a sound collection device (such as a microphone) that collects sound.

  The display unit 13 is a display device for displaying various information. For example, the display unit 13 is realized by a liquid crystal display or the like. When a touch panel is adopted for the user terminal 10, a part of the input unit 12 and the display unit 13 are integrated.

  The detection unit 14 detects various operations on the user terminal 10, environment information around the user terminal 10, and the like. For example, the detection unit 14 is realized by a sensor or an antenna that detects various information. Specifically, the detection unit 14 detects the communication status regarding the device connected to the user terminal 10, the illuminance and noise around the user terminal 10, the physical movement of the user terminal 10, the position information of the user terminal 10, etc. To detect

  The storage unit 15 stores various information. The storage unit 15 is realized by, for example, a semiconductor memory device such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. In the example illustrated in FIG. 14, the storage unit 15 includes an installation information storage unit 151. The installation information storage unit 151 stores, for example, information of the application installed in the user terminal 10.

  The control unit 16 is realized, for example, by a CPU, an MPU, or the like, which executes various programs stored in a storage device inside the user terminal 10 using a RAM as a work area. Further, the control unit 16 is realized by, for example, an integrated circuit such as an ASIC or an FPGA.

  The control unit 16 controls various processes performed in the user terminal 10. As illustrated in FIG. 14, the control unit 16 includes a reception unit 161, an acquisition unit 162, an execution unit 163, and a transmission unit 164, and implements or executes the function and action of the information processing described below. .

  The receiving unit 161 receives various information. For example, the receiving unit 161 receives information transmitted from the web server 30 or the learning device 100. Specifically, the receiving unit 161 receives the advertisement delivered in response to the request for advertisement delivery. The receiving unit 161 also receives various information detected by the detecting unit 14.

  The acquisition unit 162 acquires various information and data. For example, the acquisition unit 162 accesses the web server 30 to acquire a web page that the user desires to browse. In addition, the acquisition unit 162 acquires advertisement data and the like received by the reception unit 161. In addition, the acquisition unit 162 acquires data used for installing the application via the download site of the application and the like.

  The execution unit 163 executes various processes in the user terminal 10. For example, the execution unit 163 executes a process of installing an application. When the execution unit 163 installs an application, information on the installation is stored in the installation information storage unit 151.

  The transmission unit 164 transmits various information. For example, when the web page acquired by the acquisition unit 162 includes an advertisement space, the transmission unit 164 transmits a request for advertisement distribution to the learning device 100. Further, the transmission unit 164 transmits the terminal information of the user terminal 10 to the learning device 100 with reference to the storage unit 15 or the like.

[5. Processing procedure]
Next, the procedure of processing by the learning device 100 according to the embodiment will be described using FIGS. 15 and 16. First, a processing procedure relating to model generation will be described with reference to FIG. FIG. 15 is a flowchart (1) illustrating a processing procedure according to the embodiment.

  As illustrated in FIG. 15, the learning device 100 receives an advertisement submission from the advertiser terminal 20 (step S101). Then, the learning device 100 specifies an application corresponding to the submitted advertisement (step S102).

  Thereafter, the learning device 100 determines whether a request for advertisement distribution has been received from the user terminal 10 (step S103). When the learning apparatus 100 does not receive the request for the advertisement distribution, the learning apparatus 100 stands by until receiving the request (step S103; No).

  On the other hand, when a request for advertisement distribution is received (step S103; Yes), the learning device 100 distributes an advertisement to the user terminal 10 that has transmitted the request (step S104). After that, the learning apparatus 100 acquires, from the user terminal 10 that has distributed the advertisement, result information indicating whether or not an application corresponding to the advertisement has been installed, and terminal information (step S105).

  Then, the learning device 100 extracts feature information from the terminal information (step S106). Subsequently, the learning apparatus 100 determines whether a model corresponding to the application to be processed already exists (step S107). When a model exists (step S107; Yes), the learning device 100 performs model update processing based on the acquired terminal information (step S108).

  On the other hand, when a model does not exist (step S107; No), the learning device 100 newly generates a model based on the acquired result information and the terminal information (step S109). After that, the learning device 100 optimizes the model by repeating the terminal information acquisition process. When the learning apparatus 100 can acquire result information and terminal information from an external server or the like, the processing in steps S101 to S104 may be omitted.

  Next, a processing procedure relating to advertisement delivery will be described using FIG. FIG. 16 is a flowchart (2) illustrating a processing procedure according to the embodiment.

  As illustrated in FIG. 16, the learning device 100 determines whether a request for advertisement distribution has been received from the user terminal 10 (step S201). When the learning apparatus 100 does not receive the request for the advertisement delivery, the learning apparatus 100 stands by until receiving the request (step S201; No).

  On the other hand, when a request for advertisement distribution is received (step S201; Yes), the learning device 100 acquires terminal information from the user terminal 10 that has transmitted the request (step S202). Then, the learning apparatus 100 inputs the acquired terminal information as an explanatory variable to a model selected arbitrarily (step S203).

  Then, the learning device 100 determines whether the output value has exceeded a certain threshold (step S204). When the predetermined threshold is exceeded (step S204; Yes), the learning device 100 distributes the advertisement corresponding to the model to the user terminal 10 (step S205).

  On the other hand, when the output value does not exceed the certain threshold (step S204; No), the learning device 100 determines whether another model is present (step S206). When there is no other model (Step S206; No), the learning device 100 distributes the advertisement without using the model (Step S207).

  On the other hand, when another model exists (step S206; Yes), the learning device 100 selects another model (step S208). Then, the learning device 100 repeats the process of inputting the terminal information to the selected model (step S203).

[6. Modified example]
The learning device 100 described above may be implemented in various different forms other than the above embodiment. Therefore, another embodiment of the learning device 100 will be described below.

[6-1. Variable type]
In the embodiment described above, an example has been shown in which the acquisition unit 132 acquires, as terminal information, attribute information of the user of the user terminal 10, device information of the user terminal 10, and application information. In addition, the extraction unit 134 illustrates an example of extracting feature information based on the information acquired by the acquisition unit 132. The acquiring unit 132 and the extracting unit 134 are not limited to these examples, and may further acquire various terminal information or extract feature information.

  For example, the acquisition unit 132 may acquire the type and version information of the operating system (OS) of the user terminal 10, the resolution of the vertical screen and the horizontal screen, the total number of pixels, and the like. Further, the acquiring unit 132 may acquire, for example, the ratio of the game application among the applications installed in the user terminal 10.

  Also, the extraction unit 134 may extract other than the variables illustrated in FIG. 10 as the feature information extracted from the terminal information. For example, as the type of the feature information to be extracted, the extraction unit 134 may part-time job, job change, encounter or marriage, residence or car, household account book or coupon, point, various sports such as killing time, golf, etc. Various information may be extracted as feature information (explanatory variable), such as languages such as English, dishes, travel and fashion, brands, diets, female target products, student target products, flea markets, illustrations and photographs. The extraction unit 134 extracts, for example, variables corresponding to various interests based on elements set for each application.

  In addition, as the type of feature information extracted from the terminal information, the extraction unit 134 has genres other than the genres illustrated in FIG. 10 (for example, sports, casual, board, love, beautiful girl, training, A variety of information may be extracted as feature information (explanatory variable), such as war-torn countries, cards, strategies, scenarios, classifications such as gambling, characters, presence or absence of charges, and the like. That is, according to the extraction unit 134, information that the user of the user terminal 10 has installed a game application to which a variable such as "game preference_card" or "game preference_strategy" is given, for example It can be extracted. Then, the specifying unit 137 may output the information indicating whether the application associated with the model is likely to be installed by the user by inputting the extracted information as an explanatory variable to the model. it can.

6-2. Type of information content]
In the said embodiment, the advertisement with which the application was matched was mentioned as the example as information content provided to a user. However, the information content is not limited to such advertisements. For example, the information content may be an advertisement not associated with an application, and may be an advertisement that displays a landing page when selected. In this case, the generation unit 135 may generate a model in which the target variable is "the advertisement is clicked". As a result, the identifying unit 137 can identify a user who tends to click on the advertisement to be distributed, and can distribute the advertisement.

  Further, the information content is not limited to the advertisement but may be recommendation information of a product or the like. In this case, the generation unit 135 may generate a model in which the objective variable is “purchased recommended product”. As a result, the identifying unit 137 can identify users who tend to purchase the recommended item, and provide the recommendation information.

6-3. Relationship with media]
In the embodiment described above, an example has been shown in which the specifying unit 137 specifies the user terminal 10 that distributes the advertisement based on the model generated by the generating unit 135. Here, the specifying unit 137 may further perform the specifying process based on a predetermined condition received from the advertiser.

  For example, some advertisers may specify media content (e.g., a web page, an application, etc.) for displaying an advertisement submitted by the advertiser. Specifically, an advertiser may want to display his / her advertisement on content in which information of a particular category is posted, in order to increase the advertising effectiveness of the advertisement. Alternatively, the advertiser may want to prevent the web page provided by the competitor from displaying an advertisement that he / she has submitted.

  In this case, the specifying unit 137 may specify the user terminal 10 to which the advertisement is to be distributed, in consideration of the condition specified by the advertiser as well as the result output by the model. As a result, even if the advertiser is a web page displayed by a user who is likely to be installed, the advertiser may not display the advertisement on a web page which does not want his or her advertisement to be posted. it can.

[6-4. Use of external information]
When the learning apparatus 100 can generate a model using more detailed information on the user using the user terminal 10 as the terminal information, the learning apparatus 100 may use the information. For example, the learning device 100 acquires, for the user, a public survey result or the like announced by a public organization or the like. As an example, the learning device 100 acquires survey results such as the relationship between the average annual income of the general public including the user and the area. Then, when the area of the user can be identified, the learning device 100 may use the average annual income in the area as one of the feature information.

  In addition, as another example, the learning apparatus 100 acquires survey results such as the relationship between the average time spent for childcare and entertainment for the general public including the user and the age. Then, when the age of the user can be specified or estimated, the learning apparatus 100 may use, as one of the feature information, an average time spent for entertainment or childcare corresponding to the age. By such processing, the learning apparatus 100 can generate a model using information that is difficult to obtain between terminals. Thus, the learning device 100 can obtain feature information that more accurately captures the feature of the user.

[7. Hardware configuration]
The learning device 100 and the user terminal 10 according to the embodiment described above are realized by, for example, a computer 1000 configured as shown in FIG. Hereinafter, the learning device 100 will be described as an example. FIG. 17 is a hardware configuration diagram showing an example of a computer 1000 for realizing the functions of the learning device 100. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

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

  The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. The communication interface 1500 receives data from another device via the communication network 500 (corresponding to the network N shown in FIG. 4) and sends the data to the CPU 1100, and transmits the data generated by the CPU 1100 to the other via the communication network 500. Send to device.

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

  The media interface 1700 reads a program or data stored in the storage medium 1800 and provides the program to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a 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 digital versatile disc (DVD) or a phase change rewritable disc (PD), a magneto-optical recording medium such as a magneto-optical disk (MO), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the learning device 100 according to the embodiment, the CPU 1100 of the computer 1000 realizes the function of the control unit 130 by executing a program loaded on the RAM 1200. In addition, data in the storage unit 120 is stored in the HDD 1400. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them, but as another example, these programs may be acquired from the other device via the communication network 500.

[8. Other]
Further, among the processes described in the above embodiment, all or part of the process described as being automatically performed may be manually performed, or the process described as being manually performed. All or part of them can be performed automatically by known methods. In addition, information including processing procedures, specific names, various data and parameters shown in the above-mentioned documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

  Further, each component of each device illustrated is functionally conceptual, and does not necessarily have to be physically configured as illustrated. That is, the specific form of the distribution and integration of each device is not limited to that shown in the drawings, and all or a part thereof may be functionally or physically dispersed in any unit depending on various loads, usage conditions, etc. It can be integrated and configured. For example, the acquisition unit 132 and the reception unit 136 illustrated in FIG. 5 may be integrated. Further, the identifying unit 137 illustrated in FIG. 5 may calculate the probability that the user terminal 10 installs the application based on the model and whether to distribute the advertisement to the user terminal 10 based on the calculated result. And a specifying unit that specifies the user terminal 10 of the distribution destination based on the determined result. Also, for example, the information stored in the storage unit 120 may be stored in a predetermined storage device provided externally via the network N.

  Further, in the above embodiment, for example, the learning device 100 performs reception processing for receiving submission of an advertisement (information content), learning processing for learning a relationship between an application and a user, and distribution processing for distributing an advertisement. An example was shown. However, the learning device 100 described above may be separated into a reception device that performs reception processing, a learning device that performs learning processing, and a distribution device that performs distribution processing. In this case, the reception device has at least the submission reception unit 131. The learning device has at least a learning unit 133. In addition, the distribution apparatus has at least a distribution unit 138. And the process by said learning apparatus 100 is implement | achieved by the learning processing system 1 which has each apparatus of a reception apparatus, a learning apparatus, and a delivery apparatus.

  Moreover, it is possible to combine suitably the embodiment and modification which were mentioned above in the range which does not contradict process content.

[9. effect〕
As described above, the learning device 100 according to the embodiment includes the acquiring unit 132, the generating unit 135, and the specifying unit 137. The acquisition unit 132 acquires terminal information which is information related to the user terminal 10 that receives information content (for example, an advertisement). The generation unit 135 learns the relationship between the result information indicating whether or not the predetermined action related to the predetermined information content has been executed, and the terminal information acquired by the acquisition unit 132, and thereby, the predetermined information content relating to the predetermined information content Generate a model that shows the tendency of the action of The identifying unit 137 identifies the user terminal 10 serving as the provision destination to provide the predetermined information content based on the model generated by the generating unit 135.

  As described above, the learning device 100 according to the embodiment learns the relationship between the predetermined action on the information content to be provided and the terminal information of the provision destination to which the information content is provided, thereby to model the information content. Can be generated. The learning device 100 provides the information content and the provision destination by specifying the provision destination of the information content using the tendency indicated by the generated model (for example, the probability that the predetermined action regarding the predetermined information content is executed). The matching with the user of can be performed appropriately.

  In addition, the generation unit 135 learns the relationship between the result information and the feature information which is information extracted from the terminal information and is information indicating the feature of the user terminal 10 or the user who uses the user terminal 10. To generate a model.

  As described above, the learning device 100 according to the embodiment extracts the information indicating the feature of the user terminal 10 or the user of the user terminal 10 from the acquired terminal information. As a result, the learning apparatus 100 can acquire a large number of pieces of information indicating the features of the user terminal 10 and the user of the user terminal 10, so that the feature of the information content provision destination can be more accurately reflected on the model. it can. For this reason, the learning device 100 can improve the accuracy of the model to be generated.

  Also, the generation unit 135 generates a model using attribute information, which is information indicating an attribute of the user, as the feature information extracted from the terminal information. Specifically, the generation unit 135 generates a model using at least one of the gender, the age, and the place of residence of the user as the attribute information.

  Thus, the learning device 100 according to the embodiment generates a model using attribute information of the user. The attribute information of the user includes many pieces of information indicating the identity of the user, such as age and gender. That is, the learning device 100 can generate a model in which the user's characteristics are more reflected by generating a model using attribute information. Thereby, the learning device 100 can improve the accuracy of the model.

  Further, the acquisition unit 132 acquires, as the terminal information, device information which is information as a product of the user terminal 10. The generation unit 135 generates a model using feature information extracted from the device information. Specifically, the generation unit 135 sets at least one of a model number, a brand name, a time elapsed from sales, a communication carrier name, a maker name, and a resolution as feature information extracted from the device information. Use to generate a model.

  Thus, the learning device 100 according to the embodiment acquires information as a product of the user terminal 10 and uses it as information indicating a feature as a provision destination of the information content. Since the information as a product includes a brand, a specification, etc., it can be an element reflecting characteristics such as preference and character of the user using the user terminal 10. Then, since the learning apparatus 100 can input user characteristics in multiple ways by reflecting the device information on the model, it is possible to generate a model that can accurately identify the user who is the provision destination of the information content. it can.

  The acquiring unit 132 is also a user terminal 10 capable of receiving an advertisement related to an application, and acquires terminal information related to the user terminal 10 capable of installing the application. The generation unit 135 learns the relationship between the result information indicating whether the predetermined application is installed in the user terminal 10 and the terminal information acquired by the acquisition unit 132, whereby the predetermined application is installed. Create a model that shows The identifying unit 137 identifies, based on the model generated by the generating unit 135, the user terminal 10 that is the provision destination of the advertisement related to the predetermined application.

  Thereby, the learning device 100 according to the embodiment can generate a model that determines a user who easily installs the application associated with the advertisement. Then, the learning device 100 distributes the advertisement to users who tend to be similar to the user who has installed the application in the past by specifying the distribution destination of the advertisement based on the model. For this reason, the learning device 100 can appropriately match the distributed advertisement with the user of the distribution destination.

  In addition, the acquisition unit 132 acquires, as terminal information, application information that is information on an application installed in the user terminal 10. The generation unit 135 generates a model indicating a tendency to install a predetermined application by learning the relationship between the result information and the feature information extracted from the application information.

  Thus, the learning device 100 according to the embodiment accurately estimates, for example, a user who tends to install a similar application by acquiring information on the application already installed in the user terminal 10. can do. Thereby, the learning device 100 can improve the accuracy of the model to be generated.

  In addition, the generation unit 135 sets the total number of applications installed in the user terminal 10, the number of game applications installed in the user terminal 10, and the game installed in the user terminal 10 as the feature information extracted from the application information. The model is generated using at least one of the number of applications or the ratio of the total number of applications installed in the user terminal 10 to the number of game applications.

  As described above, the learning apparatus 100 according to the embodiment uses information on the number of applications installed in the user terminal 10 as the feature information. As for these information, for example, a user having a large number of applications installed in the user terminal 10 is more likely to install a new application, or a user having a large number of game applications may have another game application installed. It can be information indicating user behavior or characteristics, such as high gender. The learning device 100 can improve the accuracy of the model by learning such information and generating the model.

  In addition, the generation unit 135 is information specified based on a type or an element set in the application installed in the user terminal 10 as the feature information extracted from the application information, and the interest of the user of the user terminal 10 A model is generated using interest information that is information to be shown. In addition, the generation unit 135 is information specified based on a type or an element set in the game application installed in the user terminal 10 as the feature information extracted from the application information, and the game of the user of the user terminal 10 The model may be generated using preference information which is information indicating preference.

  As described above, the learning apparatus 100 according to the embodiment extracts not only the number of installed applications but also the type of the application and the elements of the application, and extracts the feature information that characterizes the user. Therefore, the learning device 100 can perform detailed characterization with respect to the user, and can improve the accuracy of the model by reflecting the information in the model.

  In addition, the generation unit 135 sets the type or element of the application installed in the user terminal 10 based on the introduction of the application set for the application from the provider providing the application.

  As described above, the learning device 100 according to the embodiment can set the type and the element for the application by, for example, analyzing an introduction sentence of the application registered in the download site of the application or the like. Thus, the learning device 100 can automatically set the type and element for the application without artificially characterizing the type and element in the application. Therefore, the learning device 100 can reduce the time and effort of the administrator of the learning device 100, for example.

  Although the embodiments of the present application have been described in detail based on the drawings, this is an example, and various modifications and improvements can be made based on the knowledge of those skilled in the art, including the embodiments described in the section of the description of the invention. It is possible to practice the invention in other forms as well.

  In addition, the "section (module, unit)" described above can be read as "means" or "circuit". For example, the acquisition unit can be read as an acquisition unit or an acquisition circuit.

Reference Signs List 1 learning processing system 10 user terminal 20 advertiser terminal 30 web server 100 learning device 110 communication unit 120 storage unit 121 advertisement information storage unit 122 terminal information storage unit 123 attribute table 124 device table 125 application table 126 learning model storage unit 127 setting table 128 variable table 129 model table 130 control unit 131 submission acceptance unit 132 acquisition unit 133 learning unit 134 extraction unit 135 generation unit 136 reception unit 137 identification unit 138 distribution unit

Claims (13)

An acquisition unit that acquires terminal information that is information related to a terminal device that receives the information content;
By learning the relationship between the result information indicating whether a predetermined action related to a predetermined information content has been performed and the terminal information acquired by the acquisition unit, the predetermined action related to the predetermined information content is executed A generation unit that generates a model indicating a tendency to be
An identification unit that identifies a terminal device to which the predetermined information content is provided based on the model generated by the generation unit;
Equipped with
The acquisition unit
The terminal information is application information which is information on an application installed in the terminal device, and the application information including an introduction of an application set for the application is acquired from the provider who provides the application. ,
The generation unit is
Generating the model based on information specified based on the type or element of the application extracted from the introduction of the application;
A learning device characterized by The generation unit is
The model is obtained by learning the relationship between the result information and the feature information that is information extracted from the terminal information and that is information indicating the feature of the terminal device or the user who uses the terminal device. Generate,
The learning device according to claim 1, characterized in that: The generation unit is
The model is generated using attribute information that is information indicating the attribute of the user as the feature information extracted from the terminal information.
The learning device according to claim 2, characterized in that: The generation unit is
The model is generated using at least one of gender, age, and residence of the user as the attribute information.
The learning device according to claim 3, characterized in that: The acquisition unit
As the terminal information, device information which is information as a product of the terminal device is acquired,
The generation unit is
Generating the model using feature information extracted from the device information;
The learning device according to any one of claims 2 to 4, characterized in that. The generation unit is
The model is generated using at least one of a model number, a brand name, a time elapsed from sale, a communication carrier name, a maker name, and a resolution as the feature information extracted from the device information. ,
The learning device according to claim 5, characterized in that: The acquisition unit
A terminal device capable of receiving an advertisement related to an application, and acquiring terminal information related to a terminal device capable of installing the application,
The generation unit is
A model indicating a tendency of the predetermined application being installed by learning a relationship between result information indicating whether the predetermined application is installed in the terminal device and the terminal information acquired by the acquisition unit Generate
The identification unit is
Identifying a terminal device to which an advertisement related to the predetermined application is to be provided, based on the model generated by the generation unit;
The learning device according to any one of claims 1 to 6, characterized in that: The generation unit is
By learning the relationship between the result information and the feature information extracted from the application information, a model indicating a tendency of the predetermined application to be installed is generated.
The learning device according to claim 7, characterized in that: The generation unit is
As the feature information extracted from the application information, the total number of applications installed in the terminal device, the number of game applications installed in the terminal device, the number of applications other than games installed in the terminal device, or Generating the model using at least one of a ratio of the total number of applications installed in the terminal device to the number of game applications;
The learning device according to claim 8, characterized in that. The generation unit is
Interest information which is information specified based on a type or an element set in an application installed in the terminal device as the feature information extracted from the application information, and is information indicating an interest of the user of the terminal device Generate the model using
The learning device according to claim 8 or 9, characterized in that: The generation unit is
The feature information extracted from the application information is information specified based on a type or an element set in the game application installed in the terminal device, and is information indicating the game preference of the user of the terminal device Generating the model using preference information;
The learning device according to any one of claims 8 to 10, characterized in that: A computer implemented learning method,
An acquisition step of acquiring terminal information which is information related to a terminal device that receives the information content;
By learning the relationship between result information indicating whether or not a predetermined action related to a predetermined information content has been performed, and the terminal information acquired by the acquisition step, the predetermined action related to the predetermined information content is executed Generating a model indicating a tendency to be
A specifying step of specifying a terminal device to which the predetermined information content is to be provided based on the model generated in the generation step;
Including
The acquisition step is
The terminal information is application information which is information on an application installed in the terminal device, and the application information including an introduction of an application set for the application is acquired from the provider who provides the application. ,
The generation step is
Generating the model based on information specified based on the type or element of the application extracted from the introduction of the application;
Learning method characterized by An acquisition procedure for acquiring terminal information which is information on a terminal apparatus that receives information content;
The predetermined action relating to the predetermined information content is executed by learning the relationship between the result information indicating whether the predetermined action relating to the predetermined information content has been executed and the terminal information acquired by the acquisition procedure. Generating a model that shows the tendency to be
A specification procedure for specifying a terminal device to which the predetermined information content is to be provided based on a model generated by the generation procedure;
On your computer,
The acquisition procedure is
The terminal information is application information which is information on an application installed in the terminal device, and the application information including an introduction of an application set for the application is acquired from the provider who provides the application. ,
The generation procedure is
Generating the model based on information specified based on the type or element of the application extracted from the introduction of the application;
A learning program characterized by

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