JP2016177649A - Information processing device, information processing program, and information processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an information processing device, information processing program, information processing method that can effectively increase content distribution target users.SOLUTION: An information processing device 3 includes a model generation unit 21 and an update unit 24. The model generation unit generates a prediction model by using information on a first user having histories of purchasing goods and/or services which are sold in predetermined e-commerce as correct answer data. The update unit updates the attribute of a second user different from the first user, on the basis of the prediction model.SELECTED DRAWING: Figure 3

Description

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

  In recent years, with the rapid spread of the Internet, advertisement distribution based on information acquired via a network has been performed. As an example of such advertisement distribution, a technique is known in which a similar audience is specified for an audience group having a certain attribute among users browsing a web page, and advertisement distribution is performed (for example, patent literature). 1).

JP 2012-94097 A

  However, the above-mentioned patent document 1 only describes that an audience whose web browsing history is similar to an audience group that is a target of advertisement distribution is described, and does not present a specific method.

  The present application has been made in view of the above, and an object thereof is to provide an information processing apparatus, an information processing program, and an information processing method capable of effectively extending a distribution target of content such as advertisement content. .

  An information processing apparatus according to the present application is based on a model generation unit that generates a prediction model using information of a first user who has a purchase history of products and / or services sold in a predetermined e-commerce as correct data, and the prediction model And an update unit for updating attributes of a second user different from the first user.

  According to one aspect of the embodiment, there is an effect that it is possible to effectively expand the distribution target users of content such as advertisement content.

FIG. 1 is an explanatory diagram of user attribute update processing according to the embodiment. FIG. 2 is a diagram illustrating an example of the configuration of the advertisement distribution system according to the present embodiment. FIG. 3 is a diagram illustrating an example of the configuration of the information processing apparatus 3 according to the embodiment. FIG. 4 is a diagram illustrating an example of a user information storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of coefficients and features of the prediction model according to the embodiment. FIG. 6 is a flowchart illustrating an example of a user attribute update processing procedure according to the embodiment. FIG. 7 is a hardware configuration diagram illustrating an example of a computer that realizes the information processing function according to the embodiment.

  Hereinafter, a mode for carrying out an information processing apparatus, an information processing program, and an information processing method according to the present application (hereinafter referred to as “embodiment”) will be described in detail with reference to the drawings. Note that the information processing apparatus, the information processing program, and the information processing method according to the present application are not limited by this embodiment.

[1. User attribute update processing]
First, user attribute update processing according to the embodiment will be described. FIG. 1 is an explanatory diagram of user attribute update processing according to the embodiment. In the following, user attribute update processing is performed by the information processing apparatus.

In the example illustrated in FIG. 1, the information processing apparatus generates a prediction model represented by the following formula (1) as a prediction model used for updating the user attribute. a _i is a coefficient, and x _i is a feature (explanatory variable). The information processing apparatus generates a prediction model by calculating a coefficient a _i by machine learning such as SVM (Support Vector Machine).

Specifically, the information processing apparatus calculates the coefficient a _i of the above formula (1) using SVM using the information of the plurality of users U1 as correct answer data and the information of the plurality of users U2 as incorrect answer data. A model is generated (step S1). Here, the user U1 is, for example, a user who has a purchase history of a product sold by predetermined e-commerce, and the user U2 is a user who has no purchase history of a product sold by predetermined e-commerce, for example.

The information of the users U1 and U2 includes demographic attributes indicating demographic user attribute information such as “gender” and “age”, user preferences such as “car” and “fashion”, and values. , User attribute information such as psychographic attributes indicating lifestyle, personality, and the like. The information processing apparatus generates a prediction model by calculating the coefficient a _i of the above equation (1) using the information of the users U1 and U2 as the feature x _i and the presence or absence of purchase history as a dependent variable.

The information processing apparatus acquires one or more feature information based on the generated prediction model (step S2). Here, the feature information is information included in many users U1 among the information of a plurality of users U1 as correct data, and is information indicating the characteristics of the plurality of users U1. Specifically, since the information included in the information about many users U1 corresponding to the coefficient a _i is greater feature x _i of the prediction model calculated in step S1, obtaining a coefficient a _i is greater feature x _i as feature information To do.

  Further, the information processing apparatus performs a calculation based on the prediction model based on information of the user U3 different from the user U1 (step S3). Here, the user U3 is, for example, a user who has no purchase history of products sold by predetermined e-commerce. Note that the user U3 may be the same as or different from the user U2. When the result of the calculation satisfies a predetermined condition, the information processing apparatus updates the attribute of the user U3 (step S4). Specifically, the feature information acquired in step 2 is used as attribute information of the user U3.

  As described above, in the user attribute update process according to the present embodiment, the information processing apparatus generates and generates a prediction model using the information of the user U1 who has a purchase history of a product sold by a predetermined ecommerce as correct data. Based on the prediction model, the attribute of the user U3 different from the user U1 is updated. In addition, the information processing apparatus extracts feature information based on the prediction model, and updates the attribute of the user U3 based on the extracted feature information.

  Therefore, for example, when advertising content is distributed to the user U1 who has a purchase history of a predetermined product, the advertising content can be distributed to a user U3 that is different from the user U1 but is similar to the user U1. In this way, for example, the user who distributes the advertising content can be effectively extended not only to the user U1 that was originally targeted for distribution but also to the user U3.

  Hereinafter, an embodiment of an advertisement distribution system that includes the above-described information processing apparatus and distributes advertisement content as an example of content will be described.

[2. Ad delivery system]
FIG. 2 is a diagram illustrating a configuration example of the advertisement distribution system 1 according to the present embodiment. As shown in FIG. 2, the advertisement distribution system 1 according to the present embodiment includes a web server 2, an information processing device 3, and a plurality of terminal devices 5. These devices are communicably connected to each other via a communication network 6. The communication network 6 is, for example, the Internet.

  The terminal device 5 is, for example, a PC (Personal Computer), a PDA (Personal Digital Assistant), a tablet terminal, a smartphone, or the like used by the user U. For example, a browser application (hereinafter referred to as a browser) is installed in the terminal device 5.

  The web server 2 stores a plurality of web pages in which advertisement spaces are set. When there is an access from the browser of the terminal device 5 via the communication network 6, the control unit of the web server 2 provides a web page corresponding to a URL (Uniform Resource Locator) specified by the terminal device 5.

  When the browser of the terminal device 5 receives the web page from the web server 2, the browser transmits an advertisement request corresponding to the advertisement space set in the web page to the information processing device 3. The advertisement request is a request for distribution of the advertisement content to be displayed in the advertisement space. For example, the user U identification information (hereinafter referred to as user ID) of the terminal device 5 and the advertisement space identification information (hereinafter referred to as advertisement space ID and Included). The user ID is, for example, an HTTP (Hypertext Transfer Protocol) cookie.

  The information processing device 3 distributes advertisement content corresponding to the advertisement request to the terminal device 5. The information processing device 3 distributes the advertising content based on the attribute of the user U. The advertisement content is, for example, a banner advertisement, and when clicked by the user U, the advertisement content is changed to the advertiser's web page.

  Hereinafter, a configuration example of the information processing apparatus 3 will be described in more detail.

[2.1. Information processing apparatus 3]
FIG. 3 is a diagram illustrating a configuration example of the information processing apparatus 3. As illustrated in FIG. 3, the information processing device 3 includes a communication unit 10, a control unit 20, and a storage unit 30.

  The communication unit 10 is a communication interface that transmits and receives information to and from the communication network 6, and connects to the communication network 6 by wire or wirelessly. The control unit 20 can transmit and receive various types of information to and from the terminal device 5 and other devices via the communication unit 10 and the communication network 6.

  The storage unit 30 includes a user information storage unit 31 and an advertisement information storage unit 32. The user information storage unit 31 and the advertisement information storage unit 32 are, for example, a semiconductor memory device such as a RAM (Random Access Memory) and a flash memory, or a storage device such as a hard disk and an optical disk.

[2.1.1. User information storage unit 31]
Here, FIG. 4 shows an example of the user information storage unit 31 according to the present embodiment. FIG. 4 is a diagram illustrating an example of the user information storage unit 31 according to the embodiment. The user information storage unit 31 stores a purchase history of merchandise in the predetermined e-commerce of the user U and attribute information of the user U.

  Here, the predetermined e-commerce is an electronic commerce performed via the communication network 6 such as an auction or shopping. In the following description, it is assumed that the purchase history of a product in a predetermined e-commerce is the successful bid history of an auction.

  In FIG. 4, the user information storage unit 31 includes items such as “user ID”, “demographic attribute”, “psychographic attribute”, and “successful bid history”.

  “User ID” is identification information for identifying the user U of the terminal device 5. In FIG. 4, the user ID is expressed as “U11” or the like. This indicates that the terminal device 5 is identified by the user ID “U11”. Here, it is assumed that the user ID matches the reference code of the user who operates the terminal device 5. Next, “demographic attributes” shown in FIG. 4 are classified into types such as “sex” and “age”. As described above, the “demographic attribute” indicates demographic user U attribute information.

  “Psychographic attributes” indicate user preferences, values, lifestyle, personality, and the like. In the example illustrated in FIG. 4, the user information storage unit 31 stores a feature word related to an object of interest of the user U as “psychographic attribute”. Specifically, the user information storage unit 31 uses, as a feature word, a keyword indicating the interest of the user U such as “car”, “fashion”, “travel”, “sports”, etc. as “psychographic attributes”. Remember.

  The feature word is a word that characteristically represents the interest of the user U, and is a word that represents the feature of the user U. Thus, the feature word is a kind of feature information that is information indicating the feature of the user U. The feature information of the user U includes, for example, the degree of interest of the user U with respect to a predetermined keyword in addition to the feature word. . The feature word may be, for example, a keyword extracted from a web page browsed by the user U, or may be a keyword extracted from a search query input to the search web page by the user U.

  The “successful bid history” stores information on products for which the user U has made a successful bid at the auction site. In the example illustrated in FIG. 4, the user information storage unit 31 stores the name of the product for which a successful bid is made and the category of the product in association with each other as “successful bid history”. For example, FIG. 4 shows an example in which the user U11 has purchased a product “truck” whose major category is “model” and whose minor category is “car”. In addition, the users U31 and U32 having a blank successful bid history indicate that the product has not been successfully bid on the auction site.

[2.1.2. Control unit 20]
The control unit 20 illustrated in FIG. 3 is realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The control unit 20 is configured to execute a program stored in an internal storage device by an internal CPU (Central Processing Unit) or MPU (Micro Processing Unit) using the RAM as a work area, thereby generating a model generation unit 21 and an acquisition unit 22. , Function as an operation unit 23, an update unit 24 and an advertisement distribution unit 25 In addition, the structure of the control part 20 is not restricted to this structure, Other structures may be sufficient if it is the structure which performs the information processing mentioned later.

[2.1.3. Model generation unit 21]
The model generation unit 21 acquires, from the user information storage unit 31, information on the user U who has a successful bid history of products at the auction site among the users U. In addition, the model generation unit 21 acquires, from the user information storage unit 31, information on the user U who has no successful bid history of the product among the users U. The model generation unit 21 generates a prediction model using the information of the user U who has a successful bid history of the product as correct data, the information of the user U without the successful bid history of the product as incorrect data, and stores the generated prediction model as a storage unit 30.

Below, the generation process of the prediction model which concerns on embodiment is demonstrated concretely using FIG. 4, FIG. FIG. 5 is a diagram illustrating a relationship between the coefficient a _{i of the} prediction model generated by the model generation unit 21 and the feature x _i . Here, the model generation unit 21 corrects the user information of the users U11, U12,... (Hereinafter also referred to as the user U1) who have a successful bid history of a product whose major category is “model” and whose minor category is “car”. User information of users U21, U22,... (Hereinafter also referred to as user U2) that have data, and have a successful bid history for a product with a large category “model” but have no successful bid history for a product with a small category “car”. An example in which a prediction model is generated using the incorrect answer data as will be described.

  Here, the user U1 who has a successful bid history of a product whose major category is “model” and whose small category is “car” is correct data, and there is a successful bid history of a product whose major category is “model”, but the small category is “car”. The reason why the user U2 who does not have a successful bid history of the product is used as incorrect answer data will be described. Since the user U1 has a successful bid history of products whose major category is “model” and small category is “car”, it is considered that the degree of interest in “car” is relatively high among “model”.

  On the other hand, since the user U2 has made a successful bid for a product whose major category is included in the “model”, it is considered that the “model” is interested in the product, and there is no successful bid history of the product whose minor category is “car”. Therefore, it is considered that the user U2 has a relatively low degree of interest in the “car” among the “models”.

  Thus, the user U1 and the user U2 have different degrees of interest in the “model” “car”. Therefore, the model generation unit 21 according to the embodiment predicts the user U who is interested in the “model” “car” by generating a prediction model using the user U1 as correct data and the user U2 as incorrect data. A prediction model can be generated.

  Here, an example in which the model generation unit 21 generates a prediction model by paying attention to a small category “car” will be described, but the present invention is not limited thereto. For example, when generating a prediction model for predicting a user U who is interested in a specific product, the model generation unit 21 sets the user U having a successful bid history of the specific product as correct data, and there is no successful bid history of the product. It is also possible to generate a prediction model using the user U as incorrect answer data. In addition, a prediction model can be generated in the same manner according to the presence / absence of a successful bid history of products included in a large category.

For example, in the SVM represented by the above-described formula (1), the model generation unit 21 uses, for example, whether or not a product included in the small category “car” is purchased as a dependent variable, and uses the above-described user information as a feature (explanatory variable) x. _By setting _i , a coefficient a _i corresponding to the feature x _i is obtained.

The feature x _i is an attribute of the user U, for example. For example, the feature x _i is a feature word stored in the user information storage unit 31 as “psychographic attribute”. Specifically, for example, the feature x ₁ is “car”, and is set to “1” when “car” is stored in the user information storage unit 31 as the “psychographic attribute” of the user U. Otherwise, it is set to “0”. The feature x ₂ is “child”, and is set to “1” when “child” is stored in the user information storage unit 31 as the “psychographic attribute” of the user U, and “0” is set otherwise. "Is set. As described above, the attribute information stored as the “psychographic attribute” in the user information storage unit 31 is assigned to the feature x _i .

  The dependent variable is set to “1” when the user U makes a successful bid for a product included in the large category “model” and the small category “car” in the successful bid history, for example. ”And set to“ −1 ”when a successful bid is made for a product not included in the small category“ car ”. Specifically, the users U11, U12,... In FIG. 4 are set to “1”, and U21, U22,.

Thus, the model generation unit 21 generates a prediction model based on SVM. The model generation unit 21 associates the feature x _{i of the} prediction model generated as shown in FIG. 5 and the coefficient a _i and stores them in the storage unit 30. The dependent variables and features (explanatory variables) described above are examples, and the model generation unit 21 can also generate a prediction model using other information or a part of the information described above. For example, “−1” may be set as the dependent variable when the product is sold and “1” when the product is not sold.

The model generation unit 21 can also generate a prediction model using a machine learning method such as a logistic regression model. For example, when generating a prediction model using a logistic regression model, the model generation unit 21 uses the presence or absence of purchase of a product as a dependent variable in the following equation (2), and uses the above-described user information as a feature (explanatory variable) x _i . Thus, the coefficient a _i corresponding to the feature x _i may be obtained.

[2.1.4. Acquisition unit 22]
The acquisition unit 22 in FIG. 3 is an extraction acquisition unit that extracts and acquires one or more feature information based on the prediction model generated by the model generation unit 21. Specifically, the acquisition unit 22 extracts N features x _i corresponding to N coefficients in descending order of the coefficients a _i of the equation (1) calculated by the model generation unit 21.

For example, when the coefficient a _i illustrated in FIG. 5 is calculated by the model generation unit 21, the acquisition unit 22 extracts and acquires three pieces of feature information. In this case, the acquisition unit 22 acquires three feature words “toy”, “car”, and “child” as feature information. The acquisition unit 22 stores the extracted and acquired feature information in the storage unit 30.

  In addition, although the case where the acquisition unit 22 extracts feature information based on the prediction model has been described here, the present invention is not limited thereto. For example, the storage unit 30 may store the product or category and the feature information in association with each other in advance, and the acquisition unit 22 may acquire the feature information from the storage unit 30.

  Moreover, the example which uses the same prediction model for extraction acquisition of the feature information by the acquisition part 22, and the update of the user attribute by the update part 24 was shown. Here, the prediction model is an example, and the acquisition unit 22 can also extract and acquire feature information based on a prediction model different from the prediction model used for updating the user attribute by the update unit 24.

  Further, the number of feature information extracted by the acquisition unit 22 may be changed depending on the category, the product, and the like. Or you may make it the acquisition part 22 change the number of the feature information extracted according to the calculation result by a prediction model.

[2.1.5. Calculation unit 23]
3 is generated by the model generation unit 21 based on information of the user U different from the user U1, that is, the user U who has no purchase history of products included in the large category “model” and the small category “car”. Perform calculations based on the prediction model. For example, in the example shown in FIG. 4, the items of products included in the users U31, U32,... (Hereinafter also referred to as the user U3) and the large category “model” and the small category “car” that have no purchase history at the auction site. Based on the information of the user U2 who does not have a purchase history, calculation based on the prediction model is performed.

  As described above, the calculation unit 23 performs calculation based on the prediction model based on the information of the user U including the user U2 as incorrect answer data. Accordingly, it is possible to predict whether or not the user U2 who is not interested in the “model” “car” includes the user U2 who may be interested in the “model” “car”. it can.

For example, when performing a calculation based on the user information of the user U31 illustrated in FIG. 4, the calculation unit 23 acquires the user information of the user U31 from the user information storage unit 31. The calculation unit 23 performs calculation based on the prediction model of Expression (1) using the “psychographic attribute” acquired from the user information storage unit 31 as the feature x _i .

Specifically, for example, as shown in FIG. 4, the user U31 has a psychographic attribute of “child”. As shown in FIG. 5, the "child" is a feature x _2. Therefore, calculation unit 23 sets the feature _{x 2} to "1". As described above, the attribute information stored as the “psychographic attribute” in the user information storage unit 31 is assigned to the feature x _i . The calculation unit 23 performs calculation based on the prediction model by multiplying the assigned feature x _i by a coefficient a _i to obtain a sum. The calculation unit 23 stores the user U and the calculation result in the storage unit 30 in association with each other.

[2.1.6. Update unit 24]
The update unit 24 updates the attributes of the users U2 and U3 that have been calculated by the calculation unit 23 using the prediction model. The update unit 24 updates the attribute information of the users U2 and U3 when the calculation result by the calculation unit 23 satisfies a predetermined condition.

  Here, the update unit 24 updates the attribute of the user U when the number of users U1 set as correct data satisfies a predetermined condition. For example, when the number of users U1 is equal to or less than a predetermined threshold, the update unit 24 includes the user U2 who has purchased a product of the small category “railway” included in the large category “model” including the product purchased by the user U1. Decide to update the attribute.

  Further, when the number of users U2 is equal to or less than a predetermined threshold, the updating unit 24 may determine to update the attribute of the user U3 who has no purchase history at the auction site, for example. As described above, the updating unit 24 may add a user whose attribute is to be updated, for example, depending on whether or not a product included in the category has been purchased until the number of users U whose attributes are to be updated exceeds a predetermined threshold. it can.

  The update unit 24 notifies the calculation unit 23 of the user U determined to update the attribute. Receiving the notification, the calculation unit 23 performs calculation based on the prediction model based on the information of the user U determined to update the attribute. The update unit 24 updates the attribute of the user U according to the calculation result of the calculation unit 23.

  Specifically, the update unit 24 adds the feature information acquired by the acquisition unit 22 to the user information when the calculation result by the calculation unit 23 is equal to or greater than a predetermined threshold. The update unit 24 newly stores the feature word acquired by the acquisition unit 22 in the user information storage unit 31 in association with the users U2 and U3. Specifically, a feature word extracted as feature information is added to the psychographic attribute shown in FIG. 4 as a new item. For example, it is assumed that the user attribute of the user U31 is updated by adding feature words “toy”, “car”, and “child” as feature information to the psychographic attribute of the user U31 in FIG. In this case, since the user U31 in FIG. 4 already has the characteristic word “child”, the update unit 24 adds “toy” and “car” as psychographic attributes to the user information of the user U31.

  Here, an example in which the update unit 24 updates the attribute information of the users U2 and U3 has been described, but the present invention is not limited to this. For example, the update unit 24 may update the successful bid history. Specifically, the update unit 24 adds information indicating that a product in a predetermined category has been successfully bid to the successful bid history of the user U whose calculation result is equal to or greater than a predetermined threshold. In this case, for example, the update unit 24 adds “model” to the large category of the successful bid history of the user U in the user information storage unit 31 and “car” to the small category. At this time, the product name may be left blank, or the temporary product name may be added on the assumption that the temporary product has been sold.

  Further, when the user information storage unit 31 digitizes and stores the successful bid history of the user U (for example, stores “1” when a successful bid is made for a product and stores “0” when no successful bid is made), the updating unit 24. May update the successful bid history of the user U whose calculation result is equal to or greater than a predetermined threshold from “0” to “1”. Such numerical values are not limited to “0” and “1”. For example, the user U who actually made a successful bid for the product may be “2”, and the user U whose calculation result is equal to or greater than a predetermined threshold may be “1”. Further, the user U whose calculation result is equal to or greater than a predetermined threshold value may store the calculation result as a successful bid history of the user U.

  Further, an example has been described in which the update unit 24 updates the user attribute by adding the feature information acquired by the acquisition unit 22 to the “psychographic attribute” of the user information. Here, for example, the update unit 24 may store the feature information and attribute information corresponding to at least one of the auction, the category, or the product in the user information storage unit 31 in association with each other. Alternatively, the information processing apparatus 3 may store the feature information extracted and acquired by the prediction model separately from the “psychographic attribute” directly acquired from the user U. In this case, for example, when the psychographic attribute is overwritten or deleted, it may be preferentially overwritten or deleted from the feature information extracted and acquired by the prediction model.

  Note that the update unit 24 can also periodically update the demographic attributes, psychographic attributes, and the like described above based on, for example, the user U's web browsing history, web search history, input information by the user U, and the like. The updating unit 24 can also periodically acquire and update the above-described demographic attributes, psychographic attributes, and the like from an external server, for example.

  Here, although the update unit 24 determines the user U whose attribute is to be updated, the present invention is not limited to this. For example, the calculation unit 23 may determine the user U whose attribute is updated. Or you may make it the control part 20 have a determination part (not shown) which determines the user U who updates an attribute.

[2.1.7. Advertising distribution unit 25]
When the advertisement distribution unit 25 in FIG. 3 receives an advertisement distribution request from the terminal device 5 of the user U, the advertisement distribution unit 25 extracts advertisement content suitable for the user U from the advertisement information storage unit 32. The advertisement distribution unit 25 distributes the advertisement content extracted via the communication unit 10 to the terminal device 5. The advertisement distribution unit 25 uses the user information regarding the user U stored in the user information storage unit 31 and executes an appropriate matching process between the user U and the advertisement content. In other words, the advertisement distribution unit 25 extracts appropriate advertisement content corresponding to a specific user U that is assumed to have a high appeal effect, and distributes the extracted advertisement content to the user U. At this time, as the user information, the content distribution target user can be effectively expanded by updating by the updating unit 24, and the appeal effect of the content to the user U can be enhanced.

[3. Processing procedure]
Next, the procedure of the user attribute update process executed by the information processing apparatus 3 according to the embodiment will be described with reference to FIG. FIG. 6 is a flowchart illustrating a user attribute update processing procedure by the information processing apparatus 3 according to the embodiment.

  As illustrated in FIG. 6, the information processing device 3 acquires, as correct data, information on the user U1 who has a successful bid history of a product included in a predetermined category of an auction, and there is no successful bid history of a product included in the predetermined category. Information about the user U2 is acquired as incorrect answer data (step S101).

  Subsequently, the information processing device 3 generates a prediction model using the correct answer data and the incorrect answer data (step S102). The information processing apparatus 3 extracts and acquires feature information based on the prediction model (step S103).

  The information processing device 3 selects one of the users U2 and U3 who have not purchased the product purchased by the user U1 as correct data as a user who is a calculation target based on the prediction model (step S104). The information processing device 3 performs a calculation based on the prediction model based on the information of the user U selected in step S104 (step S105). The information processing device 3 compares the calculation result with a predetermined threshold Th (step S106). When the calculation result is smaller than the predetermined threshold Th (step S106; No), the process proceeds to step S108.

  On the other hand, when the calculation result is equal to or greater than the predetermined threshold Th (step S106; Yes), the user attribute selected in step S104 is updated based on the feature information extracted and acquired in step S103 (step S107). The information processing device 3 determines whether or not the calculation based on the prediction model has been performed on all the users U2 and U3 who have not purchased the product purchased by the user U1 as correct data (step S108). When it determines with having calculated with respect to all the users U2 and U3 (step S108; Yes), a process is complete | finished. On the other hand, when there are users U2 and U3 who have not performed the calculation (step S108; No), the process returns to step S104, and the calculation based on the prediction model is performed on the users U2 and U3 which have not performed the calculation.

  Note that FIG. 6 shows an example in which the information processing apparatus 3 performs calculation using a prediction model after extracting and acquiring feature information. However, the present invention is not limited to this example, and the information processing apparatus 3 may extract and acquire feature information after performing a calculation using a prediction model. In this case, the number of feature information extracted and acquired may be changed according to the calculation result of the prediction model. For example, when the value of the calculation result by the prediction model is large, the number of pieces of feature information to be acquired may be increased, and when the value is small, the number may be decreased.

  Further, as described above, when the model generation unit 21 generates a prediction model, “−1” is set as a dependent variable when a successful bid is made for a product, and “1” is set when a successful bid is not made for the product. If so, the magnitude relationship between the calculation result in step S106 and the predetermined threshold Th is reversed. That is, when the calculation result is larger than the predetermined threshold Th in step S106, the process proceeds to step S108, and when the calculation result is equal to or less than the predetermined threshold Th, the process proceeds to step S107.

[4. Modified example]
The embodiment described above may be implemented in various different forms other than the above embodiment. Accordingly, other embodiments will be described below.

  In the above-described embodiment, the purchase history in a predetermined e-commerce has been described as the successful bid history in the auction. However, the present invention is not limited to this. For example, the purchase history of shopping, the bidding history at the auction, the registration history to favorites, etc. are used as the purchase history at a predetermined e-commerce so as to determine whether the information of the user U is correct data or incorrect data. May be. Note that the purchase history of the user U is not limited to products. It may be a service, or a service and a product.

  In this way, the information processing apparatus 3 uses the information of the user U1 who has a purchase history of a product and / or service sold by a predetermined e-commerce as correct data, and the user U2 has no purchase history of the product and / or service. , U3 information is incorrect data. Thereby, the prediction accuracy of the prediction model can be improved, the content distribution target users can be effectively expanded, and the appeal effect of the content can be enhanced.

In addition, an example is shown in which the user information storage unit 31 stores the feature word itself as “psychographic attribute”. Here, the user information storage unit 31 sets the feature word as an item of “psychographic attribute” and sets “1” or “0” in the item corresponding to the feature word, thereby obtaining the feature word and the user U. You may make it memorize | store by matching. In this case, when the update unit 24 updates the user attribute based on the feature word acquired by the acquisition unit 22, “1” may be set in the item corresponding to the feature word. Alternatively, a predetermined numerical value may be set for each item according to the degree of interest of the user U. In this case, the predetermined numerical value corresponding to the degree of interest may be determined based on, for example, the coefficient a _{i of the} prediction model.

  Moreover, in the said embodiment, the user information storage part 31 of the information processing apparatus 3 showed about the example which memorize | stores the user's U attribute information and the purchase history of goods. Here, the information processing device 3 may store the attribute information of the user U and the purchase history of the products in different storage units. In this case, the purchase history of the product may be stored as a log of the user U's behavior history, for example.

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

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

  For example, the advertisement information storage unit 32 shown in FIG. 3 may be held by a storage server or the like (not shown) without being held by the information processing apparatus 3. In this case, the information processing apparatus 3 acquires information related to the advertisement from the storage server. Further, for example, the information processing apparatus 3 includes a model information generation unit 21, an acquisition unit 22, a calculation unit 23, an update unit 24, and a user information storage unit 31, a user information management device, an advertisement distribution unit 25, and an advertisement information storage unit. 32 may be distributed to the advertisement distribution apparatus having 32.

  Moreover, in the said embodiment, the advertisement delivery system 1 which delivers advertisement content as an example of content was demonstrated. Here, the content may be content other than advertising content such as article content and video content.

[5. Hardware configuration]
Note that the information processing apparatus 3 in the above-described embodiment is realized by a computer 100 having a configuration as illustrated in FIG. 7, for example. FIG. 7 is a hardware configuration diagram illustrating an example of a computer 100 that realizes an information processing function. The computer 100 includes a CPU 301, a RAM 302, a ROM (Read Only Memory) 303, an HDD (Hard Disk Drive) 304, a communication interface (I / F) 305, an input / output interface (I / F) 306, and a media interface (I / F). 307.

  The CPU 301 operates based on a program stored in the ROM 303 or the HDD 304 and controls each unit. The ROM 303 stores a boot program executed by the CPU 301 when the computer 100 is started up, a program depending on the hardware of the computer 100, and the like.

  The HDD 304 stores a program executed by the CPU 301, data used by the program, and the like. The communication interface 305 receives data from other devices via the communication line 309 and sends the data to the CPU 301, and transmits the data generated by the CPU 301 to other devices via the communication line 309.

  The CPU 301 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 306. The CPU 301 acquires data from the input device via the input / output interface 306. Further, the CPU 301 outputs the generated data to the output device via the input / output interface 306.

  The media interface 307 reads a program or data stored in the recording medium 308 and provides it to the CPU 301 via the RAM 302. The CPU 301 loads the program from the recording medium 308 onto the RAM 302 via the media interface 307 and executes the loaded program. The recording medium 308 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  When the computer 100 functions as the information processing apparatus 3 in the embodiment, the CPU 301 of the computer 100 executes a program loaded on the RAM 302 to thereby execute the model generation unit 21, the acquisition unit 22, and the calculation unit 23 of the control unit 20. Each function of the update unit 24 and the advertisement distribution unit 25 is realized. The HDD 304 stores data in the storage unit 30.

  The CPU 301 of the computer 100 reads these programs from the recording medium 308 and executes them, but as another example, these programs may be acquired from other devices via the communication line 309.

[6. effect]
As described above, the information processing apparatus 3 according to the embodiment generates a prediction model using the information of the first user (user U1) who has a purchase history of products and / or services sold in a predetermined e-commerce as correct data, Based on the prediction model, the attributes of the second users (users U2, U3) different from the first user are updated. Thereby, the information processing apparatus 3 can effectively extend the content distribution target user to the second user as well as the first user, and can enhance the appeal effect of the content.

  Moreover, the update part 24 of the information processing apparatus 3 of embodiment updates the attribute of the 2nd user determined that the calculation result by a prediction model satisfy | fills predetermined conditions based on a 2nd user's information. Thereby, the information processing apparatus 3 can effectively extend the content distribution target user to the second user whose calculation result based on the prediction model satisfies the predetermined condition, and can enhance the appeal effect of the content.

  In addition, the update unit 24 of the information processing apparatus 3 according to the embodiment, based on the prediction model generated by the model generation unit 21, among at least one of predetermined e-commerce, products, and services among the attributes of the second users U2 and U3. Update the attribute corresponding to one. As a result, the information processing apparatus 3 can effectively expand the content distribution target user and update the attribute corresponding to at least one of predetermined e-commerce, product, and service, thereby appealing the content. The effect can be enhanced.

  Further, the update unit 24 of the information processing apparatus 3 according to the embodiment updates the attribute information of the user who has no purchase history of products and / or services in a predetermined e-commerce based on the prediction model. Thereby, the information processing apparatus 3 can effectively expand the content distribution target users.

  In addition, the acquisition unit 22 of the information processing apparatus 3 according to the embodiment extracts and acquires one or more feature information based on the prediction model, and the update unit 24 updates the attributes of the users U2 and U3 based on the feature information. . Accordingly, the content can be distributed to the users U2 and U3 based on the feature information, the content distribution target users can be effectively expanded, and the content appeal effect can be enhanced.

  Further, the model generation unit 21 of the information processing device 3 according to the embodiment generates a prediction model different from the prediction model used by the update unit 24 as the prediction model used by the acquisition unit 22. As described above, even when the prediction models are different, feature information can be extracted, and content distribution target users can be effectively expanded.

  Further, the information processing apparatus 3 according to the embodiment distributes the advertising content based on the attributes of the users U2 and U3 updated by the updating unit 24. As a result, it is possible to effectively expand the content distribution target users, and to distribute content with a high appeal effect to the expanded users.

  In addition, the update unit 24 of the information processing apparatus 3 according to the embodiment may include attribute information of a second user who has a purchase history of products and / or services in a category including products and / or services purchased by the first user, Update based on predictive model. Accordingly, it is possible to effectively extend the content distribution target user to the second user who has the purchase history of the product and / or service of the category including the product and / or service purchased by the first user.

  Moreover, the update part 24 of the information processing apparatus 3 of embodiment updates the attribute of a 2nd user, when the number of 1st users satisfy | fills predetermined conditions. Thereby, according to the number of 1st users, the distribution target user of a content can be expanded effectively.

  As described above, some of the embodiments of the present application have been described in detail with reference to the drawings. However, these are merely examples, and various modifications, including the aspects described in the disclosure section of the invention, based on the knowledge of those skilled in the art, It is possible to implement the present invention in other forms with improvements.

  In addition, the above-mentioned “section (module, unit)” can be read as “means” or “circuit”. For example, the calculation unit can be read as calculation means or a calculation circuit.

DESCRIPTION OF SYMBOLS 1 Advertisement delivery system 2 Web server 3 Information processing apparatus 5 Terminal device 6 Communication network 10 Communication part 20 Control part 21 Model generation part 22 Acquisition part 23 Calculation part 24 Update part 25 Advertisement distribution part 30 Storage part 31 User information storage part 32 Advertisement Information storage unit

Claims (11)

A model generation unit that generates a prediction model using information of a first user who has a purchase history of goods and / or services sold in a predetermined e-commerce as correct data;
An updating unit for updating an attribute of a second user different from the first user based on the prediction model;
An information processing apparatus comprising: The update unit
The information processing apparatus according to claim 1, wherein an attribute of the second user that satisfies a predetermined condition as a result of calculation by the prediction model is updated. The update unit
The attribute corresponding to at least one of the predetermined e-commerce, the product, and the service among the attributes of the second user is updated based on the prediction model generated by the model generation unit. Item 3. The information processing device according to item 1 or 2. Based on the prediction model generated by the model generation unit, comprising an extraction acquisition unit that extracts and acquires one or more feature information,
The update unit
The information processing apparatus according to any one of claims 1 to 3, wherein an attribute of the second user is updated based on the feature information. The model generation unit
The information processing apparatus according to claim 4, wherein a prediction model different from the prediction model used by the update unit is generated as the prediction model used by the extraction acquisition unit. The update unit
The attribute of the second user having no purchase history of goods and / or services in the predetermined e-commerce is updated based on the prediction model. Information processing device. The update unit
The attribute of the second user having a purchase history of a product and / or service in a category including the product and / or service purchased by the first user is updated based on the prediction model. Item 6. The information processing device according to any one of Items 1 to 5. The update unit
The information processing apparatus according to any one of claims 1 to 7, wherein the attribute of the second user is updated when the number of the first users satisfies a predetermined condition. The information processing apparatus according to claim 1, further comprising: a distribution unit that distributes content based on the attribute of the second user updated by the update unit. An information processing method executed by a computer,
Generating a prediction model using information of a first user who has a purchase history of goods and / or services sold in a predetermined e-commerce as correct data;
Updating an attribute of a second user different from the first user based on the prediction model;
An information processing method comprising: On the computer,
A procedure for generating a prediction model using information of a first user who has a purchase history of goods and / or services sold in a predetermined e-commerce as correct data;
Updating an attribute of a second user different from the first user based on the prediction model;
An information processing program for executing

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