WO2017158798A1

WO2017158798A1 - Information processing device, information distribution system, information processing method, and information processing program

Info

Publication number: WO2017158798A1
Application number: PCT/JP2016/058556
Authority: WO
Inventors: 今村　健; 美紀岡本; 浩邦福
Original assignee: 富士通株式会社
Priority date: 2016-03-17
Filing date: 2016-03-17
Publication date: 2017-09-21
Also published as: JP6669244B2; JPWO2017158798A1

Abstract

An information processing device (100) sorts a plurality of users with membership into clusters according to their merchandise purchase tendencies on the basis of purchase history information (110) indicating a merchandise purchase history in association with each of the plurality of users with membership. The information processing device (100) identifies a web page browsing tendency of a user with membership belonging to a first cluster on the basis of browsing history information (120) indicating a web page browsing history in association with each of the users with membership belonging to the first cluster. The information processing device (100) outputs identification information of a user terminal from which the web page browsing history corresponding to the identified browsing tendency has been detected.

Description

Information processing apparatus, information distribution system, information processing method, and information processing program

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

Conventionally, there are SSP (Supply Side Platform) and DSP (Demand Side Platform) as Internet advertisement distribution support tools. The SSP supports the selection of the optimal advertisement and the sales of the advertising space so that the profit on the side of the distributor having the advertising space is maximized. The DSP supports the purchase of the optimal advertising space and the selection of the distribution target so that the advertising effect is maximized for the advertiser.

Also, there is a so-called retargeting advertisement as a behavioral targeting advertisement using the Internet. The retargeting advertisement is a technique for increasing the recognition rate and appeal to the viewer by distributing the same advertisement as the advertisement distributed on a certain website again when visiting another site.

As prior art, the customer attribute database that defines the customer attributes in the company, the purchase history database that records the customer's product purchase history, and the customer satisfaction database data are integrated and analyzed to group the customers. Then, there is one that extracts the needs of customers of each group. Further, there is a technique for storing advertisement information corresponding to purchase history information related to purchase history of individual customers, and calling and outputting advertisement information corresponding to customer specifying information for specifying a customer who has entered the store. . In addition, there is a technique for classifying a user category by reflecting a user's interest response to an advertisement.

JP 2001-23047 A JP 11-219481 A JP 2013-210940 A

However, it is still difficult to perform effective advertisement distribution with the conventional technology. For example, in a retargeting advertisement, a person who happens to see a website or a person who is examining some function of a purchased product may be targeted, and effective advertisement distribution may not be performed.

In one aspect, the present invention makes it possible to identify a user who has a predetermined purchase tendency for a product and another user who has a similar browsing tendency of a web page, and to enable effective information distribution. With the goal.

According to one aspect of the present invention, the plurality of member users are classified into clusters according to the purchase tendency of the products based on the purchase history information of the products associated with each of the plurality of member users. Based on the browsing history information of the web pages associated with each member user belonging to one cluster, the browsing tendency of the web pages of the member users belonging to the first cluster is specified, and the browsing tendency is identified An information processing apparatus, an information processing method, and an information processing program that output identification information of a user terminal in which a browsing history of a web page to be detected is detected are proposed.

Further, according to one aspect of the present invention, the classification unit that classifies the plurality of member users into clusters according to the purchase tendency of the products based on the purchase history information of the products associated with each of the plurality of member users. And the browsing tendency of the web pages of the member users belonging to the first cluster based on the browsing history information of the web pages associated with each of the member users belonging to the first cluster classified by the classification unit. A specifying unit to be identified; an output unit for outputting identification information of a user terminal in which a browsing history of the web page corresponding to the browsing tendency identified by the identifying unit is detected; and a predetermined embedded in a predetermined region of the web page An information distribution system including a distribution unit that distributes information to a user terminal of the identification information output by the output unit is proposed.

According to one aspect of the present invention, the plurality of users are classified into clusters according to the purchase tendency of the products based on the purchase history information of the products associated with each of the plurality of users. Based on the cookie information stored in the terminal device of the user belonging to one cluster, the browsing history information of the web page of the user belonging to the first cluster is acquired, and based on the acquired browsing history information, the first An information processing apparatus, an information processing method, and an information processing program for specifying a browsing tendency of a user's web page belonging to one cluster and outputting the specified browsing tendency are proposed.

According to one aspect of the present invention, it is possible to specify a user who has a predetermined purchase tendency for a product and another user whose web page browsing tendency is similar. Further, it is possible to assist so that effective information distribution can be performed.

FIG. 1 is an explanatory diagram of an example of the information processing method according to the embodiment. FIG. 2 is an explanatory diagram showing a system configuration example of the information distribution system 200. FIG. 3 is a block diagram illustrating a hardware configuration example of the information processing apparatus 100. FIG. 4 is a block diagram illustrating a hardware configuration example of the advertisement request side device 201. FIG. 5 is an explanatory diagram showing an example of the contents stored in the purchase history DB 220. FIG. 6 is an explanatory diagram showing an example of the contents stored in the browsing history DB 230. FIG. 7 is a block diagram illustrating a functional configuration example of the information processing apparatus 100. FIG. 8 is an explanatory diagram showing a specific example of the cluster information 800. FIG. 9 is an explanatory diagram showing a specific example of the keyword list 900. FIG. 10 is an explanatory diagram showing a specific example of the fitness level list 1000. FIG. 11 is an explanatory diagram of a specific example of distribution destination information. FIG. 12 is a flowchart illustrating an example of an information processing procedure of the information processing apparatus 100. FIG. 13 is a flowchart illustrating an example of a specific processing procedure of the browsing tendency specifying process. FIG. 14 is a flowchart illustrating an example of a delivery request side support processing procedure of the DSP server 203.

Embodiments of an information processing apparatus, an information distribution system, an information processing method, and an information processing program according to the present invention will be described below in detail with reference to the drawings.

(One Example of Information Processing Method)
FIG. 1 is an explanatory diagram of an example of the information processing method according to the embodiment. In FIG. 1, an information processing apparatus 100 is a computer that supports the distribution of information. The information to be distributed is information that the distribution side issues to the distribution destination with some intention, and is, for example, an advertisement of goods or services from the company side to consumers. The information to be distributed may be a questionnaire request from a company, a publicity message, or the like.

Here, people with similar hobbies and lifestyles often have similar purchase trends. For example, many people who make fruit brandies (brandy soaked with fruits) tend to cook, and tend to purchase and try even slightly expensive cooking utensils. For this reason, it can be said that it is effective to advertise high-class cooking utensils to those who are interested in making fruit brandy.

If such a relationship can be identified in advance, it can be said that effective advertisement distribution can be performed and the advertising effect can be improved. However, it is difficult to identify such relationships one by one manually. Also, it is often not easy to find a relationship where it is effective to advertise high-quality cooking utensils to people who are interested in making fruit brandies.

Therefore, in the present embodiment, a plurality of users are classified from the purchase history of products, the browsing tendency of web pages of users belonging to a specific cluster on the Internet is specified, and other users with similar browsing tendencies are distributed to The information processing method elected as will be described. Hereinafter, a processing example of the information processing apparatus 100 will be described.

(1) Based on the purchase history information 110, the information processing apparatus 100 classifies a plurality of users into clusters according to the purchase tendency of products. Here, the plurality of users is a set of users to which identification information for identifying the users is assigned, for example, a plurality of member users who are participating in some kind of association. As a member user, for example, a house card member of a department store can be cited.

In the following explanation, a plurality of member users who participate in a certain society will be described as an example as a plurality of users classified according to the purchase tendency of products.

The purchase history information 110 is information indicating a purchase history of a product in association with each of a plurality of member users. For example, the purchase history information 110 indicates the product name, brand name, product category, purchase amount, unit price, purchase price, purchase date, etc. of the product purchased by each member user in association with the identification information of each member user.

The existing arbitrary method can be used as the clustering method. Specifically, for example, based on the purchase history information 110, the information processing apparatus 100 uses a feature vector whose element is the purchase amount of each member user for each product category, using a division optimization type clustering method, You may decide to classify into clusters according to the purchase tendency.

The product category is a category for classifying products and can be set arbitrarily. For example, the product category may be men's clothing, women's clothing, furniture, bedding, groceries, miscellaneous goods, etc., or a specific brand name or product name, or a combination of a specific brand name or product name. May be. Thereby, it is possible to classify a plurality of member users based on the feature of how much products in which product category are purchased.

In addition, the information processing apparatus 100 may set a word that characterizes the classified cluster. The phrase characterizing the cluster is information for determining what kind of member user the cluster is. The words that characterize the cluster may be set manually, or may be automatically set by the information processing apparatus 100.

For example, a user of the information processing apparatus 100 may analyze a purchase history of a member user's product belonging to a cluster, and set a phrase that characterizes the cluster. Further, for example, the information processing apparatus 100 refers to the purchase history of the product of the member user belonging to the cluster, and uses the category name (or brand name) of the product category having the largest purchase amount as a word that characterizes the cluster. It may be set.

The purchase history information 110 may include, for example, information indicating a store visit history in association with each of a plurality of users. Thereby, for example, it is possible to classify a plurality of users on the basis of the characteristics of how many customers have visited which store.

In the example of FIG. 1, it is assumed that a plurality of member users are classified into cluster X, cluster Y, and cluster Z. Further, it is assumed that the cluster X is a set of member users who purchase a large amount of high-quality cooking utensils, and “high-class cooking utensil” is set as a word that characterizes the cluster X.

(2) Based on the browsing history information 120, the information processing apparatus 100 identifies the browsing tendency of the web pages of member users belonging to the first cluster. Here, the first cluster is any cluster in which a plurality of member users are classified according to the purchase tendency of products, and can be arbitrarily selected.

The browsing history information 120 is information indicating the browsing history of the web page in association with each member user belonging to the first cluster. For example, the browsing history information 120 indicates the URL (Uniform Resource Locator) of the web page browsed by each member user, the browsing date, etc. in association with the identification information of each member user.

Specifically, for example, the information processing apparatus 100 refers to the browsing history information 120 and extracts a keyword from the URL of a web page browsed by each member user belonging to the first cluster. More specifically, the information processing apparatus 100 extracts, for example, a directory name or a file name as a keyword from a URL path name.

Next, the information processing apparatus 100 calculates the appearance frequency of each keyword based on the number of appearances of each extracted keyword. Then, the information processing apparatus 100 identifies the keyword with the highest appearance frequency or the top several keywords with the highest appearance frequency as the browsing tendency.

In the example of FIG. 1, “fruit brandy” is specified as the browsing tendency of the web pages of the member users belonging to the cluster X. The browsing tendency “fruit brandy” means that there is a high possibility that many member users belonging to the cluster X are browsing web pages related to fruit brandy.

(3) The information processing apparatus 100 outputs the identification information of the user terminal in which the browsing history of the web page corresponding to the specified browsing tendency is detected. Here, the web page corresponding to the browsing tendency is, for example, a web page including the keyword specified as the browsing tendency in the web page or in the URL. The user terminal is a computer having a browser for browsing web pages, such as a PC (Personal Computer), a tablet PC, a smartphone, and a mobile phone.

Specifically, for example, the information processing apparatus 100 detects a user terminal having a web page browsing history corresponding to the specified browsing tendency based on the browsing history information 130 of the user terminal. Here, the browsing history information 130 of the user terminal is information indicating the browsing history of the web page in association with the user terminal. For example, the browsing history information 130 of the user terminal indicates the URL of the web page browsed on each user terminal, the browsing date, etc. in association with the identification information of each user terminal.

The user terminal identification information is, for example, information that identifies a browser instance used by the user. Even if the same user has different user terminals, the browser instance is different. Also, even if the same user uses the same user terminal and logs in with a different ID, browser instances are different. Furthermore, even if the same user logs in with the same ID using the same user terminal, if the browser is different, the browser instance is different.

As the identification information of the user terminal, for example, cookie information transmitted from the accessed website and stored in the user terminal through the browser can be used. The cookie information is information used for identifying the visitor of the website, and includes, for example, information (cookie name and value) regarding user identification and attributes.

Note that the browsing history information 130 of the user terminal may be included in the information processing apparatus 100 or may be included in another computer (for example, an SSP server 204 as illustrated in FIG. 2). Further, the information processing apparatus 100 may detect a user terminal having a browsing history of a web page corresponding to the specified browsing tendency by inquiring to another computer.

In the example of FIG. 1, distribution destination information 140 including identification information (for example, cookie information) of the user terminals Ta, Tb, and Tc in which the browsing history of the web page corresponding to the browsing tendency “fruit brandy” is detected is output. Yes. The distribution destination information 140 includes, for example, the phrase “luxury cooking utensil” that characterizes the cluster X.

As described above, according to the information processing apparatus 100, the browsing tendency of the web page is similar to the member user belonging to a certain cluster classified according to the purchase tendency of the product, that is, the hobby preference is similar to the member user. The user terminal of the user can be specified. This makes it possible to distribute advertisements to other users who have similar hobbies and preferences to member users who have a predetermined purchase tendency for the product. From another aspect, it is possible to specify the user terminal to which information is to be distributed, so the amount of data transmitted to the communication network is reduced rather than distributing information uniformly without specifying the user terminal. It leads to things.

In the example of FIG. 1, an advertiser, an advertising agency, or the like refers to the distribution destination information 140, so that the user terminals Ta of other users who have similar hobbies and preferences with member users who have a purchase history of “luxury cooking utensils”. , Tb, Tc can be specified. As a result, it is possible to narrow down the targets (user terminals Ta, Tb, Tc) that are likely to be interested in “luxury cooking utensils” and perform advertisement distribution, thereby improving the advertising effect.

(System configuration example of the information distribution system 200)
Next, a system configuration example of the information distribution system 200 according to the embodiment will be described. In the following description, advertisements for products and services from the company side to consumers will be described as examples of distribution target information.

FIG. 2 is an explanatory diagram showing a system configuration example of the information distribution system 200. In FIG. 2, the information distribution system 200 includes an information processing apparatus 100, an advertisement request side apparatus 201, a plurality of user terminals 202, a plurality of DSP servers 203, and an SSP server 204. In the information distribution system 200, the information processing apparatus 100, the advertisement request side apparatus 201, the user terminal 202, the DSP server 203, and the SSP server 204 are connected via a wired or wireless network 210. The network 210 is, for example, a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, or the like.

Here, the information processing apparatus 100 has a purchase history DB (Database) 220 and a browsing history DB 230, and supports advertisement distribution. The contents stored in the purchase history DB 220 and the browsing history DB 230 will be described later with reference to FIGS. 5 and 6.

The advertisement request side device 201 is a computer used by a user (for example, an advertiser) on the advertisement request side, such as a PC or a tablet PC. The user terminal 202 is a computer having a browser for browsing web pages, and is a PC, a tablet PC, or the like, for example. The user terminals Ta, Tb, and Tc illustrated in FIG. 1 correspond to the user terminal 202, for example.

The DSP server 203 is a computer that supports the purchase of the optimal advertising space and the selection of the distribution target so that the advertising effect is maximized for the advertiser. The SSP server 204 is a computer that supports the selection of the optimal advertisement and the sales of the advertising space so that the profit on the side of the distributor having the advertising space is maximized.

Further, the SSP server 204 manages browsing history information indicating browsing history of web pages on the user terminal 202 in association with identification information of the user terminal 202, for example. The identification information of the user terminal 202 is, for example, cookie information transmitted from the accessed website and stored in the user terminal 202 through the browser.

Here, an operation example of the information distribution system 200 will be described.

First, the advertisement requesting apparatus 201 presents advertisement bid conditions to the DSP server 203. The bid conditions for the advertisement include, for example, a budget, an advertisement placement standard, an upper limit bid price, a target user attribute (for example, cookie information of the user terminal 202), and the like.

Next, when the website of the publisher (medium operator) is accessed on the user terminal 202 of the viewer, a tag embedded in the web page is activated, and an advertisement space is sent to the SSP server 204 via an unillustrated ad server. Is requested (ad request). The advertisement request includes, for example, cookie information of the user terminal 202 of the viewer.

When the SSP server 204 receives the advertisement request, the SSP server 204 transmits a bid request to each DSP server 203. The bid request includes, for example, cookie information of the user terminal 202 of the viewer, IP (Internet Protocol) address, advertisement space ID, advertisement size, minimum bid price (floor price), bid closing time, and the like.

When each DSP server 203 receives the bid request, it compares with the bid condition of the advertisement presented from the advertisement request side device 201 to determine whether or not to bid. When each DSP server 203 determines to bid, it sends a bid response to the SSP server 204. The bid response includes, for example, a bid price.

The SSP server 204 compares the bid price of the bid response transmitted from each DSP server 203 and determines the winning DSP. Then, the SSP server 204 transmits a victory notification and a contract price to the winning DSP. At this time, the information on the posted advertiser received from the advertisement requesting apparatus 201 is notified to the unillustrated ad server via the winning DSP, and is transmitted from the ad server to the user terminal 202 of the viewer.

After that, when the user terminal 202 (tag embedded in the web page) of the viewer receives the information of the posted advertiser, it transmits an advertisement request to the DSP server 203 (victory DSP). When the DSP server 203 receives the advertisement request from the user terminal 202, the DSP server 203 distributes advertisement information embedded in a predetermined area of the web page to the user terminal 202. The predetermined area is, for example, an area in the web page specified from the advertising space ID included in the bid request. As a result, the advertisement information is displayed in the advertisement space in the web page of the user terminal 202 of the viewer.

In the example of FIG. 2, only one advertisement request side device 201 is shown, but the information distribution system 200 may include a plurality of advertisement request side devices 201. Further, the information processing apparatus 100 may be realized by any one of the advertisement request side apparatus 201, the DSP server 203, and the SSP server 204, for example. The above description is an example in which the advertisement delivery process is executed by the DSP server 203 and the SSP server 204 performing the process in cooperation with the user terminal 202 specified by the information processing apparatus 100. The information distribution system 200 is not limited to the above example, and the information distribution system 200 only needs to have a configuration for transmitting advertisement information to be displayed in the advertisement space in the web page to the user terminal 202 specified by the information processing apparatus 100. .

(Hardware configuration example of information processing apparatus 100)
Next, a hardware configuration example of the information processing apparatus 100 illustrated in FIG. 2 will be described.

FIG. 3 is a block diagram illustrating a hardware configuration example of the information processing apparatus 100. In FIG. 3, the information processing apparatus 100 includes a CPU (Central Processing Unit) 301, a memory 302, an I / F (Interface) 303, a disk drive 304, and a disk 305. Each component is connected by a bus 300.

Here, the CPU 301 controls the entire information processing apparatus 100. The memory 302 includes, for example, a ROM (Read Only Memory), a RAM (Random Access Memory), a flash ROM, and the like. Specifically, for example, a flash ROM or ROM stores various programs, and a RAM is used as a work area for the CPU 301. The program stored in the memory 302 is loaded into the CPU 301 to cause the CPU 301 to execute the coded process.

The I / F 303 is connected to the network 210 through a communication line, and is connected to other computers (for example, the advertisement request side device 201, the user terminal 202, the DSP server 203, and the SSP server 204 shown in FIG. 2) via the network 210. Is done. The I / F 303 controls an interface between the network 210 and the apparatus, and controls input / output of data from other computers. For example, a modem or a LAN adapter may be employed as the I / F 303.

The disk drive 304 controls reading / writing of data with respect to the disk 305 according to the control of the CPU 301. The disk 305 stores data written under the control of the disk drive 304. Examples of the disk 305 include a magnetic disk and an optical disk.

Note that the information processing apparatus 100 may include, for example, an SSD (Solid State Drive), a keyboard, a mouse, and a display in addition to the above-described components. Also, the DSP server 203 and the SSP server 204 shown in FIG. 2 can be realized by the same hardware configuration as the information processing apparatus 100.

(Hardware configuration example of the advertisement requesting apparatus 201)
Next, a hardware configuration example of the advertisement request side device 201 illustrated in FIG. 2 will be described.

FIG. 4 is a block diagram illustrating a hardware configuration example of the advertisement request side apparatus 201. In FIG. 4, the advertisement request side device 201 includes a CPU 401, a memory 402, a disk drive 403, a disk 404, an I / F 405, a display 406, and an input device 407. Each component is connected by a bus 400.

Here, the CPU 401 governs overall control of the advertisement request side device 201. The memory 402 includes, for example, a ROM, a RAM, a flash ROM, and the like. Specifically, for example, a flash ROM or ROM stores various programs, and the RAM is used as a work area of the CPU 401. The program stored in the memory 402 is loaded on the CPU 401 to cause the CPU 401 to execute the coded process.

The disk drive 403 controls data read / write with respect to the disk 404 according to the control of the CPU 401. The disk 404 stores data written under the control of the disk drive 403. Examples of the disk 404 include a magnetic disk and an optical disk.

The I / F 405 is connected to the network 210 via a communication line, and is connected to another device (for example, the DSP server 203 shown in FIG. 2) via the network 210. The I / F 405 controls the interface between the network 210 and the own apparatus, and controls input / output of data from other apparatuses.

The display 406 displays data such as a document, an image, and function information as well as a cursor, an icon, or a tool box. As the display 406, for example, a liquid crystal display, CRT (Cathode Ray Tube), or the like can be employed.

The input device 407 has keys for inputting characters, numbers, various instructions, etc., and inputs data. The input device 407 may be a keyboard or a mouse, or may be a touch panel type input pad or a numeric keypad.

It should be noted that the advertisement requesting apparatus 201 may not include, for example, the disk drive 403 and the disk 404 among the above-described components. The user terminal 202 shown in FIG. 2 can also be realized by the same hardware configuration as that of the advertisement requesting apparatus 201.

(Storage contents of purchase history DB 220)
Next, the contents stored in the purchase history DB 220 of the information processing apparatus 100 will be described. The purchase history DB 220 is realized by a storage device such as the memory 302 and the disk 305 shown in FIG. The purchase history information 110 illustrated in FIG. 1 corresponds to the purchase history information in the purchase history DB 220, for example.

FIG. 5 is an explanatory diagram showing an example of the contents stored in the purchase history DB 220. In FIG. 5, the purchase history DB 220 has fields of member user ID, purchase date and time, product category, brand name, product name, quantity, unit price, and amount of money. By setting information in each field, purchase history information ( For example, purchase history information 500-1 to 500-3) is stored as a record.

Here, the member user ID is an identifier for uniquely identifying the member user. The purchase date and time is the date and time when the member user purchased the product. The product category is a classification for classifying products purchased by member users. The brand name is the brand name (manufacturer name) of the product purchased by the member user.

The product name is the name of the product purchased by the member user. The quantity is the quantity of the product purchased by the member user (unit: piece). The unit price is the unit price of the product purchased by the member user (unit: yen). The amount is the amount paid when the member user purchases the product purchased (unit: yen). The product purchased by the member user may be purchased by actually visiting the store, or may be purchased on the Internet.

For example, the purchase history information 500-1 indicates the purchase date and time “11:20 on March 1, 2016” when the member user U1 purchased the product. The purchase history information 500-1 includes the product category “luxury cooking utensil”, the brand name “xxx”, the product name “anhydrous pan”, the quantity “1”, the unit price “28,000” of the product purchased by the member user U1. "Yen" and amount "28,000 yen" are shown.

(Storage contents of browsing history DB 230)
Next, the contents stored in the browsing history DB 230 of the information processing apparatus 100 will be described. The browsing history DB 230 is realized by a storage device such as the memory 302 and the disk 305 shown in FIG. Note that the browsing history information 120 illustrated in FIG. 1 corresponds to browsing history information in the browsing history DB 230, for example.

FIG. 6 is an explanatory diagram showing an example of the contents stored in the browsing history DB 230. In FIG. 6, the browsing history DB 230 has fields of member user ID, browsing date and URL, and by setting information in each field, browsing history information (for example, browsing history information 600-1 to 600-3) Is stored as a record.

Here, the member user ID is an identifier for uniquely identifying the member user. The browsing date is the date when the member user browsed the web page, for example, the date when the member user accessed the web page. URL is the URL of a web page viewed by a member user.

For example, the browsing history information 600-1 includes the browsing date “February 27, 2016, 19:12, when the member user U1 browsed the web page of the URL“ http://www.xry.co.jp/fruitbrandy/ ”. Is shown.

The browsing history information in the browsing history DB 230 may be generated, for example, by managing the browsing history of the member user in the information processing apparatus 100, or acquired by the information processing apparatus 100 from the SSP server 204. It may be.

(Functional configuration example of information processing apparatus 100)
Next, a functional configuration example of the information processing apparatus 100 will be described.

FIG. 7 is a block diagram illustrating a functional configuration example of the information processing apparatus 100. In FIG. 7, the information processing apparatus 100 includes an acquisition unit 701, a classification unit 702, a specification unit 703, a detection unit 704, and an output unit 705. The acquisition unit 701 to the output unit 705 are functions serving as control units. Specifically, for example, by causing the CPU 301 to execute a program stored in a storage device such as the memory 302 and the disk 305 illustrated in FIG. Alternatively, the function is realized by the I / F 303. The processing result of each functional unit is stored in a storage device such as the memory 302 and the disk 305, for example.

The acquisition unit 701 acquires purchase history information indicating a purchase history of a member user's product. The purchase history information indicates information on products purchased by the member users, and is, for example, purchase history information 500-1 to 500-3 shown in FIG.

Specifically, for example, the acquisition unit 701 acquires purchase history information of products sold at each store from a point-of-sale terminal (POS) terminal or an advertisement request side device 201 of each store that sells the product. You may decide to do it. For example, the acquisition unit 701 may acquire purchase history information of a plurality of member users by an operation input of a user (for example, an advertiser) using an input device (not shown). The acquired purchase history information is stored in, for example, the purchase history DB 220 shown in FIG.

Also, the acquisition unit 701 acquires browsing history information indicating the browsing history of the member user's web page. The browsing history information indicates information on web pages browsed by member users, and is browsing history information 600-1 to 600-3 shown in FIG. 6, for example.

Specifically, for example, the acquisition unit 701 collects cookie information stored in the user terminal 202 of the member user. And the acquisition part 701 acquires the browsing history information of the member user corresponding to the said cookie information by inquiring to the SSP server 204 using the collected cookie information of the user terminal 202 of the member user. Note that the cookie information stored in the user terminal 202 of the member user may be collected by inquiring the user terminal 202 from the information processing apparatus 100, for example.

Further, for example, the acquisition unit 701 may acquire browsing history information of a plurality of member users by an operation input of a user (for example, an advertiser) using an input device (not shown). For example, in the case where the information processing apparatus 100 is a computer that provides a search site, the acquisition unit 701 acquires browsing history information indicating a browsing history of web pages browsed by member users using the search site. You may decide. The acquired browsing history information is stored, for example, in the browsing history DB 230 shown in FIG.

The classification unit 702 classifies a plurality of member users into a cluster C corresponding to the purchase tendency of the product based on the acquired purchase history information indicating the purchase history of the product of the member user. Specifically, for example, first, the classification unit 702 refers to the purchase history DB 220 and creates a feature vector whose element is the purchase amount (quantity) of each member user for each product category.

Suppose, for example, product category 1, product category 2, and product category 3 as product categories. Further, it is assumed that the purchase amount of the

product category

1, 2, 3 of the member user U1 is 1, 0, 3 pieces. In this case, the feature vector of the member user U1 is (1, 0, 3).

Then, the classification unit 702 may classify a plurality of member users into the cluster C corresponding to the purchase tendency of the product by the division optimization type clustering method using the created feature vector of each member user. The partition optimization type clustering method is a method in which an evaluation function representing the goodness of partitioning is defined, and the partitioning is repeated so as to optimize the evaluation function. For example, there is a method using the k-means method. However, any existing method can be used as a clustering method.

Also, the classification unit 702 sets a phrase that characterizes the classified cluster C. Specifically, for example, the classification unit 702 may set a word or phrase that characterizes the classified cluster C by an operation input of a user (for example, an advertiser) using an input device (not shown). Further, for example, the classification unit 702 may accept an input of a word that characterizes the cluster C from the advertisement requesting apparatus 201. Thereby, for example, the advertiser can set an arbitrary phrase that characterizes each cluster C by analyzing the purchase history of the product of the member user belonging to each classified cluster C.

Further, for example, the classification unit 702 refers to the purchase history of the member user's products belonging to the classified cluster C, and uses the category names of the top several product categories with the largest total purchase amount as terms that characterize the cluster C. It may be set. Thereby, the words that characterize the cluster C can be automatically set.

The classified result is output as, for example, cluster information 800 as shown in FIG. Here, a specific example of the cluster information 800 will be described.

FIG. 8 is an explanatory diagram showing a specific example of the cluster information 800. In FIG. 8, cluster information 800 is information indicating a cluster ID, a cluster feature word, and a member user ID in association with each other. The cluster ID is an identifier for identifying a cluster C in which a plurality of member users are classified according to the purchase tendency of products. A cluster feature word is a phrase that characterizes cluster C. The member user ID is an identifier for identifying a member user belonging to the cluster C.

According to the cluster information 800, a member user belonging to each cluster C obtained by classifying a plurality of member users and a cluster feature word characterizing each cluster can be specified. For example, it can be specified that the member users belonging to the cluster C1 are “member users U1, U3, U7, U13, U22,...” And the cluster feature word that characterizes the cluster C1 is “high-class cooking utensils”.

Note that the classification unit 702 may accept input of cluster information from a user operation input using an input device (not shown) or from the advertisement requesting device 201, for example.

Returning to the description of FIG. 7, the specifying unit 703 specifies the browsing tendency of the web pages of the member users belonging to the cluster C based on the browsing history information of the web pages of the member users belonging to the classified cluster C. Here, the target cluster C for specifying the browsing tendency of the web page can be arbitrarily selected.

For example, the specifying unit 703 may accept selection of the target cluster C from, for example, a user operation input using an input device (not shown) or from the advertisement request side device 201. Thereby, for example, the advertiser can arbitrarily select the target cluster C based on the cluster feature word that characterizes each cluster C. Further, the specifying unit 703 may select all the classified clusters C as target clusters C.

Specifically, for example, first, the specifying unit 703 acquires the browsing history information of each member user belonging to the cluster C from the browsing history DB 230. Next, the specifying unit 703 refers to the acquired browsing history information and extracts a keyword (for example, a directory name or a file name) from the URL of the web page browsed by each member user belonging to the cluster C.

Further, the specifying unit 703 may access a web page browsed by each member user belonging to the cluster C and extract a keyword from the metadata of the web page. More specifically, for example, the specifying unit 703 may extract a keyword embedded as a meta tag for SEO (Search Engine Optimization).

Some keywords have the same meaning even if the keywords do not completely match, such as fluctuations in the notation and differences between English and Japanese. For this reason, for example, the specifying unit 703 may treat a keyword representing the same meaning as one keyword even if the keywords do not completely match.

The extracted keywords are stored in a keyword list 900 as shown in FIG.

Next, the specifying unit 703 refers to the acquired browsing history information, and for each extracted keyword, the number of member users who have a browsing history of the web page of the URL including the keyword (hereinafter referred to as “number of appearing users”). ) Is calculated. Further, the specifying unit 703 calculates the appearance frequency of each keyword by dividing the calculated number of appearance users of each keyword by the total number of member users belonging to the cluster C.

The number of appearance users and the appearance frequency of the calculated keyword are stored in, for example, a keyword list 900 as shown in FIG. Here, a specific example of the keyword list 900 will be described.

FIG. 9 is an explanatory diagram showing a specific example of the keyword list 900. In FIG. 9, a keyword list 900 is information indicating the number of appearance users and the appearance frequency for each keyword extracted from the URL and / or metadata of a web page viewed by each member user belonging to a certain cluster C.

Then, the specifying unit 703 may specify, for example, the top N keywords having the highest appearance frequency as browsing tendencies of web pages of member users belonging to the cluster C with reference to the keyword list 900. N can be arbitrarily set, and is set to a value of about 3 to 5, for example.

In the example of FIG. 9, when N is “N = 3”, the specifying unit 703 displays the top three keywords “fruit brandy, quilting, golf school” with the highest appearance frequency for the web pages of member users belonging to the cluster C. Identifies as the browsing tendency of

Also, the identifying unit 703 browses the web page browsing date before the latest purchase date included in the purchase history information of each member user among the browsing history information of each member user's web page belonging to the cluster C. History information may be acquired. At this time, the specifying unit 703 may acquire browsing history information of web pages whose browsing date / time is included in a period of the last few weeks before the latest purchase date / time.

Then, the specifying unit 703 may specify the browsing tendency of the web pages of member users belonging to the cluster C based on the acquired browsing history information. The latest purchase date and time is, for example, the latest purchase date and time when the target product set according to the cluster C is purchased. The target product is, for example, a product to be advertised set according to the cluster feature word of cluster C.

As an example, taking the cluster C1 of the cluster feature word “luxury cooking utensil” shown in FIG. 8 as an example, any product of the luxury cooking utensil is set as the target product. Note that the specifying unit 703 may accept setting of the target product from, for example, an operation input of a user (for example, an advertiser) using an input device (not shown) or from the advertisement request side device 201.

Thereby, the browsing tendency of the web page of the member user who belongs to the cluster C is specified based on the browsing history information of the web page browsed before the latest purchase date and time when each member user purchased the target product. be able to. That is, the browsing tendency according to the web page browsed before the member user purchased the advertising target product can be specified.

Further, the identifying unit 703 identifies the browsing tendency of the web pages of the member users belonging to the cluster C based on the browsing history information of the member users belonging to the cluster C who have relatively high suitability for the cluster C. You may decide to do it. Here, the degree of conformity with respect to the cluster C is an index value that represents how much the member user conforms to the characteristics of the cluster C.

Specifically, for example, the specifying unit 703 calculates an average vector of feature vectors having the purchase amount (quantity) for each product category of each member user belonging to the cluster C as an element, and calculates the calculated average vector of the cluster C. The center of gravity. Next, the specifying unit 703 calculates the distance between the feature vector of each member user and the center of gravity of the cluster C, and sets the calculated distance as the fitness of each member user with respect to the cluster C.

Then, the specifying unit 703 determines the web pages of the member users who belong to the cluster C based on the browsing history information of the top α member users who have a high calculated fitness, or the member users whose calculated fitness is equal to or greater than the threshold β. The browsing tendency may be specified. α and β can be arbitrarily set.

Thereby, the browsing tendency of the web page of the cluster C can be specified based on the browsing history information of the web page of the member user according to the characteristics of the cluster C.

The detecting unit 704 detects a user terminal having a web page browsing history corresponding to the specified browsing tendency. Specifically, for example, the detection unit 704 may inquire of the SSP server 204 about a user terminal 202 that has a browsing history of a web page with a URL including a keyword specified as a browsing tendency. In this case, the SSP server 204 specifies the cookie information of the user terminal 202 that has a browsing history of the web page with the URL including the keyword, and transmits the specified cookie information of the user terminal 202 to the information processing apparatus 100.

Further, for example, the detection unit 704 may inquire the SSP server 204 about the user terminal 202 having a browsing history of a web page including a keyword specified as a browsing tendency. In this case, for example, the SSP server 204 specifies the cookie information of the user terminal 202 that has a browsing history of the web page in which the keyword is embedded as a meta tag, and transmits the specified cookie information of the user terminal 202 to the information processing apparatus 100. May be.

Thereby, it is possible to detect the identification information of the user terminal 202 having a web page browsing history corresponding to the browsing tendency of the web pages of member users belonging to the cluster C.

The output unit 705 outputs the identification information of the detected user terminal 202. Examples of the output format of the output unit 705 include transmission to an external computer by the I / F 303, display on a display (not shown), and storage in a storage device such as the memory 302 and the disk 305.

Specifically, for example, the output unit 705 may transmit distribution destination information indicating the detected cookie information of the user terminal 202 to the advertisement requesting apparatus 201. The distribution destination information includes, for example, a cluster feature word that characterizes the cluster C. The advertisement request side device 201 as the transmission destination is, for example, the advertisement request side device 201 used by the advertiser of the target product. A specific example of the delivery destination information will be described later with reference to FIG.

Thereby, the advertiser is similar to the member user who belongs to the cluster C classified according to the purchase tendency of the product, and the browsing tendency of the web page is similar, that is, the user terminal 202 of another user whose hobby preference is similar to the member user. Can be specified.

Also, the output unit 705 may output the identification information of the user terminal 202 having a relatively high degree of suitability for the identified browsing tendency among the detected user terminals 202. Here, the degree of conformity with respect to the browsing tendency is an index value representing how much the detected user of the user terminal 202 follows the browsing tendency of the member user web pages belonging to the cluster C.

Specifically, for example, first, the output unit 705 acquires information indicating the web page browsing history in the detected user terminal 202 from the SSP server 204. At this time, the output unit 705 may acquire, from the SSP server 204, information indicating a browsing history of web pages browsed within a predetermined period on the detected user terminal 202. The predetermined period can be arbitrarily set. For example, the predetermined period is set to a period of about several weeks to several months.

Next, the output unit 705 calculates, based on the information indicating the acquired browsing history, for each detected user terminal 202, the number of browsing histories of web pages that include the keyword specified as the browsing tendency in the URL, The calculated number of browsing histories is taken as the degree of fitness for the browsing tendency. At this time, the output unit 705 may calculate the number of browsing histories of the web page in which the keyword specified as the browsing tendency is embedded as a meta tag, and set the degree of conformity to the browsing tendency.

The calculated adaptability to the browsing tendency for each user terminal 202 is stored, for example, in the adaptability list 1000 as shown in FIG. Here, a specific example of the fitness level list 1000 will be described.

FIG. 10 is an explanatory diagram showing a specific example of the fitness level list 1000. In FIG. 10, the fitness level list 1000 includes the cookie information of the user terminal 202 in which the browsing history of the web page corresponding to the browsing trend of the web page of the member user belonging to a certain cluster C is detected, and the fitness level for the browsing trend. Is shown in association with each other.

For example, the matching degree “10” of the cookie information “cookie1” indicates that the user terminal 202 identified from the cookie information “cookie1” has the number of browsing histories of web pages that include the keyword specified as the browsing tendency in the URL as “10”. ".

Then, the output unit 705 refers to the fitness level list 1000, for example, and outputs the top M pieces of cookie information having a high fitness level with respect to the browsing tendency. M can be arbitrarily set. For example, M is set based on the upper limit number of distributions determined according to the advertisement budget.

Specifically, for example, the output unit 705 displays the distribution destination information that associates the cluster feature words that characterize the cluster C with the top M pieces of cookie information having a high matching degree with respect to the browsing tendency, in the advertisement request side device 201. You may decide to transmit to. The distribution destination information may include the degree of fitness for the browsing tendency. Here, a specific example of distribution destination information will be described. Here, it is assumed that “M = 3” is set as M.

FIG. 11 is an explanatory diagram showing a specific example of distribution destination information. In FIG. 11, the distribution destination information 1100 associates the cluster feature words that characterize the cluster C, the top M pieces of cookie information having a high degree of fitness with respect to the browsing tendency of the cluster C, and the fitness with respect to the browsing tendency of the cluster C. It is information to represent. Note that the browsing tendency of the cluster C is a browsing tendency of web pages of member users belonging to the cluster C.

According to the distribution destination information 1100, for example, the browsing tendency of a web page is similar to a member user who has a purchase history of “luxury cooking utensils”, that is, the user terminals 202 of other users who have similar hobbies and preferences to the member user. Can be identified. In addition, since it is possible to confirm the degree of suitability of the browsing tendency of the cluster C, it is possible to support, for example, a determination as to which target the advertisement can be delivered by increasing the advertisement effect.

Also, the output unit 705 may transmit a request for distributing an advertisement to the detected user terminal 202. Specifically, for example, the output unit 705 may transmit an advertisement distribution request including the detected cookie information of the user terminal 202 as a bid condition to the DSP server 203. The advertisement distribution request includes, for example, advertisement information of the target product.

This makes it possible to automatically make an advertisement distribution request to users who have similar hobbies and preferences to member users belonging to cluster C.

For example, the output unit 705 refers to the distribution destination information 1100 and transmits an advertisement distribution request including the top M pieces of cookie information having high conformance to the browsing tendency of the cluster C to the DSP server 203. You may decide to do it. Thereby, it becomes possible to narrow down the target from which a higher advertising effect is obtained, and to perform advertisement distribution.

Further, the output unit 705 may output information indicating the browsing tendency of the cluster C specified by the specifying unit 703. Specifically, for example, the output unit 705 may transmit distribution destination information including information indicating the browsing tendency of the cluster C to the advertisement request side apparatus 201. Thereby, the information which can specify the browsing tendency of the member user who belongs to the cluster C can be provided.

(Information processing procedure of information processing apparatus 100)
Next, an information processing procedure of the information processing apparatus 100 will be described.

FIG. 12 is a flowchart illustrating an example of an information processing procedure of the information processing apparatus 100. In the flowchart of FIG. 12, first, the information processing apparatus 100 refers to the purchase history DB 220 and classifies a plurality of member users into a cluster C corresponding to the purchase tendency of the product (step S1201).

Next, the information processing apparatus 100 selects the target cluster C among the classified clusters C (step S1202). The target cluster C is a cluster C that identifies a browsing tendency of a web page, and is specified by, for example, a user (for example, an advertiser) of the advertisement request side device 201.

Then, the information processing apparatus 100 executes a browsing tendency specifying process for specifying the browsing tendency of the web pages of the member users belonging to the selected target cluster C (step S1203). A specific processing procedure of the browsing tendency specifying process will be described later with reference to FIG.

Next, the information processing apparatus 100 detects the user terminal 202 having a web page browsing history corresponding to the browsing tendency of the identified target cluster C (step S1204). Then, the information processing apparatus 100 calculates the degree of fitness for the browsing tendency of the target cluster C for each detected user terminal 202 (step S1205).

Next, the information processing apparatus 100 generates distribution destination information including cookie information of the top M user terminals 202 having a high degree of suitability for the calculated browsing tendency of the target cluster C (step S1206). Then, the information processing apparatus 100 transmits the generated distribution destination information to the advertisement request side apparatus 201 (step S1207), and ends a series of processes according to this flowchart.

Thereby, it is possible to output the distribution destination information that can identify the user terminal 202 in which the browsing history of the web page corresponding to the browsing tendency of the cluster C classified according to the purchase tendency of the product is detected. From another aspect, it is possible to specify the user terminal 202 to which information is to be distributed, so that the amount of data sent to the communication network can be reduced rather than distributing information uniformly without specifying the user terminal 202. It leads to reduction.

In step S1201, the information processing apparatus 100 may acquire cluster information (for example, cluster information 800) indicating a classification result obtained by classifying a plurality of member users according to a purchase tendency of products.

<Specific processing procedure of browsing tendency identification processing>
Next, a specific processing procedure of the browsing tendency specifying process in step S1203 shown in FIG. 12 will be described.

FIG. 13 is a flowchart illustrating an example of a specific processing procedure of the browsing tendency specifying process. In the flowchart of FIG. 13, first, the information processing apparatus 100 acquires browsing history information of each member user belonging to the target cluster C from the browsing history DB 230 (step S1301).

Next, the information processing apparatus 100 refers to the obtained browsing history information, extracts keywords from the URL of the web page browsed by each member user belonging to the target cluster C (step S1302), and appears for each keyword. The number is calculated (step S1303). The number of appearing users is the number of member users who have a browsing history of web pages with URLs including keywords.

Next, the information processing apparatus 100 calculates the appearance frequency for each keyword by dividing the calculated number of appearance users for each keyword by the total number of member users belonging to the target cluster C (step S1304). Then, the information processing apparatus 100 specifies the top N keywords with the highest appearance frequency as the browsing tendency of the web pages of the member users belonging to the target cluster C (step S1305), and calls the browsing tendency specifying process. Return to step.

Thereby, keywords (for example, directory names and file names) that frequently appear in URLs of web pages browsed by member users belonging to the cluster C can be specified as the browsing tendency of the cluster C.

In step S1301, the information processing apparatus 100 acquires the browsing history information of each member user by making an inquiry to the SSP server 204 using the cookie information of the user terminal 202 of each member user belonging to the target cluster C. It may be. In step S1302, the information processing apparatus 100 may extract a keyword embedded as a meta tag in a web page browsed by each member user belonging to the target cluster C.

(Delivery request side support processing procedure of DSP server 203)
Next, the distribution request side support processing procedure of the DSP server 203 will be described.

FIG. 14 is a flowchart showing an example of a delivery request side support processing procedure of the DSP server 203. In the flowchart of FIG. 14, first, the DSP server 203 determines whether an advertisement distribution request has been received from the advertisement request side apparatus 201 or the information processing apparatus 100 (step S1401). The advertisement distribution request includes, for example, bid conditions including cookie information of the user terminal 202 as a distribution destination and advertisement information of the target product.

Here, the DSP server 203 waits to receive an advertisement distribution request (step S1401: No). When the DSP server 203 receives an advertisement distribution request (step S1401: Yes), the DSP server 203 sets bid conditions for the advertisement included in the received advertisement distribution request (step S1402).

Next, the DSP server 203 determines whether or not a bid request has been received from the SSP server 204 (step S1403). The bid request includes, for example, cookie information of the user terminal 202 that displays the web page, an advertisement space ID, and the like. Here, the DSP server 203 waits to receive a bid request (step S1403: No).

Then, when the DSP server 203 receives the bid request (step S1403: Yes), the DSP server 203 determines whether or not the received bid request satisfies the bid condition of the set advertisement (step S1404). Specifically, for example, when the cookie information of the user terminal 202 included in the bid request does not match the cookie information of the user terminal 202 included in the bid condition, the DSP server 203 does not satisfy the bid condition. Judge.

Here, when the bid condition for the advertisement is not satisfied (step S1404: No), the DSP server 203 ends the series of processes according to this flowchart.

On the other hand, when the bid condition of the advertisement is satisfied (step S1404: Yes), the DSP server 203 transmits a bid response to the SSP server 204 (step S1405). The bid response includes, for example, a bid price. Then, the DSP server 203 determines whether or not a result notification has been received from the SSP server 204 (step S1406).

Here, the DSP server 203 waits for reception of the result notification (step S1406: No), and when the result notification is received (step S1406: Yes), whether the received result notification is a victory notification or not. Judgment is made (step S1407). Here, when it is not a victory notification (step S1407: No), the DSP server 203 ends a series of processes according to this flowchart.

On the other hand, when the notification is a victory notification (step S1407: Yes), the DSP server 203 determines whether an advertisement request is received from the user terminal 202 (step S1408). Here, the DSP server 203 waits to receive an advertisement request from the user terminal 202 (step S1408: No).

When the DSP server 203 receives the advertisement request (step S1408: Yes), the DSP server 203 distributes the advertisement information to be embedded in a predetermined area of the web page to the user terminal 202 (step S1409). Terminate the process. The predetermined area is, for example, an area in the web page identified from the advertisement ID included in the bid request.

Thus, for example, it is possible to distribute advertisements for the same product only to users who have similar hobbies and preferences to member users who have purchased a product.

As described above, according to the information processing apparatus 100 according to the embodiment, a plurality of member users can be classified into the cluster C corresponding to the purchase tendency of products with reference to the purchase history DB 220. Further, according to the information processing apparatus 100, the browsing tendency of the web pages of the member users belonging to the cluster C can be specified based on the browsing history information of the web pages of the member users belonging to the classified cluster C. Then, according to the information processing apparatus 100, it is possible to output the identification information of the user terminal 202 from which the browsing history of the web page corresponding to the browsing tendency of the identified cluster C is detected.

Thereby, the user terminal 202 of the other user who is similar in the browsing tendency of the web page with the member user belonging to the cluster C classified according to the purchase tendency of the product, that is, the member user and the hobby preference is similar is specified. Can do. As a result, advertisement distribution can be performed for members who belong to the cluster C and have similar hobbies and preferences. In other words, it is possible to distribute advertisements for products and services purchased by the member user to other users who have similar hobbies and preferences to the member user, and the advertisement effect can be improved. From another aspect, it is possible to specify the user terminal 202 to which information is to be distributed, so that the amount of data sent to the communication network can be reduced rather than distributing information uniformly without specifying the user terminal 202. It leads to reduction.

Further, according to the information processing apparatus 100, it is possible to transmit an advertisement distribution request to the user terminal 202 from which the browsing history of the web page corresponding to the browsing tendency of the identified cluster C is detected, to the DSP server 203. Thereby, it is possible to automatically make an advertisement distribution request to users who have similar hobbies and preferences to member users belonging to the cluster C.

Further, according to the information processing apparatus 100, the browsing date before the latest purchase date included in the purchase history information of each member user among the browsing history information of the web page of each member user belonging to the cluster C. The browsing history information of the page can be acquired. The latest purchase date and time is, for example, the latest purchase date and time when the target product set according to the cluster C is purchased. Specifically, for example, the information processing apparatus 100 browses web page browsing history information whose browsing date and time is included in a predetermined period (for example, a period of the latest few weeks) before the latest purchase date and time when the target product is purchased. May be obtained. Then, according to the information processing apparatus 100, the browsing tendency of the web pages of member users belonging to the cluster C can be specified based on the acquired browsing history information.

Thereby, the browsing tendency of the cluster C is specified based on the browsing history information of the web page browsed in the last few weeks before the latest purchase date and time when each member user purchased the target product. be able to. As a result, for example, it is possible to specify other users who are in the same psychological state as the member user immediately before purchasing the target product and perform advertisement distribution of the target product, thereby maximizing the advertising effect. it can.

Moreover, according to the information processing apparatus 100, the browsing tendency of the cluster C can be identified based on the browsing history information of the member users who belong to the cluster C and have a relatively high degree of fitness for the cluster C. . Thereby, the browsing tendency of the web page according to the characteristics of the cluster C can be specified, and as a result, the target of advertisement distribution can be appropriately narrowed down.

Further, according to the information processing apparatus 100, the browsing tendency of the cluster C can be specified based on the keyword extracted from the URL included in the browsing history information of the member users belonging to the cluster C. Thereby, keywords (for example, directory names and file names) that frequently appear in URLs of web pages browsed by member users belonging to the cluster C can be specified as the browsing tendency of the cluster C.

Further, according to the information processing apparatus 100, the browsing tendency of the cluster C can be specified based on the keyword obtained from the metadata of the web page indicated by the URL included in the browsing history information of the member users belonging to the cluster C. . As a result, keywords that frequently appear in the metadata (meta tag) of web pages browsed by member users belonging to the cluster C can be identified as the browsing tendency of the cluster C.

Further, according to the information processing apparatus 100, among the user terminals 202 in which the browsing history of the web page corresponding to the browsing tendency of the cluster C is detected, the user terminal 202 having a relatively high degree of fitness with respect to the browsing tendency of the cluster C. Identification information can be output. Thereby, the user terminal 202 of the other user according to the browsing tendency of the member user belonging to the cluster C, that is, the user according to the characteristics of the cluster C, can be specified.

Further, according to the information processing apparatus 100, the degree of fitness of the user terminal 202 with respect to the browsing tendency of the cluster C in association with the identification information of the user terminal 202 in which the browsing history of the web page corresponding to the browsing tendency of the cluster C is detected. Can be output. As a result, the user terminal 202 of another user that matches the characteristics of the cluster C can be specified, and the distribution destination of the advertisement for the target product can be easily selected.

Note that the information processing method described in this embodiment can be realized by executing a program prepared in advance on a computer such as a personal computer or a workstation. This information processing program is recorded on a computer-readable recording medium such as a hard disk, flexible disk, CD-ROM, MO (Magneto-Optical disk), DVD (Digital Versatile Disk), USB (Universal Serial Bus) memory, etc. It is executed by being read from the recording medium by a computer. The information processing program may be distributed via a network such as the Internet.

DESCRIPTION OF SYMBOLS 100 Information processing apparatus 110,500-1 to 500-3 Purchase history information 120,600-1 to 600-3 Browsing history information 130

Browsing history information

140,1100 Distribution destination information 200 Information distribution system 201 Advertisement request side apparatus 202 User terminal 203 DSP server 204 SSP server 220 Purchase history DB
230 browsing history DB
701 Acquisition unit 702 Classification unit 703 Identification unit 704 Detection unit 705 Output unit 800 Cluster information 900 Keyword list 1000 Conformance list

Claims

Based on the purchase history information of products associated with each of a plurality of member users, classify the plurality of member users into clusters according to the purchase tendency of the products,
Based on the browsing history information of the web page associated with each of the member users belonging to the classified first cluster, the browsing tendency of the web page of the member user belonging to the first cluster is specified,
Outputting identification information of a user terminal from which a browsing history of a web page corresponding to the identified browsing tendency is detected;
An information processing apparatus having a control unit.
The controller is
The information processing apparatus according to claim 1, wherein an information distribution request is transmitted to a user terminal in which a browsing history of a web page corresponding to the identified browsing tendency is detected.
The controller is
Based on the browsing history information of the web page before the latest purchase date and time included in the purchase history information of the product, the browsing tendency of the web page of the member user belonging to the first cluster is specified, The information processing apparatus according to claim 1 or 2.
The latest purchase date and time is the latest purchase date and time when the target product set according to the first cluster is purchased,
The controller is
The browsing tendency of the web pages of member users belonging to the first cluster is specified based on browsing history information of web pages within a predetermined period before the latest purchase date and time when the target product is purchased. The information processing apparatus according to claim 3.
The controller is
Identifying the browsing tendency based on browsing history information of a web page associated with a member user having a relatively high degree of fitness for the first cluster among the member users belonging to the first cluster; The information processing apparatus according to any one of claims 1 to 4, wherein:
The controller is
The browsing tendency is specified based on a keyword extracted from a URL (Uniform Resource Locator) included in browsing history information of a web page associated with each of the member users belonging to the first cluster. The information processing apparatus according to any one of claims 1 to 5.
The controller is
The information processing according to any one of claims 1 to 6, wherein the browsing tendency is specified based on a keyword obtained from metadata of a web page indicated by a URL included in the browsing history information. apparatus.
The controller is
8. The identification information of a user terminal having a relatively high degree of suitability for the browsing tendency among user terminals from which a browsing history of a web page corresponding to the browsing tendency is detected is output. The information processing apparatus according to any one of the above.
The controller is
9. The information processing apparatus according to claim 1, wherein information indicating the degree of suitability of the user terminal with respect to the browsing tendency is output in association with the identification information of the user terminal.
Based on the purchase history information of the products associated with each of the plurality of users, the plurality of users are classified into clusters according to the purchase tendency of the products,
Based on the cookie information stored in the terminal device of the user belonging to the classified first cluster, to obtain browsing history information of the web page of the user belonging to the first cluster,
Based on the obtained browsing history information, identify the browsing tendency of the user's web page belonging to the first cluster,
Outputting the identified browsing tendency,
An information processing apparatus having a control unit.
A classification unit that classifies the plurality of member users into clusters according to purchase trends of products based on purchase history information of products associated with each of the plurality of member users;
Based on the browsing history information of the web pages associated with each of the member users belonging to the first cluster classified by the classification unit, the browsing tendency of the web pages of the member users belonging to the first cluster is specified. A specific part,
An output unit that outputs identification information of a user terminal in which a browsing history of a web page corresponding to the browsing tendency specified by the specifying unit is detected;
A distribution unit that distributes predetermined information embedded in a predetermined region of a web page to a user terminal of the identification information output by the output unit;
An information distribution system comprising:
Computer
Based on the purchase history information of products associated with each of a plurality of member users, classify the plurality of member users into clusters according to the purchase tendency of the products,
Based on the browsing history information of the web page associated with each of the member users belonging to the classified first cluster, the browsing tendency of the web page of the member user belonging to the first cluster is specified,
Outputting identification information of a user terminal from which a browsing history of a web page corresponding to the identified browsing tendency is detected;
An information processing method characterized by executing processing.
Computer
Based on the purchase history information of the products associated with each of the plurality of users, the plurality of users are classified into clusters according to the purchase tendency of the products,
Based on the cookie information stored in the terminal device of the user belonging to the classified first cluster, to obtain browsing history information of the web page of the user belonging to the first cluster,
Based on the obtained browsing history information, identify the browsing tendency of the user's web page belonging to the first cluster,
Outputting the identified browsing tendency,
An information processing method characterized by executing processing.
On the computer,
Based on the purchase history information of products associated with each of a plurality of member users, classify the plurality of member users into clusters according to the purchase tendency of the products,
Based on the browsing history information of the web page associated with each of the member users belonging to the classified first cluster, the browsing tendency of the web page of the member user belonging to the first cluster is specified,
Outputting identification information of a user terminal from which a browsing history of a web page corresponding to the identified browsing tendency is detected;
An information processing program for executing a process.
On the computer,
Based on the purchase history information of the products associated with each of the plurality of users, the plurality of users are classified into clusters according to the purchase tendency of the products,
Based on the cookie information stored in the terminal device of the user belonging to the classified first cluster, to obtain browsing history information of the web page of the user belonging to the first cluster,
Based on the obtained browsing history information, identify the browsing tendency of the user's web page belonging to the first cluster,
Outputting the identified browsing tendency,
An information processing program for executing a process.