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

Abstract

To properly determine a sales page of a commodity to be recommended to a user.SOLUTION: An information processing apparatus acquires a list of sales page identification information identifying each sales page for one or more commodities associated with each user, converts the list of sales page identification information into a list of commodity identification information identifying a commodity, generates a recommended commodity list including recommended commodity identification information identifying each of one or more commodities to be recommended to the user, based on the list of commodity identification information, determines a sales page for each of the one or more commodities to be recommended to the user, based on one or more pieces of recommended commodity identification information included in the recommended commodity list.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a technique for determining a sales page of a product to be recommended to a user.

Background Art

[0002] In recent years, electronic commerce (EC) that sells products online using the Internet has been actively carried out. Such EC is implemented, for example, via an EC site such as an EC mall (mall-type EC site) where multiple stores develop a shopping mall online. By accessing the EC site from a mobile terminal such as a PC (Personal Computer) or a smartphone, a user can view and purchase desired products without actually visiting multiple stores and without worrying about time.

[0003] For the purpose of promoting sales on an EC site, a technique has been proposed to identify products that a user is interested in from the action history of the user on the sales page of the product, such as the purchase history and browsing history of the user, and to determine products to be recommended (recommended) to the user based on the identified products. For example, Patent Document 1 discloses determining, as recommended products, a second product that has a predetermined relationship with a first product identified based on the purchase history of the user.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] When determining recommended products for a user based on the user's action history on sales pages, it is important to appropriately associate action information (action information) with product identification information (product identification information) in order to identify products that the user is interested in. In an e-commerce mall, when the same product is sold at multiple stores, the same product is displayed on different sales pages for each store and can be sold. Even if it is the same product, when it is sold on different sales pages, there is a possibility that the action information may not be appropriately associated with the product identification information. That is, the action information of the user on the different sales pages is not associated with the same product identification information but is associated with different product identification information, and as a result, there is a possibility that the action information may not be appropriately associated with the product identification information. As a result, there was a risk that the sales pages of the products recommended to the user could not be determined appropriately.

[0006] The present invention has been made in view of the above problems, and an object thereof is to provide a technique for appropriately determining the sales pages of products recommended to users.

Means for Solving the Problems

[0007] In order to solve the above problems, one aspect of the information processing apparatus according to the present invention includes an acquisition unit that acquires a list of sales page identification information for identifying each of the sales pages of one or more products associated with each user, a conversion unit that converts the list of sales page identification information into a list of product identification information for identifying products, a generation unit that generates a recommended product list including recommended product identification information for identifying each of one or more products recommended to each user based on the list of product identification information, and a determination unit that determines the sales pages of each of one or more products recommended to each user based on one or more pieces of the recommended product identification information included in the recommended product list.

[0008] In order to solve the above problems, one aspect of the information processing method according to the present invention includes: an acquisition step of acquiring a list of sales page identification information for identifying each of one or more sales pages of products associated with each user; a conversion step of converting the list of sales page identification information into a list of product identification information for identifying products; a generation step of generating a recommended product list including recommended product identification information for identifying each of one or more products recommended to each user based on the list of product identification information; and a determination step of determining a sales page for each of one or more products recommended to each user based on one or more pieces of the recommended product identification information included in the recommended product list.

[0009] In order to solve the above problems, one aspect of the information processing program according to the present invention is for causing a computer to execute: an acquisition process of acquiring a list of sales page identification information for identifying each of one or more sales pages of products associated with each user; a conversion process of converting the list of sales page identification information into a list of product identification information for identifying products; a generation process of generating a recommended product list including recommended product identification information for identifying each of one or more products recommended to each user based on the list of product identification information; and a determination process of determining a sales page for each of one or more products recommended to each user based on one or more pieces of the recommended product identification information included in the recommended product list.

Advantages of the Invention

[0010] According to the present invention, it is possible to appropriately determine a sales page for a product recommended to a user. Those skilled in the art will be able to understand the above-described objects, aspects, and effects of the present invention, as well as the objects, aspects, and effects of the present invention not described above, from the following embodiments for carrying out the invention with reference to the descriptions in the accompanying drawings and the claims.

Brief Description of the Drawings

[0011]

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DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments for implementing the present invention will be described in detail with reference to the accompanying drawings. Among the components disclosed below, those having the same function are denoted by the same reference numerals, and the description thereof will be omitted. Note that the embodiments disclosed below are examples of means for realizing the present invention, and should be appropriately modified or changed depending on the configuration of the apparatus to which the present invention is applied and various conditions. The present invention is not limited to the following embodiments. Also, not all combinations of features described in this embodiment are essential for the solution means of the present invention.

[0013] <First Embodiment> [Configuration of Information Processing System] FIG. 1 shows a configuration example of an information processing system 1 according to this embodiment. The information processing system 1 includes an information processing apparatus 10, an EC site server 11, and a user apparatus 12. The information processing apparatus 10, the EC site server 11, and the user apparatus 12 are configured to be communicable with each other via a network 13. The network 13 can include, in addition to the Internet, an intranet, a LAN (Local Area Network), a WAN (Wide Area Network), a mobile communication network, and the like. In FIG. 1, one user apparatus 12 is illustrated, but the information processing system 1 is configured to have a plurality of user apparatuses having the same function as the user apparatus 12. In the present disclosure, the plurality of user apparatuses are collectively referred to as the user apparatus 12. Also, the user apparatus 12 is operated by a user 14. In the present disclosure, the terms user apparatus and user may be understood synonymously.

[0014] The EC site server 11 is a server device that operates an EC mall (mall-type EC site) where multiple stores deploy a shopping mall online. Specifically, the EC site server 11 operates an EC mall that deploys a shopping mall via sales pages (web pages) of products sold by multiple stores (merchants). The EC site server 11 accepts access from the user device 12 via the network 13 and can provide various services related to shopping in the EC mall to the user 14. For example, when the user accesses the EC mall and performs actions such as purchasing or viewing on the sales page of an arbitrary product, the EC site server 11 provides services related to the product to the user 14. In addition, the EC site server 11 can acquire (collect) and manage information about the sales pages of the products on which the user 14 has performed actions in the EC mall. Note that the EC site server 11 is not limited to a server device and may be realized by a mainframe or the like.

[0015] The user device 12 is operated by the user 14, can access the EC site server 11, and can receive various services in the EC mall provided by the EC site server 11. For example, the user 14 can operate the user device 12 to access the EC mall and view or purchase the sales pages of various products provided in the EC mall. When using the services in the EC mall, the user 14 registers information about the user 14 (hereinafter also referred to as user attributes). For example, the user 14 sets a user ID (user identification information) that identifies the user 14 and is associated with the user attributes of the user 14, logs in using the user ID, and uses the services in the EC mall. By setting the user ID, the user 14 can use the services of the EC mall even from a user device different from the user device 12 connected to the network 13.

[0016] User attributes include the factual attributes of the user. The factual attributes of the user include the IP address of the user device, the user's address, name, the number of the credit card held by the user, the user's demographic information (demographic user attributes such as gender, age, residential area, occupation, family composition, etc.), and the like. In addition, the factual attributes of the user may include the registration number and registration name when using web services including the e-commerce mall (web services related to the e-commerce mall). Further, the factual attributes of the user may include the usage history, search history, product purchase history (including purchase results), and information related to points that can be saved by using the service of web services including the e-commerce mall. Thus, the factual attributes of the user can include any information related to the user device or the user himself / herself, and information related to the use of web services including the e-commerce mall. In addition, the user attributes may include the estimated attributes of the user. The estimated attributes of the user can be estimated based on the factual attributes of the user, for example, by a learned user attribute estimation model. The estimated attributes of the user may include the preferences for interesting products and lifestyle.

[0017] The user device 12 is a device such as a smartphone or a tablet, and is configured to be communicable with the EC site server 11 and the information processing device 10 via the network 13. The user device 12 has a display unit (display surface) such as a liquid crystal display, and the user 14 can perform various operations through the GUI (Graphic User Interface) equipped on the display unit. The operations include various operations on the content such as images displayed on the screen, such as tap operations, slide operations, and scroll operations using a finger, a stylus, or the like. The user device 12 may separately include a display unit.

[0018] The information processing device 10 acquires information regarding the sales pages of products collected by the EC site server 11, and determines the sales pages of products to be recommended to the user 14 based on the information. Specifically, the information processing device 10 first acquires a list of sales page IDs that identify the sales pages of products associated with the user 14, and converts the list of sales page IDs into a list of product IDs that identify the products. Subsequently, the information processing device 10 generates a recommended product list including recommended product IDs that identify the products to be recommended to the user 14 based on the list of product IDs, and determines the sales pages of the products to be recommended to the user 14 based on the recommended product IDs. The information of the determined sales pages of the products is provided to the EC site server 11, and the EC site server 11 can provide the sales pages to the user device 12 according to the information. Note that in FIG. 1, the information processing device 10 and the EC site server 11 are configured as separate devices, but the information processing device 10 may be configured to include the functions of the EC site server 11.

[0019] FIG. 13A shows an example of a screen 1300 including a plurality of sales pages in the EC mall provided by the EC site server 11. Each of the plurality of sales pages is managed and operated by a different store (merchant). Therefore, each of the plurality of sales pages is associated with the store of the product sold on the sales page. In the screen 1300, each sales page includes an image regarding the product to be sold and information (description, price, etc.) regarding the product. Taking the sales page 1302 as an example, the sales page 1302 is a sales page for selling the product 1301. Specifically, the product 1301 is an image of the product sold on the sales page 1302, but in the present disclosure, the image is referred to as a product. Also, the image on the sales page may be an image that explains the product to be sold.

[0020] [Functional Configuration of Information Processing Device] FIG. 2 shows an example of the functional configuration of the information processing apparatus 10 according to the present embodiment. As an example of its functional configuration, the information processing apparatus 10 includes a sales page ID (sales page identification information) acquisition unit 201, a product ID (product identification information) list generation unit 202, a recommended product list generation unit 203, a sales page determination unit 204, a table storage unit 210, and a learning model storage unit 220.

[0021] The table storage unit 210 is configured to be able to store a product conversion table 211 for converting a sales page ID into a product ID. The product conversion table 211 is a table that associates a product ID for identifying a product sold by an EC mall with a sales page ID for identifying one or more sales pages for selling the product. The same product ID is assigned to the same product. Even for the same product, since it can be sold on different sales pages, in the product conversion table 211, different (multiple) sales page IDs may be associated with one product ID. In the present embodiment, it is assumed that multiple product IDs are not associated with one sales page ID.

[0022] The learning model storage unit 220 is configured to be able to store the learned feature vector extraction model 221. The feature vector extraction model 221 is a machine learning model that extracts (derives) the vector representation of the user with the user ID based on the past actions associated with the user ID. Specifically, the feature vector extraction model 221 is configured to output the vector representation of the user with the user ID by taking as input a list of product IDs of the products on which the user with the user ID has taken actions in the past (for example, information indicating the list). The vector representation corresponds to the vectorized representation of the product IDs in which the user has shown interest. The feature vector extraction model 221 is configured based on MF (Matrix Factorization) or Natural Language Processing (NLP). The MF-based model is a model that decomposes an evaluation value matrix composed of m (number of rows) × n (number of columns) (where m and n are integers of 2 or more) into two matrices by reducing the dimension through collaborative filtering. Also, the NLP-based model is a natural language processing model that converts words into vector representations, such as Word2Vec. The feature vector extraction model 221 is relearned at any time by a processing unit (not shown) in the information processing apparatus 10 or an external apparatus.

[0023] Note that the entire information processing apparatus 10 may not be provided in one apparatus, and the information processing apparatus 10 may be divided and provided in a plurality of apparatuses. For example, a part of the information processing apparatus 10 may be provided in an external server apparatus such as an EC site server 11. In this case, the functions shown in the present embodiment are realized by the cooperation between the information processing apparatus 10 and the external server apparatus.

[0024] FIG. 2 also shows an example of the functional configuration of the EC site server 11 related to the present embodiment. The EC site server 11 is connected to the information processing apparatus 10 via the network 13. In FIG. 2, as the functional configuration related to the present embodiment, the EC site server 11 has an EC mall operation unit 231, an evaluation index derivation unit 232, a sales page ID database 240, and an evaluation index database 250.

[0025] The EC mall operation unit 231 operates an EC mall in which a plurality of stores sell products online. For example, the EC mall operation unit 231 can create a plurality of sales pages based on product information provided by a plurality of stores and provide them to the user device 12 connected via the network 13. Further, the EC mall operation unit 231 can provide a service corresponding to the type of action on the product sales page from the user device 12 to the user device 12. Further, the EC mall operation unit 231 can provide the sales page instructed by the information processing device 10 to the user device 12. The EC mall operation unit 231 stores the sales page ID associated with the user ID in the sales page ID database 240 in response to a predetermined action being performed on the product sales page by the user device 12. An example of the sales page ID will be described later with reference to FIG. 3. The evaluation index derivation unit 241 derives evaluation indexes (marketing indexes) associated with the sales pages in the EC mall provided by the EC mall operation unit 231. The evaluation index represents an evaluation index for the products sold on the sales page and the stores (merchants) selling the products, and may include the CVR (conversion rate) for the sales page. In this embodiment, the conversion in CVR assumes the purchase of a product, but in other embodiments, it may be a click on a product. Further, the evaluation index may include the evaluation points given by the user to the sales page and the ranking (rank in the ranking) of the evaluation points. The evaluation points may include those obtained by quantifying the evaluation, comment, and recommendation degree of at least any one of the sales page, store, and product by the user.

[0026] Each time a predetermined action is performed on the product sales page in the EC mall by a user, the EC mall operation unit 231 adds the sales page ID to the list of sales page IDs stored in the sales page ID database 240 in chronological order. That is, each time the predetermined action is performed, a list of sales page IDs is generated. The list of sales page IDs stored in the sales page ID database 240 is hereinafter referred to as the chronological sales page ID list. FIG. 3 shows an example of the chronological sales page ID list 30 stored in the sales page ID database 240. The chronological sales page ID list 30 is a list of sales page IDs collected each time a predetermined action is performed by 3 users on the sales pages of multiple products in the EC mall. As an example, the chronological sales page ID list 30 is configured such that each column includes components of user ID 301, sales page ID 302, action type 303, action date and time 304, and genre ID (genre identification information) 305 for identification. The genre ID 305 is the genre of the product sold on the sales page identified by the sales page ID 302 and is associated with each product page.

[0027] The user ID 301 is information that identifies each of the three users, and is any one of UID_1, UID_2, and UID_3. Each UID of the user ID 301 is associated with user attributes. For example, UID_1 is associated with the user attributes registered when the user identified by UID_1 uses the service at 0 in the EC mall. The sales page ID 302 is information that identifies a sales page (web page). The sales page ID 302 is, as an example, composed of a character string that identifies the sales page of a product, and may be a character string as shown in FIG. 3, or may be a URL (Uniform Resource Locator). The action type 303 indicates the type of a predetermined action with respect to the sales page of a product. In the present embodiment, the predetermined action is any one of the actions of "viewing", "bookmarking (adding to bookmarks)", and "purchasing". "Viewing" corresponds to an action of accessing (visiting) the sales page and viewing the sales page. "Bookmarking" corresponds to an action of online marking and registering the product sold on the sales page. "Purchasing" corresponds to an action of purchasing the product sold on the sales page. Note that when the "purchasing" action is performed within a predetermined time from the "viewing" action, only the "purchasing" action may be registered. The action date and time 304 is information indicating the date and time when the action indicated by the action type 303 is performed. The list 30 of chronological sales page IDs is configured such that new components are at the top of the list according to the date and time. The genre ID 305 is information (genre identification information) that identifies the genre of the product sold on the sales page identified by the sales page ID 302. The genre ID 305 is used in the second embodiment.

[0028] Referring to FIG. 13B, the actions of "viewing", "adding to favorites", and "purchasing" for the product sold on the sales page will be described. FIG. 13B shows an example of a screen 1310 including a sales page 1303 that is displayed when the sales page 1302 in FIG. 13A is selected (activated) and accessed. The sales page 1302 in FIG. 13A and the sales page 1303 in FIG. 13B are both sales pages for selling the product 1301. The sales page 1302 and the sales page 1303 are examples of sales pages for selling the product 1301, and they may be configured with other designs or displayed on the screen in different display methods. "Viewing" corresponds to the action of accessing the sales page 1303 and viewing the information about the product 1301. "Adding to favorites" corresponds to the action of selecting (activating) the favorite button 1304 to mark and register the product 1301 online on the screen 1310. "Purchasing" corresponds to the action of selecting (activating) the cart button (the button indicating the shopping cart) 1305 to purchase the product 1301 on the screen 1310 and then proceeding with the purchase procedure. Note that in this embodiment, as the action types, any of the three actions of "purchasing", "viewing", and "adding to favorites" are assumed, but it is not limited thereto. For example, the access types may include other access types such as searching for or clicking on the product sales page.

[0029] In the information processing apparatus 10, the sales page ID acquisition unit 201 acquires a list of sales page IDs for each user ID from a list of time-series sales page IDs stored in the sales page ID database 240 in the EC site server 11. The product ID list generation unit 202 generates a list of product IDs by converting the sales page IDs included in the list of sales page IDs for each user ID acquired by the sales page ID acquisition unit 201 into product IDs. The recommended product list generation unit 203 determines one or more recommended product IDs for each user ID based on the list of product IDs generated by the product ID list generation unit 202, and generates a list of recommended product IDs. The sales page determination unit 204 determines a sales page for the recommended product (hereinafter also referred to as a recommended sales page) for each user ID based on the list of recommended product IDs generated by the recommended product list generation unit 203. Specific processing by the sales page ID acquisition unit 201, the product ID list generation unit 202, the recommended product list generation unit 203, and the sales page determination unit 204 will be described later.

[0030] [Hardware Configuration of Information Processing Apparatus] Next, an example of the hardware configuration of the information processing apparatus 10 will be described. FIG. 4 is a block diagram showing an example of the hardware configuration of the information processing apparatus 10 according to the present embodiment. The information processing apparatus 10 according to the present embodiment can be implemented on a single or multiple, any computer, mobile device, or other processing platform. Referring to FIG. 4, an example in which the information processing apparatus 10 is implemented on a single computer is shown, but the information processing apparatus 10 according to the present embodiment may be implemented in a computer system including a plurality of computers. The plurality of computers may be connected to be communicable with each other via a wired or wireless network.

[0031] As shown in FIG. 4, the information processing apparatus 10 may include a CPU (Central Processing Unit) 401, a ROM (Read Only Memory) 402, a RAM (Random Access Memory) 403, an HDD (Hard Disk Drive) 404, an input unit 405, a display unit 406, a communication I / F (communication unit) (interface) 407, and a system bus 408. The information processing apparatus 10 may also include an external memory. The CPU 401 comprehensively controls the operations in the information processing apparatus 10 and controls each component (402 to 407) via the system bus 408 which is a data transmission path.

[0032] The ROM 402 is a non-volatile memory that stores control programs and the like necessary for the CPU 401 to execute processing. The program includes instructions (codes) for executing the processing according to the above-described embodiment. Note that the program may be stored in a non-volatile memory such as the HDD 404 or an SSD (Solid State Drive), or an external memory such as a removable storage medium (not shown). The RAM 403 is a volatile memory and functions as the main memory, work area, etc. of the CPU 401. That is, when executing processing, the CPU 401 loads necessary programs and the like from the ROM 402 into the RAM 403 and realizes various functional operations by executing the programs and the like. The RAM 403 may include the table storage unit 210 and the learning model storage unit 220 shown in FIG. 2.

[0033] The HDD 404 stores various data and various information necessary when the CPU 401 performs processing using a program, for example. Also, the HDD 404 stores various data and various information obtained when the CPU 401 performs processing using a program or the like, for example. The input unit 405 is composed of a pointing device such as a keyboard or a mouse. The display unit 406 is composed of a monitor such as a liquid crystal display (LCD). The display unit 406 may function as a GUI (Graphical User Interface) when configured in combination with the input unit 405.

[0034] The communication I / F 407 is an interface that controls communication between the information processing apparatus 10 and an external device. The communication I / F 407 provides an interface with a network and executes communication with an external device via the network. Various data, various parameters, etc. are transmitted and received between the external device via the communication I / F 407. In the present embodiment, the communication I / F 407 may execute communication via a wired LAN (Local Area Network) or a dedicated line compliant with a communication standard such as Ethernet (registered trademark). However, the network available in the present embodiment is not limited to this, and it may be configured by a wireless network. This wireless network includes a wireless PAN (Personal Area Network) such as Bluetooth (registered trademark), ZigBee (registered trademark), and UWB (Ultra Wide Band). Further, it includes a wireless LAN (Local Area Network) such as Wi-Fi (Wireless Fidelity) (registered trademark) and a wireless MAN (Metropolitan Area Network) such as WiMAX (registered trademark). Furthermore, it includes a wireless WAN (Wide Area Network) such as 4G and 5G. Note that the network may connect each device so that communication is possible, and as long as communication is possible, the communication standard, scale, and configuration are not limited to the above.

[0035] At least some of the functions of each element of the information processing apparatus 10 shown in FIG. 2 can be realized by the CPU 401 executing a program. However, at least some of the functions of each element of the information processing apparatus 10 shown in FIG. 2 may operate as dedicated hardware. In this case, the dedicated hardware operates based on the control of the CPU 401.

[0036] Note that the hardware configuration of the EC site server 11 is also the same as the configuration shown in FIG. 4. Here, the RAM 403 may include the sales page ID database 240 and the evaluation index database 250 shown in FIG. 2.

[0037] [Flow of Recommended Sales Page Determination Process] Subsequently, the recommended sales page determination process executed by the information processing apparatus 10 according to the present embodiment will be specifically described. FIG. 5 is a flowchart of the process executed by the information processing apparatus 10 according to the present embodiment. The process shown in FIG. 5 can be performed by the CPU 401 executing a control program stored in the information processing apparatus 10.

[0038] In S51, the sales page ID acquisition unit 201 acquires a list of sales page IDs for each user ID from the list of time-series sales page IDs collected by the EC site server 11 and stored in the sales page ID database 240. The list of sales page IDs for each user ID corresponds to a list of sales page IDs that identify one or more sales pages associated with the user identified by the user ID. To distinguish the list of sales page IDs for each user ID acquired by the sales page ID acquisition unit 201 from the time-series sales page IDs stored in the sales page ID database 240, it is also referred to as the list of sales page IDs for each user ID. FIG. 6 shows an example of the list 60 of sales page IDs for each user acquired by the sales page ID acquisition unit 201. The list 60 of sales page IDs for each user is a list generated by sorting for each user ID based on the list 30 of sales page IDs shown in FIG. 3. That is, the list 60 of sales page IDs is configured to include information on the sales page ID 302, action type 303, and action date and time 304 for each user ID 301.

[0039] The list 60 of sales page IDs for each user is a list composed of the sales page IDs within a predetermined past period from a predetermined timing among the list 30 of sales page IDs. The predetermined timing can be the timing when the sales page ID acquisition unit 201 accesses the EC site server 11 to acquire the list of time-series sales page IDs, or the timing arbitrarily set by the sales page ID acquisition unit 201. Instead of this, the list of sales page IDs for each user may be a list including the sales page IDs corresponding to a predetermined number of actions for each user. That is, the sales page ID acquisition unit 201 may generate and acquire the list of sales page IDs for each user so as to include a predetermined number of sales page IDs corresponding to a predetermined number of actions for each user. In this case, the same number of sales page IDs are listed for each user ID. Note that the list 60 of sales page IDs for each user is sorted for each user ID, but the list of sales page IDs for each user may be generated without sorting.

[0040] Next, in S52, the product ID list generation unit 202 converts the sales page IDs included in the sales page ID list acquired by the sales page ID acquisition unit 201 into product IDs and generates a product ID list. In the present embodiment, the product ID list generation unit 202 uses the product conversion table 211 stored in the table storage unit 210 to convert the sales page IDs included in the sales page ID list into product IDs. The product conversion table 211 is a table that associates the product IDs of the products sold by the EC mall with the sales page IDs that identify one or more sales pages for selling the products.

[0041] In the EC mall, the same product may be sold on different sales pages. Therefore, in the product conversion table 211, one product ID may be associated with different sales page IDs. When different sales page IDs are associated with one product ID, the product ID list generation unit 202 converts the different sales page IDs into the same product ID. FIG. 13C shows an example of a screen 1320 where the same product is sold on different sales pages. On the screen 1320, the sales page 1306 and the sales page 1307 are sales pages that sell the same product 1308. In such a case, the product ID list generation unit 202 converts the sales page IDs of the sales page 1306 and the sales page 1307 into the product ID of the product 1308, respectively.

[0042] FIG. 7 shows an example of a list 70 of product IDs for each user ID, which is converted from the list 60 of sales page IDs for each user shown in FIG. 6 by the product ID list generation unit 202. The list 70 of product IDs is configured to include a product ID 701 for each user ID 301. In the list 60 of sales page IDs for each user, it is assumed that the sales page IDs 302 = "abc121", "bcd942", "bcd125", "kyr580" for the user ID 301 = UID_1 correspond to the product IDs = "ABC", "BCD", "BCD", "KYR" according to the product conversion table 211. In such a case, the product ID list generation unit 202 lists the product IDs 701 = "ABC", "BCD", "BCD", "KYR" for the user ID 301 = UID_1. Here, the sales page IDs = "bcd942" and "bcd125" correspond to one product ID = "BCD", but "BCD" is listed for each sales page ID.

[0043] Note that in this embodiment, the product ID list generation unit 202 converts the sales page ID into a product ID by referring to the product conversion table 211, but the conversion process may be performed using other means. For example, the product ID list generation unit 202 may perform the conversion process using a trained machine learning model.

[0044] In S53, the recommended product list generation unit 203 determines, for each user ID, the product ID (recommended product ID) of the product to be recommended based on the list of product IDs generated by the product ID list generation unit 202, and generates a list of recommended product IDs. In the present embodiment, the recommended product list generation unit 203 inputs, to the feature vector extraction model 221, a list of product IDs of products on which the user with the user ID has taken actions in the past (information indicating the list), and obtains the vector representation of the user with the user ID (hereinafter referred to as the user vector representation). Then, the recommended product list generation unit 203 embeds the obtained user vector representation in a common vector space. In the vector space, the vector representations of product IDs sold on the EC site are embedded. The recommended product list generation unit 203 selects (extracts) product IDs with vector representations having a high similarity to the obtained user vector representation on the vector space. As the similarity, for example, cosine similarity is used. Cosine similarity is a measure representing the similarity between two vectors in a common vector space. The recommended product list generation unit 203 can determine that the higher the cosine value (-1 to +1) of the angle formed by the two vectors of the user vector representation and the vector representation of the product ID, the higher the similarity. The recommended product list generation unit 203 calculates the cosine similarity between the obtained user vector representation and the vector representation of an arbitrary product ID on the vector space, and determines a certain number of product IDs with a high cosine similarity (for example, equal to or higher than a predetermined threshold) as recommended product IDs to be included in the list of recommended product IDs.

[0045] Referring to FIG. 7, for example, the recommended product list generation unit 203 inputs a list of product IDs = {ABC, BCD, KYR} for the user with user ID = UID_1 into the feature vector extraction model 221. The feature vector extraction model 221 extracts and outputs the user vector representation of the user with user ID = UID_1 from the input list of product IDs. That is, the recommended product list generation unit 203 converts the list of product IDs into a user vector representation using the feature vector extraction model 221. Subsequently, the recommended product list generation unit 203 determines, in the aforementioned vector space, the product ID corresponding to the vector representation having a high cosine similarity (e.g., higher than a predetermined threshold) with the user vector representation as the recommended product ID. The recommended product list generation unit 203 performs such processing for each user ID and generates a list of recommended product IDs for each user ID.

[0046] FIG. 8 shows an example of a list 80 of recommended product IDs for each user ID generated by such processing. The list 80 of recommended product IDs is configured to include a recommended product ID 801 for each user ID 301. For example, in the list 80 of recommended product IDs, three recommended product IDs, namely, "ABE", "BCP", and "KYQ", are listed as the recommended product ID 801 for the user with user ID = UID_1.

[0047] In S54, the sales page determination unit 204 determines a sales page (recommended sales page) for each user for each of one or more products recommended based on the recommended product IDs included in the recommended product list generated by the recommended product list generation unit 203. The sales page determination unit 204 may determine a plurality of recommended sales pages for each of one or more products recommended for each user, but in this embodiment, a unique (i.e., one) recommended sales page is determined. First, the sales page determination unit 204 converts one or more recommended product IDs included in the recommended product list into one or more sales page IDs by referring to the product conversion table 211. That is, the sales page determination unit 204 uses the configured product conversion table 211 for converting from a sales page ID to a product ID to convert from a recommended product ID to a sales page. The sales page determination unit 204 generates a list of recommended sales page IDs including the converted one or more sales page IDs.

[0048] FIG. 9 shows an example of a list 90 of recommended sales page IDs corresponding to recommended product IDs for each user ID, which is generated based on the list 80 of recommended product IDs shown in FIG. 8. The list 90 of recommended sales page IDs is configured to include recommended product ID 801, sales page ID 901, and recommended sales page ID 902 for each user ID 301. The sales page determination unit 204 identifies one or more sales page IDs (candidates for recommended sales page IDs) for one recommended product ID and determines a unique recommended sales page ID from the identified one or more sales page IDs.

[0049] When there is one sales page ID (a candidate for the recommended sales page ID) for one recommended product ID, that sales page ID is determined as the recommended sales page ID. For example, in the list 90 of recommended sales page IDs, for the recommended product ID 801 = "ABE" of user ID = UID_1, the recommended sales page ID 902 = "abe528" is determined. Also, for the recommended product ID 801 = "BCP" of user ID = UID_1, the recommended sales page ID 902 = "bcp182" is determined, and for the recommended product ID 801 = "KYQ", the recommended sales page ID 902 = "kyq490" is determined. In this case, the sales page determination unit 204 determines the sales pages identified by the recommended sales page IDs = "abe528", "bcp182", and "kyq490" as the recommended sales pages to be recommended (presented) to the user identified by user ID = UID_1.

[0050] On the other hand, when there are multiple sales page IDs for one recommended product ID, one sales page ID selected from the multiple sales page IDs is determined as the recommended sales page ID. For example, in the case of user ID = UID_2, there are three sales page IDs corresponding to the recommended product ID 801 = "CDT". Thus, when there are multiple sales page IDs corresponding to a recommended product ID, the sales page determination unit 204 may determine one recommended sales page ID based on the evaluation indicators associated with the sales pages collected by the EC site server 11 and stored in the evaluation indicator database 250. For example, when using the CVR for the sales page as an evaluation indicator, the sales page determination unit 204 can select the sales page ID with a high CVR as the recommended sales page ID. A store that sells products on the sales page identified by the sales page ID with a high CVR is considered to have a high reliability as a purchase destination. Therefore, determining the recommended sales page ID based on the CVR can lead to an improvement in user usability.

[0051] Also, when there are multiple sales page IDs for one recommended product ID, the sales page determination unit 204 may determine the recommended sales page ID based on evaluation indicators other than CVR. For example, the sales page determination unit 204 may determine the recommended sales page ID based on the evaluation points for the sales page as an evaluation indicator. Alternatively, the sales page determination unit 204 may calculate a recommendation score from the evaluation points and CVR, and determine the recommended sales page ID based on the recommendation score.

[0052] Thus, according to this embodiment, the information processing apparatus 10 converts the information of the sales page of the product associated with each user into the information of the product in the EC mall. That is, the information processing apparatus 10 appropriately associates the information of the sales page on which an action is performed by each user with the information of the product in the EC mall. Then, the information processing apparatus 10 determines a recommended product based on the information of the product, and determines a unique sales page for selling the recommended product. Thereby, it becomes possible to appropriately determine the sales page of the product recommended to each user.

[0053] <Second Embodiment> The information processing apparatus according to the first embodiment determines the sales page of the product recommended to each user without considering the genre (category) of the product. The information processing apparatus according to this embodiment estimates one or more genres in which each user is interested, and determines the recommended sales page by limiting it to the genre. Hereinafter, this embodiment will be described, but the description of the same configuration and features as those of the first embodiment will be omitted.

[0054] FIG. 10 shows an example of the functional configuration of the information processing apparatus 1000 according to the present embodiment. Compared with the information processing apparatus 10 according to the first embodiment described with reference to FIG. 2, the information processing apparatus 1000 is added with a genre determination unit 205 and a target genre database 260. Further, in the learning model storage unit 220, an MF (Matrix Factorization) - based genre prediction model, an MF - based prediction model 222, and a natural language processing (NLP) - based genre prediction model, an NLP - based prediction model 223 are added. Although omitted in FIG. 10, similar to FIG. 2, the information processing apparatus 1000 is connected to the EC site server 11 via the network 13.

[0055] The target genre database 260 stores information (hereinafter referred to as target genre information) regarding a plurality of target genres determined in advance based on a predetermined criterion. The target genre information stored in the target genre database 260 is generated by a processing unit (not shown) in the information processing apparatus 1000 or an external device and is updated as needed. The target genre information can be target genre features representing the features of each of the plurality of target genres and target genre IDs for identifying each of the plurality of target genres.

[0056] The MF-based prediction model 222 is a model that decomposes an evaluation value matrix composed of m (number of rows) × n (number of columns) (where m and n are integers greater than or equal to 2) into two matrices by reducing the dimension through collaborative filtering. In this embodiment, the MF-based prediction model 222 is configured to decompose an evaluation value matrix composed of m user IDs and n genre IDs into a matrix representing user characteristics and a matrix representing genre characteristics. Also, the NLP-based prediction model 223 is a natural language processing model that converts words into vector representations, such as Word2Vec. In this embodiment, the NLP-based prediction model 223 is configured to convert user IDs and genre IDs into user characteristics and genre characteristics. Note that the MF-based prediction model 222 and the NLP-based prediction model 223 are both pre-trained models and can be trained by a learning unit (not shown) in the information processing apparatus 10.

[0057] With reference to FIGS. 11 and 12, the processing executed by the genre determination unit 205 will be described. In this embodiment, the genre determination unit 205 determines the genre IDs of the top 4 genres that the user is interested in for each user (the top 4 genre IDs). FIG. 11 is a flowchart of the genre determination process executed by the genre determination unit 205 according to this embodiment. Also, FIG. 12 is a conceptual diagram showing the data flow for determining the top 4 genre IDs. In this embodiment, the process by which the genre determination unit 205 determines the top 4 genre IDs will be described, but the number of top genre IDs to be determined is not limited to 4.

[0058] In S111, the genre determination unit 205 acquires a list of time-series sales page IDs (refer to FIG. 3) stored in the sales page ID database 240 of the EC site server 11. Subsequently, in S112, the genre determination unit 205 acquires a user ID and a genre ID (refer to FIG. 3) from the list of sales page IDs acquired in S111. As described above, the user ID is associated with the user attributes of the user identified by the user ID. Also, as shown in FIG. 3, the genre ID is an ID that identifies the genre of the product sold on the sales page identified by the corresponding sales page ID 302. In FIG. 12, the user ID and genre ID acquired by the genre determination unit 205 are represented as user ID 1200 and genre ID 1201, respectively. Although user ID 1200 and genre ID 1201 are shown in FIG. 12, the genre determination unit 205 acquires a plurality of user IDs and a plurality of genre IDs by sequentially performing the acquisition process.

[0059] In S113, the genre determination unit 205 inputs the user ID 1200 and the plurality of genre IDs 1201 into the learned genre prediction model, thereby acquiring a user feature 1202 that represents the user's interest and a genre feature 1203 that represents the feature of the genre. The genre prediction model used here is the MF-based prediction model 222 or the NLP-based prediction model 223. The user feature 1202 and the genre feature 1203 can be associated with the user ID 1200 and the genre ID 1201, respectively.

[0060] When using the MF-based prediction model 222, the genre determination unit 205 first generates, as input data, an evaluation value matrix composed of m user IDs and n genre IDs. In the example of FIG. 3, since the number of user IDs is 3 (from UID_1 to UID_3) and the number of genre IDs is 5 (from g_1 to g_5), a 3×5 evaluation value matrix is generated. The value of an element in the evaluation value matrix can be set by referring to the list of time-series sales page IDs acquired in S111. For example, the genre determination unit 205 refers to the list of time-series sales page IDs acquired in S111, sets a predetermined value for the genre IDs corresponding to each user ID, and sets zero (null value) for the genre IDs not corresponding to each user ID. Referring to FIG. 3, the genre determination unit 205 can set the values of the elements with genre IDs g_1, g_3, and g_4 to a predetermined value and the values of the elements with genre IDs g_2 and g_5 to zero in the column of user ID = UID_1. Note that different values may be set for the genre IDs corresponding to each user ID according to the action type 303 in FIG. 3. For example, different values (e.g., the weights 702 shown in FIG. 15 described later) may be set for "purchase", "favorite registration", and "viewing".

[0061] Subsequently, the genre determination unit 205 inputs the generated evaluation value matrix into the MF-based prediction model 222. The MF-based prediction model 222 reduces the dimension of the input evaluation value matrix and generates and outputs two matrices, namely, a matrix representing user features and a matrix representing genre features. The matrix representing the user features and the matrix representing the genre features can be divided into user features 1202 for each user ID and genre features 1203 for each genre ID. The user features 1202 and the genre features 1203 are vector representations embedded in a common vector space and correspond to user embeddings and genre embeddings, respectively.

[0062] On the one hand, when using the NLP-based prediction model 223, the genre determination unit 205 inputs the user ID 1200 and the genre ID 1201 into the NLP-based prediction model 223. The NLP-based prediction model 223 converts each of the user ID and the genre ID into a vector representation and outputs them as user features and genre features. The user features and the genre features are vector representations embedded in a common vector space, corresponding to user embeddings and genre embeddings respectively.

[0063] In S114, the genre determination unit 205 determines the target genre feature 1204 based on the genre feature 1203 and the target genre information stored in the target genre database 260. In this example, in order to finally determine the top 4 genre IDs, the target genre information of 5 or more target genres more than the number of the top genre IDs is used. The genre determination unit 205 filters (selects) the genre feature 1203 based on the target genre information and determines the target genre feature 1204. For example, when the target genre information is 5 target genre IDs, the genre determination unit 205 determines the genre feature 1203 associated with the 5 target genre IDs as the target genre feature 1204.

[0064] In S115, the genre determination unit 205 determines the top 4 genre IDs 1205 based on the user feature 1202 and the target genre feature 1204. The genre determination unit 205 determines the top 4 genre IDs 1205 according to the similarity between the user feature 1202 and the target genre feature 1203. As the similarity, for example, the cosine similarity is used. The cosine similarity is a measure representing the similarity between two vectors in a common vector space as described above. The genre determination unit 205 can determine that the higher the cosine value (-1 to +1) of the angle formed by the two vectors of the user feature 1202 and the target genre feature 1204, the higher the similarity.

[0065] The top 4 genre IDs 1205 for each user determined by the genre determination unit 205 are used to determine the sales page. For example, the top 4 genre IDs 1205 can be transmitted to the sales page ID acquisition unit 201. In this case, when the sales page ID acquisition unit 201 acquires a list of sales page IDs for each user as shown in FIG. 6, genre IDs other than the top 4 genre IDs may be excluded. Alternatively, the top 4 genre IDs 1205 can be transmitted to the sales page determination unit 204. In this case, when the sales page determination unit 204 generates a list of sales page IDs as shown in FIG. 9, genre IDs other than the top 4 genre IDs 1205 may be excluded. Thereby, for each user, the recommended sales page for the products recommended in the genre IDs included in the top 4 genre IDs 1205 is determined.

[0066] As described above, according to the present embodiment, for one or more genres that are estimated to be of more interest to each user, the recommended sales page for the products to be recommended is determined. Thereby, it is possible to appropriately determine the recommended sales page for the products to be recommended to each user by limiting it to one or more genres that match the interests of each user.

[0067] <Third Embodiment> According to the above embodiment, the information processing apparatus determines the sales page of the products to be recommended for each user based on the information collected in response to a predetermined action being performed on the sales page developed in the EC mall by a plurality of users. In this embodiment, a process for determining the sales page to be presented as a default to a user who has accessed (visited) the EC mall for the first time (hereinafter referred to as a new user) will be described. Note that since the process according to the present embodiment can be implemented by any of the information processing apparatuses of the above embodiments, the description will be made with reference to the information processing apparatus 10 shown in FIG. 2 described in the first embodiment.

[0068] The sales page ID acquisition unit 201 acquires a list of time-series sales page IDs collected by the EC site server 11 and stored in the sales page ID database 240. In the present embodiment, without sorting for each user, for example, within a predetermined period (for example, the period from the acquisition time point to a predetermined time before), a list of time-series sales page IDs acquired by the EC site server 11 is acquired. An example of the acquired list of sales page IDs is as shown in FIG. 3. The sales page ID acquisition unit 201 may acquire the list of time-series sales page IDs every predetermined time, or may acquire it at the timing when a new user accesses the EC mall.

[0069] Next, the product ID list generation unit 202 generates a list of product IDs from the list of time-series sales page IDs acquired by the sales page ID acquisition unit 201. The generation procedure of the list of product IDs is as described in the first embodiment and can be performed using the product conversion table 211. In the processing so far, a list of product IDs that identify products corresponding to the sales pages on which a plurality of users have performed actions within a predetermined period is generated.

[0070] The sales page determination unit 204 determines a recommended sales page for each of one or more products recommended to a new user based on the product IDs included in the list of product IDs generated by the product ID list generation unit 202. For example, the sales page determination unit 204 converts the product IDs included in the list of product IDs into one or more sales page IDs using the product conversion table 211, and generates a list of recommended sales page IDs for the new user. Then, the sales page determination unit 204 determines the sales page identified by the recommended sales page ID included in the list of recommended sales page IDs as the sales page to be recommended (presented) to the new user.

[0071] When the list of chronological sales page IDs is acquired by the sales page ID acquisition unit 201 at predetermined time intervals, the sales page determination unit 204 sequentially generates a list of recommended sales page IDs. Then, the sales page determination unit 204 can determine the sales page to recommend to the new user based on the list of recommended sales page IDs generated at a timing close to the timing when the new user accessed the EC mall. Also, when the list of chronological sales page IDs is acquired by the sales page ID acquisition unit 201 at the timing when the new user accessed the EC mall, the sales page determination unit 204 generates a list of recommended sales page IDs corresponding to that timing. Then, the sales page determination unit 204 can determine the sales page to recommend to the new user based on the list of recommended sales page IDs.

[0072] The sales page determination unit 204 may identify a user not included in the browsing history information of the EC mall acquired by the EC site server 11 as a new user. Alternatively, the sales page determination unit 204 may not limit the new user to a user who accessed the EC mall for the first time, and may identify a user other than the users included in the list of chronological sales page IDs acquired by the sales page ID acquisition unit 201 as a new user.

[0073] Note that the list of chronological sales page IDs acquired by the sales page ID acquisition unit 201 is not limited to the list of chronological sales page IDs acquired by the EC site server 11 within a predetermined period. For example, the sales page ID acquisition unit 201 may acquire the list of chronological sales page IDs limited by the number of times of one or more predetermined action types. Specifically, the sales page ID acquisition unit 201 may limit it to the "purchase" action and acquire from the EC site server 11 the list of chronological sales page IDs associated with the "purchase" action for a predetermined number of times. Also, the sales page ID acquisition unit 201 may generate a list of chronological sales page IDs acquired for one or more users having user attributes similar to those of the new user from the list of chronological sales page IDs acquired by the EC site server 11 and output it to the product ID list generation unit 202.

[0074] Thus, according to this embodiment, it is possible to present a recommended sales page based on a sales page associated with the action history of other users collected in the past to a new user who has accessed the EC mall for the first time. As a result, the new user can view the sales pages of products for which more actions such as purchases have been performed in the EC mall, and the willingness to purchase may be improved.

[0075] <Fourth Embodiment> In the first embodiment, the recommended product list generation unit 203 generates a list of recommended product IDs using the feature vector extraction model 221 stored in the learning model storage unit 220. However, in this embodiment, the recommended product list generation unit 203 generates this based on rules. Specifically, the recommended product list generation unit 203 generates a list of recommended product IDs using the recommended product conversion table 212 described below. Hereinafter, this embodiment will be described, but descriptions of the same configurations and features as those of the first embodiment will be omitted. Also, the above-described second and third embodiments can be applied to this embodiment.

[0076] FIG. 14 shows an example of the functional configuration of the information processing apparatus 1400 according to the present embodiment. Compared with the information processing apparatus 10 according to the first embodiment described with reference to FIG. 2, the information processing apparatus 1400 has a table storage unit 210 having a recommended product conversion table 212. The recommended product conversion table 212 is a table that associates a product ID with a recommended product ID (recommended product identification information) that identifies one or more products recommended based on the product identified by the product ID. The recommended product conversion table 212 has one or more recommended product IDs associated with the product ID based on past performance. For example, in the recommended product conversion table 212, in an EC mall, the product ID of a product purchased by a user group having similar user attributes and the product ID (recommended product ID) of one or more other products purchased by the users belonging to the group simultaneously with or after the purchase of the product are associated. When there are a large number of such other products, the product IDs of a predetermined number of products with a high purchase rate in the user group (that is, purchased by many users in the user group) may be listed as recommended product IDs. The product conversion table 211 and the recommended product conversion table 212 are generated by a processing unit (not shown) in the information processing apparatus 10 or an external device and are updated as needed. Note that the information processing apparatus 1400 may be configured to perform the processing described in the first embodiment simultaneously. In this case, the information processing apparatus 1400 may determine the sales page of one or more recommended product IDs determined by both processes.

[0077] The processing executed by the information processing apparatus 10 according to the present embodiment will be described with reference to FIG. 5. In FIG. 5, the processing of S51 and S52 is the same as that described in the first embodiment. In S53, the recommended product list generation unit 203 generates a recommended product list based on the recommended product conversion table 212. For example, the recommended product list generation unit 203 determines the recommended product ID corresponding to each product ID by querying each product ID included in the product ID list 70 shown in FIG. 7 in the recommended product conversion table 212, and generates a list of recommended product IDs.

[0078] As an option process, in S52, when the product ID list generation unit 202 assigns a weight indicating the user's interest level according to the action type to each product ID, in S53, the recommended product list generation unit 203 may generate a list of recommended product IDs based on the weight. For example, the "purchase" action of a product is an action to actually acquire the product, and it can be said that it is an action with a higher user interest level than the "view" and "favorite registration" actions. Also, the "favorite registration" action of a product is an action that facilitates re-accessing the sales page of the product, and it can be said that it is an action with a higher user interest level than the "view" action. In this way, a weight corresponding to the user's interest level can be associated with each product ID.

[0079] FIG. 15 shows an example of a list 1500 in which a weight 702 corresponding to the action type is added to the list 70 of product IDs for each user shown in FIG. 7. In the example of FIG. 15, weights of "purchase" = 1.0, "favorite" = 0.3, and "view" = 0.2 are assigned to the corresponding product IDs according to the user's interest level. Note that different weights may be assigned even for the same action type according to the action date and time 304 in the list 60 of per-user sales page IDs corresponding to the list 70 of product IDs. For example, it can be said that the "purchase" action performed at a newer date and time has a higher user interest level than the "purchase" action performed at an older date and time. Therefore, a higher weight may be assigned to the "purchase" action performed at a newer date and time than to the "purchase" action performed at an older date and time. For example, the weight for an action performed within a first period retroactively from the date and time when the list 30 of sales page IDs serving as the basis for the list 60 of sales page IDs was acquired may be multiplied by a coefficient = 1, the weight for an action performed within a second period retroactively from the first period may be multiplied by a coefficient = 0.8, and the weight for an action performed before the second period may be multiplied by a coefficient = 0.5. Also, when multiple identical product IDs are listed for the same user as in product ID 701 = "BCD" in the list 70 of product IDs and weights are assigned to each of them, the total weight may be assigned to the product ID.

[0080] When weights are assigned according to the action type (weights 702 shown in FIG. 15), the recommended product list generation unit 203 can determine recommended product IDs based on such weights. For example, in the recommended product conversion table 212, when a plurality of recommended product IDs correspond to one product ID, the recommended product list generation unit 203 may determine more recommended product IDs for the product ID with a higher weight.

[0081] <Modification Example> In the above embodiment, the sales page ID acquisition unit 201 acquires a list of time-series sales page IDs, and the product ID list generation unit 202 generates a list of product IDs from the list. However, the list of product IDs may be generated by the EC site server 11. In this case, the product ID list generation unit 202 may acquire the list of product IDs generated by the EC site server 11.

[0082] Also, in the above fourth embodiment, an example was described in which when weights are assigned according to the action type, the recommended product list generation unit 203 determines recommended product IDs based on such weights. However, the weights may be used in another process. For example, when the sales page determination unit 204 determines a recommended sales page, the EC site server 11 may be instructed to display the recommended sales page with a higher weight in a different display mode (for example, having a more visual effect) than other recommended sales pages.

[0083] The effects according to the above-described embodiments and the above-described modifications will be described with reference to FIGS. 13A and 13C. In the screen 1300 in FIG. 13A, each of a plurality of products is sold on a different sales page. On the other hand, in the screen 1320 in FIG. 13C, the product 1308 is sold on the sales page 1306 and the sales page 1307. In such a case, whether the user purchases the product 1308 on the sales page 1306 or purchases the product 1308 on the sales page 1307, the purchase action is associated with the product 1308. Thereby, actions can be accurately aggregated for each product, and it becomes possible to determine appropriate recommended products for the user and determine the sales pages of the recommended products.

[0084] Also, when a plurality of candidate recommended sales pages are determined for one product, one (unique) recommended sales page for the product is determined based on an evaluation index such as CVR. By determining the recommended sales page based on the evaluation index, a sales page for a store highly evaluated by a plurality of users is determined, and usability and the user's purchase intention may be improved.

[0085] The disclosure of the present embodiment includes the following configurations. [1] An acquisition unit that acquires a list of sales page identification information for identifying each of the sales pages of one or more products associated with each user, a conversion unit that converts the list of sales page identification information into a list of product identification information for identifying products, a generation unit that generates a recommended product list including recommended product identification information for identifying each of one or more products recommended to each user based on the list of product identification information, and a determination unit that determines the sales page of each of one or more products recommended to each user based on one or more pieces of the recommended product identification information included in the recommended product list. An information processing apparatus.

[0086] [2] The information processing apparatus according to [1], wherein the determination unit determines a unique sales page for each of one or more products identified by one or more pieces of the recommended product identification information.

[0087] [3] The determination unit identifies one or more sales pages for selling each of one or more products identified by the one or more recommended product identification information, and determines the unique page based on the one or more sales pages. The information processing apparatus according to [2].

[0088] [4] When the determination unit identifies one sales page for selling the product identified by the recommended product identification information, the determination unit determines the identified sales page as the unique sales page. When the determination unit identifies a plurality of sales pages for selling the product identified by the recommended product identification information, the determination unit determines the unique sales page based on an evaluation index associated with the plurality of sales pages among the identified plurality of sales pages. The information processing apparatus according to [3].

[0089] [5] The evaluation index includes the CVR (conversion rate) for each of the identified plurality of sales pages. The information processing apparatus according to [4].

[0090] [6] The evaluation index includes the evaluation points given by users for each of the identified plurality of sales pages. The information processing apparatus according to [4] or [5].

[0091] [7] For each user, the apparatus further includes a conversion unit that converts the product identification information included in the list of the product identification information into a user vector representation using a machine learning model. The generation unit determines, as the recommended product identification information, one or more product identification information corresponding to one or more vector representations whose cosine similarity with the user vector representation is higher than a predetermined threshold on a common vector space where vector representations of a plurality of product identification information exist. The information processing apparatus according to [1] to [6].

[0092] [8] The apparatus further includes a selection unit that selects one or more genres in which each user shows interest. The acquisition unit acquires a list of the sales page identification information for the one or more genres selected for each user. The information processing apparatus according to any one of [1] to [7].

[0093] [9] The selection unit is the information processing apparatus according to [8], which selects one or more genres for each of the users using a machine learning model based on MF (Matrix Factorization).

[0094]

[10] The selection unit is the information processing apparatus according to [8], which selects one or more genres for each of the users using a machine learning model based on natural language processing.

[0095]

[11] The determination unit further determines the sales pages of one or more products to be recommended to users other than each of the users based on the list of the product identification information, for the information processing apparatus according to any one of [1] to

[10] .

Explanation of Signs

[0096] 10: Information processing apparatus, 11: EC site server, 12: User device, 13: Network, 14: User, 201: Sales page ID acquisition unit, 202: Product ID list generation unit, 203: Recommended product list generation unit, 204: Sales page determination unit, 210: Table storage unit, 211: Product conversion table, 212: Recommended product conversion table, 220: Learning model storage unit, 221:. Feature vector extraction model, 222: MF (Matrix Factorization)-based genre prediction model, 223:, Natural language processing (Natural Language Processing (NLP))-based genre prediction model, 231: EC mall operation unit, 232: Evaluation index derivation unit, 240: Sales page ID database, 250: Evaluation index database, 260: Target genre database

Claims

1. An acquisition unit that acquires a list of sales page identification information for identifying each of the sales pages of one or more products associated with each user; A conversion unit that converts the list of the sales page identification information into a list of product identification information for identifying products; A generation unit that generates a recommended product list including recommended product identification information for identifying each of one or more products recommended to each user based on the list of the product identification information; A determination unit that determines the sales page of each of one or more products recommended to each user based on one or more pieces of the recommended product identification information included in the recommended product list; An information processing apparatus having the above.

2. The information processing apparatus according to claim 1, wherein the determination unit determines a unique sales page for each of one or more products identified by one or more pieces of the recommended product identification information.

3. The information processing apparatus according to claim 2, wherein the determination unit specifies one or more sales pages for selling each of one or more products identified by one or more pieces of the recommended product identification information, and determines the unique page based on the specified one or more sales pages.

4. The determination unit when specifying one sales page for selling the product identified by the recommended product identification information, determines the specified sales page as the unique sales page; when specifying a plurality of sales pages for selling the product identified by the recommended product identification information, determines the unique sales page based on an evaluation index associated with the plurality of sales pages among the specified plurality of sales pages. The information processing apparatus according to claim 3.

5. The information processing apparatus according to claim 4, wherein the evaluation index includes a CVR (conversion rate) for each of the specified plurality of sales pages.

6. The information processing apparatus according to claim 4 or 5, wherein the evaluation index includes an evaluation score by a user for each of the specified plurality of sales pages.

7. For each user, further having a conversion unit that converts the product identification information included in the list of the product identification information into a user vector representation using a machine learning model. The generation unit determines, as the recommended product identification information, one or more pieces of product identification information corresponding to one or more vector representations having a cosine similarity higher than a predetermined threshold with the user vector representation on a common vector space in which vector representations of a plurality of pieces of product identification information exist, for the information processing apparatus according to claim 1.

8. further comprising a selection unit that selects one or more genres that each user is interested in; the acquisition unit acquires a list of the sales page identification information for one or more genres selected for each user; the information processing apparatus according to claim 1.

9. The selection unit selects one or more genres for each user using a machine learning model based on MF (Matrix Factorization), for the information processing apparatus according to claim 8.

10. The selection unit selects one or more genres for each user using a machine learning model based on natural language processing, for the information processing apparatus according to claim 8.

11. The determination unit further determines, based on the list of the product identification information, one or more products to be recommended to users other than each user and determines the respective sales pages, for the information processing apparatus according to claim 1.

12. An information processing method executed by an information processing apparatus, comprising: an acquisition step of acquiring a list of sales page identification information for respectively identifying one or more sales pages of products associated with each user; a conversion step of converting the list of the sales page identification information into a list of product identification information for identifying products; a generation step of generating a recommended product list including recommended product identification information for respectively identifying one or more products to be recommended to each user based on the list of the product identification information; a determination step of determining, based on one or more pieces of the recommended product identification information included in the recommended product list, the respective sales pages of one or more products to be recommended to each user; An information processing method including the above.

13. An information processing program for causing a computer to execute information processing, wherein the program causes the computer to: an acquisition process of acquiring a list of sales page identification information for respectively identifying one or more sales pages of products associated with each user; a conversion process of converting the list of the sales page identification information into a list of product identification information for identifying products; A generation process for generating a recommended product list including recommended product identification information for identifying each of one or more products to be recommended to each user based on the list of the product identification information; A determination process for determining a sales page for each of one or more products to be recommended to each user based on one or more pieces of the recommended product identification information included in the recommended product list, and a processing for executing the processes is provided. An information processing program.

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