KR102193345B1 - Item recommendataion method and apparatus between group member and group item - Google Patents

Abstract

An aspect of the present invention discloses a product recommendation method between a group customer and a group product in an e-commerce system. The method includes receiving data of a plurality of products available for purchase in a plurality of online shopping malls and a plurality of customers related to purchases through the online shopping mall, and a purchaseability index of individual products and individual customers based on the received data Obtaining, generating a matrix by configuring at least two or more of the plurality of products as a column and at least two or more of the plurality of customers as a row (some of the plurality of customers are It has a relationship with some of the plurality of products, and the content included in the blanks of the matrix is a purchaseability index between the product and the customer), for each row of the matrix, the purchaseability index of at least two customers Calculating a first subtotal, ranking the calculated first subtotal, selecting products with a higher first subtotal ranking as many as the number of first products, for individual columns of the matrix, for the selected products Calculating a second subtotal for each customer, ranking the calculated second subtotal, selecting customers with a higher second subtotal ranking by the number of first customers, and the selected products for the selected customers And performing a promotion using the online shopping mall.

Description

Product recommendation method and device between group customers and group products {ITEM RECOMMENDATAION METHOD AND APPARATUS BETWEEN GROUP MEMBER AND GROUP ITEM}

The present invention relates to a product recommendation method, and more particularly, to a product recommendation method for maximizing the efficiency of product promotion.

Recently, as the phenomenon of purchasing products using e-commerce increases, many researches on product recommendation algorithms for recommending products optimized for individuals have been conducted.

One of the conventional product recommendation methods is a method of ranking recommended products by quantifying the similarity of products by analyzing images and attributes of this product based on a specific product.

However, a different method is required when promoting a plurality of products and a plurality of customers. For example, if you choose 4 out of 10 products and want to promote a promotion to 20 specially selected out of 100 customers, you need to solve the following questions.

(1) Which of the 10 products will constitute the promotion

(2) Which of the 100 customers will be promoting the above product

However, in the conventional method of quantifying and recommending similarity, it is difficult to substitute and use product promotions for group-level products and group-level customers as described above, and the number of people and products that actually visit the shopping mall is large enough. In consideration, although recommending products based on similarity between individual products and individual customers using a conventional method may be efficient from a buyer's point of view, there is a problem that it is inefficient from the point of view of an operator who operates a shopping mall.

An object of the present invention for solving the above-described problem is to provide a product basket-based product recommendation (promotion) method that optimizes the likelihood of a purchase occurring in a product group and a customer group.

In order to achieve the above object, a product recommendation method between a group customer and a group product in an electronic commerce system according to an aspect of the present invention is related to a plurality of products available for purchase in a plurality of online shopping malls and a purchase through the online shopping mall. Receiving data of a plurality of customers, acquiring an individual product and a purchaseability index of an individual customer based on the received data, at least two of the plurality of products as a column, the plurality of Creating a matrix by configuring at least two or more of the customers in rows (some of the plurality of customers have a relationship with some of the plurality of products, and the contents included in the blanks of the matrix are products And a purchaseability index between customers), calculating a first subtotal of at least two purchaseability indexes of at least two customers for each row of the matrix, ranking the calculated first subtotal, the number of first products Selecting products with a higher first subtotal ranking by the first subtotal, calculating a second subtotal for each customer for the selected products, with respect to individual columns of the matrix, ranking the calculated second subtotal, first It may include selecting customers having a second subtotal ranking as high as the number of customers, and performing a promotion for the selected products for the selected customers using an online shopping mall.

Selecting a product having a higher first subtotal ranking as much as the number of first products includes reconfiguring a matrix by selecting a product with a higher first subtotal ranking and deleting the remaining products, Selecting a customer with a higher second subtotal ranking may include selecting a customer with a higher second subtotal ranking and deleting the remaining customers.

The purchaseability indicator may include the number of connection paths in graph-type data consisting of a product node hierarchy, an order node hierarchy, and a customer node hierarchy.

In the graph-type data, at least one second node of the second node layer is connected to the first node of the first node layer, and a third node of the third node layer is connected to the at least one second node. As connected, it can be generated in the form of a radial graph.

The number of connection paths to which at least two of the nodes of the at least one product node layer and the nodes of the at least one customer node layer can be connected may be used as the purchaseability index.

The purchaseability index may be a value calculated using a product recommendation algorithm based on similarity.

Promoting the selected products to the selected customers using an online shopping mall includes placing and displaying the selected products in a first area of a first web page of the online shopping mall to the selected customers. Can include.

Promoting the selected products to the selected customers by using an online shopping mall may include selling the selected products at a discount to only the selected customers.

A product recommendation device in an electronic commerce system according to an aspect of the present invention for achieving the above object includes a communication unit communicating with at least one of a wired and wireless network, and a plurality of purchaseable online shopping malls through the communication unit. Receives data of a plurality of customers related to products and purchases through the online shopping mall, obtains an individual product and a purchaseability index of an individual customer based on the received data, and selects at least two or more of the plurality of products. A matrix consisting of at least two or more of the plurality of customers in a row as a column-Some of the plurality of customers have an association relationship with some of the plurality of products, and in the blank space of the matrix The included content is a purchaseability index between a product and a customer-is generated, and for each row of the matrix, a first subtotal of the purchaseability index of at least two customers is calculated and ranked, and divided by the number of the first product. 1 Selecting products with a high subtotal ranking, calculating and ranking a second subtotal for each customer for the selected products for individual columns of the matrix, and selecting customers with a second subtotal ranking as high as the number of first customers. Accordingly, it may include a processor that performs a promotion for the selected products to the selected customers using an online shopping mall.

In order to achieve the above object, a product recommendation method between a group customer and a group product in an electronic commerce system according to another aspect of the present invention is related to a plurality of products available for purchase in a plurality of online shopping malls and a purchase through the online shopping mall. Receiving data of a plurality of customers, acquiring an individual product and a purchaseability index of an individual customer based on the received data, at least two of the plurality of products as a column, the plurality of Creating a matrix by configuring at least two or more of the customers in rows (some of the plurality of customers have a relationship with some of the plurality of products, and the contents included in the blanks of the matrix are products And a purchaseability index between customers), calculating a first subtotal of a purchaseability index for at least two products, for individual columns of the matrix, ranking products having the calculated first subtotal value , Selecting customers with a higher first subtotal ranking by the number of first customers, calculating a second subtotal for each product for the selected customers with respect to individual columns of the matrix, and ranking the calculated second subtotal The step may include selecting products with a second subtotal ranking as high as the number of first products, and performing a promotion for the selected products for the selected customers using an online shopping mall.

The selecting of customers having a higher first subtotal ranking as many as the number of first customers includes reconfiguring a matrix by selecting customers with a higher first subtotal ranking and deleting the remaining products, Selecting products with a higher second subtotal ranking may include selecting products with a higher second subtotal ranking and deleting the remaining products.

The purchaseability indicator may include the number of connection paths in graph-type data consisting of a product node hierarchy, an order node hierarchy, and a customer node hierarchy.

In the graph-type data, at least one second node of the second node layer is connected to the first node of the first node layer, and a third node of the third node layer is connected to the at least one second node. As connected, it can be generated in the form of a radial graph.

The number of connection paths to which at least two of the nodes of the at least one product node layer and the nodes of the at least one customer node layer can be connected may be used as the purchaseability index.

The purchaseability index may be a value calculated using a product recommendation algorithm based on similarity.

Promoting the selected products to the selected customers using an online shopping mall includes placing and displaying the selected products in a first area of a first web page of the online shopping mall to the selected customers. Can include.

Promoting the selected products to the selected customers by using an online shopping mall may include selling the selected products at a discount to only the selected customers.

A product recommendation device in an electronic commerce system according to another aspect of the present invention for achieving the above object includes a communication unit communicating with at least one of a wired and wireless network, and a plurality of products available for purchase in a plurality of online shopping malls through the communication unit. Receive data of a plurality of customers related to purchases through the online shopping mall and the online shopping mall, obtain an individual product and a purchaseability index of an individual customer based on the received data, and open at least two of the plurality of products. (column), a matrix consisting of at least two or more of the plurality of customers in a row-Some of the plurality of customers have a relationship with some of the plurality of products, and are included in the blanks of the matrix The content is a purchaseability index between product and customer-is generated, and for each column of the matrix, a first subtotal of the purchaseability index of at least two product customers is calculated and ranked, and the number of first customers is first. By selecting customers with a high subtotal ranking, calculating and ranking a second subtotal for each product for the selected customers for individual rows of the matrix, and selecting products with a second subtotal ranking as high as the number of first products, It may include a processor that performs a promotion for the selected products to the selected customers using an online shopping mall.

According to the method and apparatus for recommending a product between a group customer and a group product of the present invention, since promotions for a plurality of products and a plurality of customers can be performed in a single bundle, there is an effect that management from the viewpoint of an operator is very efficient.

1 is a conceptual diagram showing a system to which a product recommendation method using a graph database of the present invention is applied;
2 is a conceptual diagram for explaining a similarity-based product recommendation method;
3 is a graph based on a product node, an order node, and a customer node used in a product recommendation method using a graph database according to an embodiment of the present invention;
4 is a conceptual diagram for explaining direct and indirect correlations between products;
5 is a flowchart showing a product recommendation method using a graph database according to an embodiment of the present invention;
6 is a conceptual diagram showing a state in which a plurality of radial graphs centered on a product are entangled;
7 is a diagram for explaining proper use of the same/similar category and a combined category in a product recommendation method using a graph database according to an embodiment of the present invention;
FIG. 8 is a conceptual diagram illustrating an artificial intelligence learning method related to a recommendation algorithm used in a product recommendation method using a graph database according to an embodiment of the present invention.
9 is a conceptual diagram illustrating a connection path indexing method of a product recommendation method using a graph database according to an embodiment of the present invention;
10A to 10D are diagrams showing various embodiments of finding similar products and customers of similar tendency as outputs by designating products and customer nodes as inputs;
11 is a diagram showing a result of implementing a product recommendation method using a graph database according to an embodiment of the present invention in a web page;
12 is a flowchart illustrating a product recommendation method using a graph database according to another embodiment of the present invention.
13A and 13B are diagrams showing an exemplary aspect of product path prediction in a product recommendation method using a graph database according to another embodiment of the present invention;
14A and 14B are diagrams illustrating exemplary product path prediction in a situation in which a next product is inquired after product inquiry in the embodiment of FIG. 13A;
15 is a conceptual diagram for explaining a situation in which a promotion is required for products with low sales according to product sales activity analysis;
16A and 16B are conceptual diagrams illustrating a matching method for providing a special promotion by matching a specific number of customer groups and a specific number of product groups when there are 5 customers and 6 products;
17A and 17B are flowcharts illustrating a method of recommending a product basket according to another embodiment of the present invention;
18 is a flow chart showing a product-based basket recommendation method according to another embodiment of the present invention;
19A to 19C are tables for specifically explaining a method of generating a matrix for each step of the basket recommendation method of FIG. 18;
20 is a flow chart showing a customer-based basket recommendation method according to another embodiment of the present invention;
21A to 21C are tables for specifically explaining a method of generating a matrix for each step of the basket recommendation method of FIG. 20;
22 is a block diagram schematically illustrating an apparatus using a product recommendation method based on a graph database and a product basket recommendation method according to an embodiment of the present invention.

In the present invention, various modifications may be made and various embodiments may be provided, and specific embodiments will be illustrated in the drawings and described in detail.

However, this is not intended to limit the present invention to a specific embodiment, it is to be understood to include all changes, equivalents, and substitutes included in the spirit and scope of the present invention.

Terms such as first and second may be used to describe various elements, but the elements should not be limited by the terms. These terms are used only for the purpose of distinguishing one component from another component. For example, without departing from the scope of the present invention, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element. The term and/or includes a combination of a plurality of related listed items or any of a plurality of related listed items.

When a component is referred to as being "connected" or "connected" to another component, it is understood that it may be directly connected or connected to the other component, but other components may exist in the middle. Should be. On the other hand, when a component is referred to as being "directly connected" or "directly connected" to another component, it should be understood that there is no other component in the middle.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In the present application, terms such as "comprise" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof, does not preclude in advance.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in this application. Does not.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the accompanying drawings. In describing the present invention, in order to facilitate an overall understanding, the same reference numerals are used for the same elements in the drawings, and duplicate descriptions for the same elements are omitted.

Product recommendation method using graph database

1 is a conceptual diagram showing a system to which a product recommendation method using a graph database of the present invention is applied. As shown in FIG. 1, the product recommendation system according to an embodiment of the present invention includes a plurality of shopping malls 110-1 to 110-N, a network 120, a server 130, a database 140, and a user terminal. 150 may be included.

Referring to FIG. 1, shopping malls 110-1 to 110 to N refer to online shopping malls that sell products and/or services through the Internet. Shopping malls 110-1 to 110 to N sell products to visitors to a corresponding address on the Internet with a specific URL. The shopping mall operator operates the shopping mall by acquiring at least a portion of the amount of the sold product through his/her account associated with the shopping mall.

In the embodiment of FIG. 1, shopping malls 110-1 to 110 to N are web pages associated with a specific address (eg, URL) on the Internet, a server handling information related to the shopping mall, or an operator of the shopping mall, as described above. It can be implemented as a terminal of. The terminal may include a computing device such as a smartphone or a PC.

According to an embodiment of the present invention, the plurality of shopping malls 110-1 to 110 -N may interwork with the server 130 through the network 120. Here, the server 130 is a device that obtains information from a plurality of shopping malls 110-1 to 110-N and recommends a product in response to a product recommendation query. The term “device” or “product recommendation device” may be interpreted as referring to the server 130 of FIG. 1. The server 130 may be a server (not shown) that distributes and manages a platform that supports opening and operating a shopping mall, or may be a server interlocking with the server. Therefore, the server 130 not only handles information related to one shopping mall 110-1, but also handles information related to a plurality of shopping malls 110-1 to 110-N, and provides various information based on big data. Can analyze and learn.

The server 130 may be implemented as a computing device. The computing device referred to herein may be implemented as a server-class computer terminal. The computing device may include an input device of a typical computer terminal, a display device, a networking device, a hard disk, a memory for storing a program, and a processor for executing a program stored in the memory. However, it is not necessarily implemented as a server-class computer terminal.

The network 120 is a communication network connecting the shopping malls 110-1 to 110-N and the server 130, and may include a wired network and/or a wireless network (Internet).

The server 130 is the order history data and/or products from the shopping mall operation server (not shown) that generally manages the information of the shopping malls 110-1 to 110-N and/or the shopping malls 110-1 to 110-N. Acquire inquiry data. Then, the obtained data is converted into a graph form having a plurality of node hierarchies. The data converted into a graph form is stored in the database 140 outside the server 130. However, the database 140 does not necessarily have to be outside the server 130. It may exist inside.

The server 130 responds to a product recommendation request from the user terminal 150 (which may be referred to as a "recommended product search request", a "query", etc.), using a graph composed of three node hierarchies to provide similar products from a specific product. Process queries for search. At this time, on the converted graph data, the connection path between the product and the product, the customer and the customer, or the product and the customer is extracted, and the degree of similarity between the extracted connection paths may be determined by considering the distance and/or number of the extracted connection paths. . In addition, when product search data is used, a query for similar products is processed by analyzing the inquiry history during a certain step before or after product search on a graph. The part related to the technology for processing queries for similar products will be described in more detail below.

In another embodiment of the present invention, the server 130 uses the product based on the existence of an index (which may include similarity using the graph database above) indicating the purchaseability between individual customers and individual products. The basket can be optimized. That is, if the product basket recommendation method according to an embodiment of the present invention is used, a plurality of product groups optimized for at least one customer (in multiple cases, a customer group) can be appropriately promoted.

The user terminal 150 may be a terminal of a purchaser of the shopping mall 110-1. The purchaser may transmit a product recommendation request to the server 130 or a separate shopping mall management server (not shown) using the user terminal 150. If a separate shopping mall management server exists, the shopping mall management server transmits the received product recommendation request to the server 130. The product recommendation request may include a query for similar products. Furthermore, it may include queries for similar customers. Then, when the processing of the query is completed using a graph-based data analysis method and/or a basket recommendation method, the processed data is provided to the user terminal 150 so that the buyer checks it and selects a product based on this. Make it possible. The user terminal 150 may provide a product recommendation request to the server 130 using the network 120 (a specific web page within the network).

According to another embodiment of the present invention, a terminal or user terminal 150 associated with the shopping malls 110-1 to 110-N may download an application provided from the server 130 and perform automatic generation through the application. . That is, in the terminal or user terminal 150 associated with the shopping malls 110-1 to 110-N, when a user inputs a query for a similar product or similar customer, a graph-based product recommendation process or a product basket recommendation process is performed in the terminal. Can be done in Without a separate server device, the processor (not shown) of the terminal executes the downloaded application, and the optimal product may be recommended through data transformation and analysis of the transformed data.

2 is a conceptual diagram for explaining a product recommendation method based on similarity.

Referring to FIG. 2, the device generally recommends products based on similarities between images and attributes of products. That is, first, the degree of similarity between product images can be calculated using an image analysis module to be utilized as a purchaseability index. In the embodiment of FIG. 2, the apparatus may analyze the color, shape, etc. of the shoe product 1 to extract the shoe product 2 having the most similar color and shape among numerous product images stored in the database. Alternatively, shoe product 2 having similar attributes may be extracted based on the product attributes of shoe product 1. For example, if the color of the shoe product 1 is red, the size is 250, the brand is "A brand", and the shape is "shoe", the shoe product 2 of the same or similar attribute may be searched and extracted from the database. . However, the method of using the similarity based on the image and/or product attributes, as described above, corresponds to the case of purchasing similar products at the same time, so it cannot be said that the purchase possibility is very high, and the demand of the buyer cannot be satisfied. Occurs.

According to an embodiment of the present invention, the device may extract attributes of products that have been purchased a lot based on the purchase history of a specific customer, and set a high purchaseability index of products having corresponding attributes. Using this point, a purchase possibility index can be calculated by categorizing the customer with the attributes of the product purchased by the customer. That is, it is possible to find customers who have a similar tendency to those who have purchased the product, and recommend a product purchased by a similar customer as a similar product.

As described above, the purchaseability index based on various methods of product image, product attribute, and/or customer attribute analysis may be used as an element that enters the blank space of the matrix in the method of recommending product baskets in the future.

3 is a graph based on product, order, and customer nodes used in a product recommendation method using a graph database according to an embodiment of the present invention.

Referring to FIG. 3, the device may collect order history data and generate a graph composed of at least three node hierarchies. The order history data basically includes information that "a specific customer A has ordered product C using order B in a specific shopping mall". In other words, it is data that includes the relationship between customers, orders, and products.

The apparatus generates a radial graph by forming a product into a first node layer, an order into a second node layer, and a customer into a third node layer based on each order history data. In the embodiment of FIG. 3, product A 310 is included in six orders 320 (orders A to F), and each order is obtained from a different customer 330. In this case, the device connects the order A (320) to the order F using a connecting line, centering on the product A (310), and connects the customer A (330) requesting the order A (320) to the order A (320). Connect each so that a radial graph is created from the product. In other words, the product-order connection line represents the product included in the order, and the order-customer connection line represents the customer who placed the order. In this case, the first node layer (product layer) of each node layer of the graph may be indicated in red color, the second node layer (order layer) may be indicated in green color, and the third node layer (customer layer) may be indicated in blue color. The graph generated in this way may be visualized and displayed through a display device.

As shown in the embodiment of FIG. 3, the device may display a plurality of orders including one product 310 (product A) and a customer who has placed the order. At this time, it can be seen that between the customers 330 included in the graph of FIG. 3, there is one path connected by a length of distance 4 (customer → order → product → order → customer). Therefore, it can be seen that these customers show similarity in purchasing tendency compared to those who do not. The meaning of the distance 4 will be described in more detail with reference to FIG. 4 below.

4 is a conceptual diagram illustrating direct correlation and indirect correlation between products.

Referring to FIG. 4A, there are not many people who order two very similar products (which may belong to a top) at the same time. That is, in the case of the connection path of distance 2, which has a high direct correlation, it is preferable to determine that the similarity is low.

However, referring to (b) of FIG. 4, when viewed through another product (bottom (product 3)), a number of customers who purchase with a combination of (product 1- product 3) and (product 2- product 3) May exist. That is, the initial direct correlation (distance 2) (refer to the embodiment (a) of FIG. 4) shows a low degree of similarity, but when the distance becomes distant (e.g., distance 4), indirect correlation can be observed, It is desirable that this indirect correlation be implemented as a high indicator in terms of purchaseability. In other words, the number of paths to which two products (product 1 and product 2) can be connected at a fixed distance (distance 4 in the embodiment of FIG. 4B) may be an index indicating similarity.

Returning to FIG. 3 again, in view of the above correlation, customers connected to the distance 4 may have a relationship of “other customers who have purchased a product purchased by a specific customer”. For a specific customer, by looking for a relationship of "customer who purchased the same product" as above, a path through which a specific customer and another customer are connected through different products can be extracted. According to an embodiment of the present invention, the device may be determined as a customer with the largest number of connected routes, and as a customer B having the most similar purchasing tendency to a specific customer (customer A).

Accordingly, the device may recommend a product that a specific customer A has purchased, but a similar customer B has not purchased, to the customer B based on the graph database. Similarly, a product purchased by customer B but not purchased by customer A may be recommended to A. That is, the device can connect the customer A and the customer B via the common product A 310, and analyze the similarity in consideration of the length of the connection path and the number of connection paths.

5 is a flowchart illustrating a product recommendation method using a graph database according to an embodiment of the present invention.

Referring to FIG. 5, a server (which may be referred to as a “device”) collects order history data from an online shopping mall or a server managing the shopping mall (S510). At this time, the acquired data includes information on the product, the order including the product, and the relationship between the customer, who performed the order.

The device converts the collected order history data into graph-type data (S512). The graph is composed of three node hierarchies, and the first node layer is connected to the product, which is the second node layer, through a connecting line, and the order is connected to the customer, which is the third node layer, through a connecting line, and the first node layer is connected. It can have a radial graph shape around the center. Then, the device stores the data converted in the form of a graph in the database (S514).

The client terminal inputs a recommended product search request (S516). The input request information is transmitted to the device (server) side. In this case, the recommended product search request may be divided into (i) a product-oriented search request and (ii) a customer-oriented search request, and input. This will be described in more detail with reference to FIGS. 10A to 10D.

When a request to search for recommended products is input, the device analyzes the correlation centering on the target product or target customer using the converted data in the form of a graph (S518). Then, the target product or target customer is placed at the center, and a connection path to other products or other customers is generated based on the analyzed correlation (S520). At this time, it is determined whether the connection path exists within a certain distance range based on the distance of the generated connection path (S522). As described above, it is preferable to have a distance larger than the distance 2 indicating a direct correlation (e.g., a=2), and as the distance increases, the route to be connected increases proportionally, so that the distance value is excessively large. It is preferable to exclude the connection path (eg b=10). The values a and b that define the distance range can be set through user settings and can be arbitrarily changed. In addition, the values of a and b can be changed in the form of finding an optimal solution through machine learning. A connection path having a distance less than a predetermined range or a distance exceeding the range is distributed (S524).

According to an embodiment of the present invention, the device may adjust the number of connection paths in consideration of the distance of connection paths between two nodes. As described above, the distance between two directly connected nodes is defined as a distance of 1 hop. At this time, when the distance of the first connection path is n ₁ (n ₁ is a natural number) and the distance of the second connection path is n ₂ (n ₂ is a natural number), the number of the first connection paths is adjusted to 1/n ₁ Can be calculated. In addition, the number of second connection paths may be calculated by adjusting to 1/n ₂ . That is, except for the direct correlation of distance 2, since nodes connected by a short distance may have high similarity, the number of connection paths can be adjusted by dividing by the corresponding distance considering this more importantly. For example, the number of connection paths between nodes of the distance 4 is considered as 1/4, and may be considered larger (importantly) than 1/8, which is the number of connection paths of the distance 8.

The device calculates the number of connection paths existing within the set distance range based on the above method (S526). Then, the product or customer having the most number of connection paths is extracted (S528). In this case, by sorting based on the number of connection paths, the top several products or customers may be extracted as products or customers with high similarity. The high degree of similarity means that the purchasing tendency is similar. In particular, when the similarity between customers and customers is high, it is likely that the target customer will purchase the remaining products minus the common product within the first order containing the common product through a common product from the viewpoint of the customer determined to be similar. Because it can be judged, these products can be derived as recommended products.

In this way, information on the products and customers resulting from the similarity, and other products in the first order, excluding common products based on the corresponding customer, is provided to the client terminal. The terminal may display the provided information to the purchaser (S530).

6 is a conceptual diagram showing a state in which a plurality of radial graphs centered on a product are entangled.

Referring to FIG. 6, a plurality of radial graphs 610, 620, 630, and 640 may be entangled. In the apparatus, a first radial graph 610 centered on product A and a second radial graph 620 centered on product B may be connected to each other through order A or customer A. These two radial graphs 610 and 620 may be visualized and displayed in two dimensions on a display screen. Furthermore, the first radial graph 610 and the third radial graph 630 centered on the product C may be connected to each other via order B or customer B, and at this time, the second radial graph 620 and the third radial graph 630 The graph 630 may also be connected to each other via order C or customer C. As described above, when three or more radial graphs 620 are connected, it is difficult to visualize and express the correlation between each radial graph in two dimensions.

Thus, in an embodiment of the present invention, the device may display radial graphs in three dimensions. That is, if the first and second radial graphs 610 and 620 are displayed in the xy plane, the first and third radial graphs 610 and 630 may be displayed with a first angle in the z-axis direction, and the second The second and fourth radial graphs 620 and 640 may be displayed with an angle. Accordingly, it is possible to pull the graphs through the zoom-in/zoom-out function of the user interface, and to see the connection relationship between a plurality of radial graphs by adjusting the three-dimensional angle using the panning function. Can be visualized. In addition, when a specific node is selected, the selected node may be brought to the center of the screen, so that a plurality of radial graphs connected to the central node may be displayed in all directions.

According to another embodiment of the present invention, it is not necessary to take the type of the relationship as "customer-order-product-order-customer", but the relationship of "product-order-customer-order-product" centering on the customer. Can be specified. In other words, it is okay to take various standards of relationship.

7 is a diagram for explaining appropriate use of the same/similar category and a combined category in a product recommendation method using a graph database according to an embodiment of the present invention.

Referring to FIG. 7, the device may utilize product category information when determining similarity and when extracting similar products/customers and then derives recommended products. For example, in searching for similar products, similar products can only be found within the same category (e.g., tops), or in different categories (categories by competition or combination) (e.g., tops and bottoms, bottoms and shoes, etc.) You can try to find a product corresponding to ).

In particular, by classifying the class of the product category, the first class is divided into the largest category such as tops, bottoms, shoes, and accessories, and the same/similar category and the combined category are classified, and the second class is within the first class. It can be subdivided and defined in Each product category is defined through an identifier, and a similarity/combination relationship between the identifiers is coded so that it is possible to identify what kind of relationship each product category is. In the case of the second class, for example, it can be classified into a shirt, a southern shirt, a T-shirt, a hood, a vest, etc. The third class is defined as a category subdividing the second class.

According to an embodiment of the present invention, assuming that class classification is up to the third class, it is possible to derive similar products by using the same/similar product category in all classes from the first class to the third class. In this case, for the "T-shirt", the "Southern" product category may be selected as a "top" and similar product category as the "T-shirt", and products belonging to the "Southern" product category within a specific order are derived as similar products. Can be.

Alternatively, similar products may be derived using the combined category in the first class. In this case, a "bottom" category is selected for the "T-shirt", so that products belonging to the bottom product category may be derived as similar products of the T-shirt. In the case of T-shirts, since there are many situations where they are purchased in combination with bottoms, this setting may be appropriate.

In another embodiment, the first class may use the same/similar category, but the second class may use the combined category. In this case, for "T-shirt", as the same/similar category in the first class, a category of "top" may be selected, and in the second class, a "jumper" category combined with "T-shirt" may be selected. That is, within a specific order, products belonging to the product category of the "jumper" may be derived as similar products.

In this way, in deriving a similar product using a graph, determining whether to use the same/similar product category or a combined product category is an important factor to be considered because the resulting value is different. Also, the result value varies depending on which class uses the similar category and the combined category. The device can define these settings through user settings and allow the user to arbitrarily change these settings. Or, big data-based artificial intelligence can be used to optimally tune these areas. A method of using the artificial intelligence model will be described in more detail with reference to FIG. 8.

FIG. 8 is a conceptual diagram illustrating an artificial intelligence learning method related to a recommendation algorithm used in a product recommendation method using a graph database according to an embodiment of the present invention.

Referring to FIG. 8, it can be said that it is very important to determine whether to use "same/similar category" or "combination category" in a divided specific class because the result value is completely different. Accordingly, the device may train an artificial intelligence model using an artificial intelligence learning module (not shown), and then set whether to use a similar or combined category in a specific class based on the learned artificial intelligence model.

According to an embodiment of the present invention, an artificial intelligence model used in a device includes a deep learning convolutional neural network model (CNN). However, the present invention is not limited thereto, and may include any one of a deep neural network (DNN), a recurrent neural network (RNN), and a long short term memory network (LSTM).

The device generates order data 1 and order data 2 as a training data set. At this time, the two order data are generated by the order data prior to the selection process (order data subject to the similarity search request) and the actual order after product recommendation according to graph-based similar product derivation according to an embodiment of the present invention. It has a relationship of order data. That is, a customer who ordered order data 1 receives a recommendation of a plurality of products using a graph-based similar product derivation method according to an embodiment of the present invention, and combines order data 2 formed by selecting one of the recommended data. Create a learning data set. At this time, in deriving graph-based similar products for product recommendation, similar products are derived while changing whether to use a similar or combined category in the first class and whether to use a similar or combined category in the second class, Based on the order data after derivation, artificial intelligence is trained in the direction of placing an order within the product derived as the highest through the graph-based similar product derivation process.

In addition, while changing the threshold for the optimal distance range (values a and b in Fig. 5), the optimal threshold value is found by accumulating the result value based on the learning data set of order data 1 and order data 2. Let's do it.

At this time, the node layer used as an intermediate between the order data 1 and the order data 2 forming the learning data does not necessarily have to be the first node layer “product”, but may be the third node layer “customer”. Also, the second node layer may be the reference.

9 is a conceptual diagram illustrating a connection path indexing method of a product recommendation method using a graph database according to an embodiment of the present invention.

Referring to FIG. 9, the device identifies products and products, customers and customers, or a connection path between products and customers by indexing data. In this case, the connection path may be composed of a node layer identifier and an index number. The node layer identifier is composed of a combination of the identifier of the target node that is the center of the search and the identifier of the final destination node. In this case, the first node layer (product node) may be identified as p, and the third target node layer (customer node) may be identified as m. After identifying the node hierarchy of the starting node and the destination node in this way, the indexing number is attached. The indexing number is related to a query and may have a special meaning. For example, if an index of "0100" is attached to the pp node hierarchy, it means a query for customers who purchased the product to find another product purchased, and the path associated with this is a group of customers who order the first product (departure node). It means another product ordered. A description of the query according to the meaning of the index attached directly to the node layer identifier will be described in more detail with reference to FIG. 10.

In this way, each connection path is identified in the form of "-1" with the index attached. That is, the first path associated with the pp0100 query may be dataized as "pp0100-1". After that, the number at the end of the "-" is increased by 1 to identify the route. For example, if a customer who ordered the first product has a plurality of other products ordered in a bundle, it may be defined as pp0100-1, pp0100-2, pp0100-3, and the like.

In the same way, the connection path from the first customer to the second customer is expressed as mm0100-1, mm0100-2, or mm0101-1, mm0101-2, and the like. The connection path from the first product to the first customer is expressed as pm0100-1, pm0100-2, and so on. The connection path from the first customer to the first product is expressed as mp0100-1, mp0100-2, and the like.

In this way, the connection path is converted into data, and the device can hold it, and the number of connection paths within a specific distance range is calculated using this.

Additionally, the device may be controlled to enable conversion in the form of a connection path list in the implementation of the graph of FIG. 6. That is, while visualizing a plurality of radial graphs, if there is a request to convert information related to a connection path between a specific departure node and a specific arrival node into a list, the connection path index between the departure node and the destination node is converted into a list. Can be displayed. Also, the distance of the connection path of the first index (eg, pp0100-1) may be displayed in the corresponding list. Accordingly, when a connection route having a specific index is clicked, the corresponding route is displayed in a highlighted state on the graph, and through which route is connected from the departure node to the destination node, it is visualized so that it can be seen intuitively.

In addition, in the case of pp0100-1 and pp0101, they may be duplicated and indexed.To prevent this, when all connection paths for pp0100 are extracted, they are extracted as pp0100-1, and all connection paths for pp0101 are extracted. In this case, it is extracted as pp0101-1 so that it is not duplicated, and when all routes are extracted, the duplicated pp0100-1 can be excluded and identified as pp0101-1, which represents a more detailed connection route.

10A to 10D are diagrams illustrating various embodiments of searching for similar products and similar-minded customers as outputs by designating products and customer nodes as inputs.

The device generates a query to find similar products and customers by designating a product or customer node as an input.

Referring to FIG. 10A, a query for finding products similar to a specific product is generated. A customer who ordered a product can generate a pp0100 query that shows other products ordered in a bundle. In addition, a connection path can be derived by using a query pp0101 that indicates a product in the same category among other products ordered in a bundle by a customer who ordered the product.

In addition, a pp0200 query indicating a product purchased by a customer who has ordered a product in another order and a pp0201 query indicating a product in the same category in the other order may be used.

Referring to FIG. 10B, a query for finding a customer having similarity to a specific customer is as follows. An mm0100 query representing a second customer who has ordered the same product as the first customer can be used. Further, the mm0101 query may be used to find a route to a second customer who has ordered the second product of the same category, not the product that is not exactly the same as the first product ordered by the first customer.

Referring to FIG. 10C, in relation to a query for finding a customer with similarity to a specific product, a query pm0000 indicating a path to a customer who has purchased a product and a product purchased in a bundle, and among the products purchased in a bundle, the corresponding product You can use the pm0100 query indicating the path of the customer who purchased the product in the same category as and.

Referring to FIG. 10D, in relation to a query for finding a product similar to a specific customer, an mp0000 query indicating a path to products purchased in a bundle, such as a product ordered by a corresponding customer, and among the bundled products, the corresponding You can use the mp0001 query indicating the path to the product and the product in the same category.

According to an embodiment of the present invention, in determining the similarity between a product and a product, the above query exists, and the connection path for the query is also expressed as ppXXXX-X. In particular, although ppXXXX-X may be used as the connection path between products and products, a combination of the pmXXXX-X path, which represents the path related to similarity between product and customer, and the mpXXXX-X path, represents the connection path related to similarity between customers and products. You can use That is, it is also possible to determine the similarity between a product and a product using pm0000-1 + mp0000-1. In addition, it is possible to determine the similarity between a product and a product by using three or more similarity-related connection paths, such as using pm0000-1 + mm0100-1 + mp0000-1.

According to an embodiment of the present invention, when using any of the paths of (i) ppXXXX-X and (ii) pmXXXX-X + mmXXXX-X + mpXXXX-X, it is also possible to recommend an optimal product. The accuracy can be improved by learning while changing the parameters (including weights) associated with the path using the artificial intelligence learning model of.

11 is a diagram illustrating a result of implementing a product recommendation method using a graph database according to an embodiment of the present invention in a web page.

Referring to FIG. 11, the device enables the above algorithm to be executed by accessing it using a REST API. The device designates which logic (relationship) to apply using a URL (Uniform Resource Locator). In the embodiment of FIG. 11, pp0100 is designated to derive other products purchased by members who have purchased the product.

In the URL path, a query parameter such as pp0100 may be followed by a query parameter, which may include a central product number, the number of data to be returned, and a member ID. That is, only customer or product information can be used according to the recommendation logic. Additional parameters such as product code and customer number are transmitted in a URL encoding method.

In response to these queries, as query output, the number of data (n), product or member (node), importance (hits: number of paths retrieved), and cryptographic queries (Cypher query for debugging purposes) will be provided. I can.

The device may return the number of products of the top n (n=5, ie, 5) and the number of paths for each product in the order of high similarity, that is, the number of connection paths.

In the case of using the product purchase history, the time to grasp the customer's preference may be delayed, and in the case of purchase, it is difficult to see statistical relevance because there is not enough data, so that the user recommends the product using the product search history. Can be considered.

12 is a flowchart illustrating a product recommendation method using a graph database according to another embodiment of the present invention.

Referring to FIG. 12, an apparatus (server) collects inquiry history data related to product inquiry from a server that manages shopping malls and/or shopping malls (S1210). This can be achieved by extracting the user's product information inquiry history from the web log file. The inquiry history can be converted into sequential product codes and converted into data. For example, after viewing product 1, inquiring product 2, and inquiring product 4, the inquiry history data is generated by converting the product code into a product code by considering product 1 and logging out from the logout history.

Then, the device converts the inquiry history data into a graph form (S1212) and stores it in the database (S1214). In this case, the structure of the graph may have a structure of “session-page-product” so as to correspond to the preceding “product-order-customer”.

The client (user terminal) may input a request to search for a recommended product (S1216). This can be accomplished using a URL, as described in FIG. 11.

The device extracts the searched product within the range of x web pages before or after searching for a specific product (S1218). The x value can be determined by user settings and can be arbitrarily changed.

The device may recommend to the buyer in the order of the products with the highest number of views among the searched products (S1220). The client may receive and output the recommended product information (S1222).

13A and 13B are diagrams illustrating exemplary product path prediction in a product recommendation method using a graph database according to another embodiment of the present invention.

13A and 13B, when the number of recommended products n is set to 10, and when there is a request for recommended products, the device generates a graph formed by at least three node hierarchies. This may include a web page 1320 (a second node layer) searched by a user centering on the session 1330 as a first node layer and a product 1310 (third node layer) searched on the web page. The node layer may be changed so that the product is the first node layer, the page is the second node layer, and the session is the third node layer.

The device connects the web page 1320 associated with the specific session 1330 and configures the product 1310 searched in the web page 1320 to be connected to the web page 1320. In addition, it is preferable to construct a graph by using an arrow (→) instead of a connecting line (-) formed in a straight line to identify the time-series order by separating the preceding operation and the subsequent operation.

After constructing the graph, the device searches and recommends 10 products searched within the range of 3 pages in the order of the highest number of views after product 24522 (1310) search. Contents related to the following three web pages will be described with reference to FIGS. 14A and 14B.

14A and 14B are diagrams illustrating exemplary product path prediction in a situation in which a next product is inquired after product inquiry in the embodiment of FIG. 13A.

14A and 14B, the device uses a web page 1422 after a customer inquires a product 24522 (not shown) and then a product 25799 (1410) through a web page 1420. The fact of inquiring the product 25622 (1412) can be confirmed based on the converted graph data. In this way, after a specific product, 10 products searched within the range of a specific number of pages (which may be 3 pages in this embodiment) are searched and returned in the order of the number of views. Can process requests from However, in this case, the three pages may be set to be a page in which product search is performed. That is, it is preferable to exclude a web page without product search from calculating the number of page strokes, and to recommend a higher product that has been viewed a lot within the range of the page by calculating three pages based on the page on which the product search was performed.

According to another embodiment of the present invention, it is not necessary to recommend a product by limiting only to the page, but by limiting the session, it is possible to recommend a product based on the number of views of products made within three sessions. Further, by using the category identifier of the product recommended due to the high number of views, it is possible to recommend only products in a contending category or a product in a similar category. The use of similar or contention categories and the use of classes can also be optimized using the artificial intelligence model of FIG. 8. The learning data set at this time may be a set of inquiry data 1 that is a target of a search request and inquiry data 2 related to a product that has been actually purchased through a graph-based product recommendation process according to an embodiment of the present invention.

In an embodiment of the present invention, the apparatus may mix and use a first graph database based on product purchase history data and a second graph database based on product inquiry history data. For example, after searching for the top 10 products with many views using the second graph database, the degree of similarity between the 10 recommendation candidate products and the requested product may be calculated using the first graph database. Thus, it is possible to change the order of recommendation of 10 recommendation candidates based on products with high similarity using the first graph database. The opposite is also possible.

In addition, a weight of 1 is given to the recommended product candidate calculated based on the first graph database, and a weight 2 is assigned to the recommended product candidate calculated based on the second graph database to finalize the product with the highest similarity based on the weight. You can consider how to select a candidate.

How to recommend a product basket

15 is a conceptual diagram illustrating a situation in which a promotion is required for products whose sales are poor according to product sales activity analysis.

Referring to FIG. 15, the apparatus performs promotion for some of 10 products (products with an activity lower than 0.2) (products in lagger stage) with low sales by analyzing the sales volume of the product and the sales activity of the product. May be required. That is, when four out of 10 low-selling products are selected to be promoted, the product basket recommendation method according to an embodiment of the present invention may be used.

16A and 16B are conceptual diagrams illustrating a matching method for providing a special promotion by matching a specific number of customer groups and a specific number of product groups when there are 5 customers and 6 products.

Referring to FIG. 16A, there are 5 customers and 6 products. When 3 products are to be promoted by 3 customers, a decision to optimize saleability is required. For this, the device can perform an optimized promotion through two methods.

(1) The first method is a method of matching multiple product groups and multiple customer groups by first selecting the best product group for 5 out of 6 products and secondarily selecting the best customer for the selected product (see Fig. 18). .

(2) The second method is a method of matching multiple product groups and multiple customer groups by first selecting the best customer group for 6 products to 5 customers, and secondly selecting the best product for the selected customers (Fig. 20). Reference).

The apparatus performs a determination to optimize the saleability using at least one of the above two methods, and by the determination, the promotion may proceed in a state as shown in FIG. 16B.

Referring to FIG. 16B, the device promotes a package consisting of product 2, product 3, and product 6 to customer 1, customer 3, and customer 4. Promotion can be conducted in the form of offering a special discount only to the customer of the product included in the package. Alternatively, a product of a corresponding package may be displayed in a pop-up manner on the first page of a shopping mall, or package products may be arranged in some of the most prominent areas to induce sales.

17A and 17B are flowcharts illustrating a method of recommending a product basket according to another embodiment of the present invention.

Referring to FIGS. 17A and 17B, the apparatus follows the following procedure when selecting and promoting a customer to be recommended for each of a plurality of products (eg, 10 products).

First, the device acquires product and customer data that are candidates for promotion (S1710). In the embodiment of FIG. 17B, it is assumed that there are 10 product candidates and about 100 customer candidates, and data for this is obtained. At this time, in the customer candidate, buyers who have previously purchased for a specific product are excluded or data is set to zero. It is preferable that the collected data take the form of a matrix. At this time, products are formed in rows and customers are formed in columns. Of course, the opposite is okay. In addition, in the blank column of the matrix, an index related to the purchaseability of individual products and individual customers is input. The index related to the purchaseability may be an index calculated based on product image analysis, product attribute analysis, and propensity according to customer purchase history. Alternatively, a similarity degree based on a graph database according to an embodiment of the present invention may be used as the index. In the embodiment of FIG. 17B, the degree of similarity calculated as the number of connection paths using a graph database is used as the purchaseability index.

After acquiring data and generating a matrix, the device calculates the total number of connection paths for each product (S1720), and selects high-ranking products with a large number of connection paths (S1730). The number of products selected depends on the number of products subject to promotion. This is a situation in which a user (eg, a shopping mall operator) can select through a user interface. Alternatively, a specific number of products may not be set as a reference value, and all products having a number of connection paths greater than or equal to the threshold value may be selected. For example, products having a connection path greater than the number of 700 connection paths may be selected without limiting the number. A small number of connection paths may be judged to be less relevant. In this case, it is possible to select one with a smaller number of connection paths and process in the direction of offering more discounts. In the embodiment of Fig. 17B, the device selects the top four products with a high number of connection paths.

The device reorganizes the matrix into a new matrix by leaving only the top four selected products (which can be changed through user settings), deleting the remaining products (S1740). Then, for the remaining 4 products, 6 customers with a high total connection path are selected as target customers for promotion. The device promotes the selected four product packages to six customers of the promotion target customer soldier (S1750).

18 is a flowchart illustrating a product-based basket recommendation method according to another embodiment of the present invention. This relates to method (1) of FIG. 16A.

Referring to FIG. 18, after acquiring data on a product to be a promotion candidate and a purchaseability index associated with a customer, the device generates a matrix in which the product is a column and the customer is a row (S1810). In the blanks of the matrix, a purchaseability index between product and customer is entered.

Then, the device calculates the sum of purchase possibilities for each row (S1820). Then, the products are ranked in the order of the highest sum of purchase possibilities, as many as the number of products to be recommended by the promotion, the products with the highest total order of purchase possibilities are selected, and the remaining products are deleted to reconstruct the matrix (S1830).

After the product is selected, the device calculates the individual subtotal for each row (S1840). Then, after ranking the subtotals for each customer in a large order, customers with a higher purchaseability subtotal ranking as many as the number of customers to be promoted are selected and the remaining customers are excluded (S1850).

When the selection of the customer group and the product group is completed, the device forms the product group as a package targeting the customer group and performs promotion (S1860).

19A to 19C are tables for explaining in detail a method of generating a matrix for each step of the basket recommendation method of FIG. 18.

Referring to FIG. 19A, the apparatus generates a matrix by configuring customers as rows and products as columns based on purchaseability indicators for customers 1 to 5 and products 1 to 6.

Referring to FIG. 19B, the device ranks a subtotal for each product through the summation of rows. According to the embodiment of FIG. 19B, product 2 is ranked first with a value of 69, product 3 is ranked second with a value of 55, and product 6 is ranked third with a value of 53.

Referring to FIG. 19C, the top three products (product 2, product 3, and product 6) are selected, and a subtotal for each customer is calculated by adding up columns. Then, rank them. According to the embodiment of FIG. 19C, customer 1 is ranked first with a value of 48, customer 3 is ranked second with a value of 43, and customer 4 is ranked third with a value of 33.

Accordingly, the device selects customer 1, customer 3, and customer 4 as target customers for promotion, and selects product 2, product 3, and product 6 as promotion target products to perform promotion.

20 is a flowchart illustrating a method for recommending a customer-based basket according to another embodiment of the present invention. This relates to method (2) of FIG. 16A.

Referring to FIG. 20, after acquiring data on a product to be a promotion candidate and a purchaseability index associated with a customer, the apparatus generates a matrix in which the product is a column and the customer is a row (S2010). In the blanks of the matrix, a purchaseability index between product and customer is entered.

Then, the device calculates the sum of purchase possibilities for each column (S2020). Then, the customers are ranked in the order of the highest total purchase probability, and as many as the number of customers to be recommended, customers with a higher total purchase probability are selected, and the remaining customers are deleted to reconstruct the matrix (S2030).

After the customer is selected, the device calculates a subtotal for each product for each row (S2040). Then, after ranking the subtotals for each product in a large order, a product having a higher purchaseability subtotal ranking as many as the number of products to be promoted is selected and the remaining products are excluded (S2050).

When the selection of the customer group and the product group is completed, the device forms the product group as a package targeting the customer group and performs promotion (S2060).

21A to 21C are tables for explaining in detail a method of generating a matrix for each step of the basket recommendation method of FIG. 20.

Referring to FIG. 21A, the apparatus generates a matrix by configuring customers as rows and products as columns based on purchaseability indicators for customers 1 to 5 and products 1 to 6.

Referring to FIG. 21B, the device ranks by calculating a subtotal for each customer through the summation of columns. According to the embodiment of FIG. 21B, customer 1 is ranked first with a value of 77, customer 3 is ranked second with a value of 73, and customer 4 is ranked third with a value of 72.

Referring to FIG. 21C, the top three customers (customer 1, customer 3, and customer 4) are selected, and a subtotal for each product is calculated by adding up the rows. Then, rank them. According to the embodiment of FIG. 21C, product 2 is ranked first with a value of 47, product 3 is ranked second with a value of 41, and product 5 is ranked third with a value of 37.

Accordingly, the device selects customer 1, customer 3, and customer 4 as target customers for promotion, and selects product 2, product 3, and product 5 as promotion target products to perform promotion.

According to the product-based recommendation method according to the exemplary embodiment of FIG. 17 and the customer-based recommendation method according to the exemplary embodiment of FIG. 19, the promotional customer is the same but the product is different. As such, depending on which method you use, the results can be completely different.

22 is a block diagram schematically illustrating an apparatus using a product recommendation method based on a graph database and a product basket recommendation method according to an embodiment of the present invention. As shown in FIG. 22, the product recommendation device according to an embodiment of the present invention may include a communication unit 2210, a display unit 2220, a processor 2230, a memory 2240, and an input unit 2250. .

Referring to FIG. 22, the communication unit 2210 is a component for communication with other devices by being connected to a network. The communication unit 2210 may be implemented with an antenna, a communication related chip, or the like.

The display unit 2220 is a component that displays information input/output through the communication unit 2210 and information processed through the processor 2230. This can be implemented with one of a monitor, a TV and other panels.

The processor 2230 is a component that executes a graph-based similarity determination and product basket recommendation algorithm according to an embodiment of the present invention. This can be implemented with a microprocessor. Processor 2230 converts the data in graph form based on order history data and inquiry history data input through the communication unit 2210, and uses the data in graph form converted in response to a query for similar products from a client. This returns information on similar products and similar customers. At this time, products and/or customers may be sorted and returned in the order of the highest similarity based on the number of connection paths from the first node to the second node (this may be limited to having a certain range of distances). .

Also, the processor 2230 may sort and return products in the order of the most searched products in pages within a certain range before or after the first product search. The returned result data may be displayed through the display unit 2220 or may be provided to a terminal requested through the communication unit 2210.

The processor 2230 obtains graph-based similarity information or other similarity index-based information on purchaseability indexes of individual products and individual customers, and generates a matrix in which the customer and product are each column and row. Then, by calculating a subtotal of rows and columns of the generated matrix, ranking the calculated values, and generating an optimal product package for a specific customer group, appropriate promotion may be performed.

According to an embodiment of the present invention, the processor 2230 includes an artificial intelligence model learning module (not shown) and an artificial intelligence model execution module (not shown), and provides an artificial intelligence model for increasing the accuracy of graph-based similarity determination. It is possible to improve the accuracy of product recommendation by training and executing the learned artificial intelligence model.

The memory 2240 is a storage for storing instructions of a program executed by the processor 2230. The memory 2240 stores information received or transmitted through the communication unit 2210. Also, at least some of the information processed by the processor 2230 may be stored.

The input unit 2250 is a component that receives a user's input and may be implemented as a keyboard, a mouse, a touch panel, or the like. Various parameters related to optimal product recommendation may be directly input through the input unit 2250.

The system or device described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the systems, devices, and components described in the embodiments include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA). ), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions, such as one or more general purpose computers or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, those of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to behave as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. , Or may be permanently or temporarily embodyed in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiments may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of the program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

As described above, although the embodiments have been described by the limited embodiments and drawings, various modifications and variations are possible from the above description by those of ordinary skill in the art. For example, the described techniques are performed in a different order from the described method, and/or components such as a system, structure, device, circuit, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and claims and equivalents fall within the scope of the claims to be described later.

Claims (18)

In a product recommendation method between a group customer and a group product in an e-commerce system of a product recommendation device,
Receiving data of a plurality of products available for purchase in a plurality of online shopping malls and a plurality of customers associated with purchases through the online shopping mall;
Obtaining an individual product and a purchaseability index of an individual customer based on the received data;
Generating a matrix by configuring at least two or more of the plurality of products as a column and at least two or more of the plurality of customers as a row, and some of the plurality of customers are the plurality of products Has a relationship with some of them, and the content included in the blanks of the matrix is a purchaseability index between products and customers;
For each row of the matrix, calculating a first subtotal of at least two or more customers' purchaseability indicators;
Ranking the calculated first subtotals;
Selecting products having a higher first subtotal ranking by the number of first products;
Calculating a second subtotal for each customer for the selected products for individual columns of the matrix;
Ranking the calculated second subtotals;
Selecting customers having a higher second subtotal ranking by the number of first customers;
A product recommendation method between a group customer and a group product in an e-commerce system comprising the step of performing a promotion on the selected products to the selected customers using an online shopping mall. The method of claim 1,
Selecting a product having a high first subtotal ranking as much as the number of first products includes reconfiguring a matrix by selecting a product having a high first subtotal ranking and deleting the remaining products,
The step of selecting a customer with a higher second subtotal ranking as much as the number of first customers includes selecting a customer with a higher second subtotal ranking and deleting the remaining customers, between a group customer and a group product in an e-commerce system. How to recommend products. The method of claim 1,
The purchaseability index includes the number of connection paths in graph-type data consisting of a product node hierarchy, an order node hierarchy, and a customer node hierarchy, wherein the product recommendation method between a group customer and a group product in an e-commerce system. The method of claim 3, wherein the data in the form of a graph
At least one second node of the second node layer is connected around the first node of the first node layer; And,
As a third node of a third node hierarchy is connected to the at least one second node, a product recommendation method between a group customer and a group product in an e-commerce system is generated in the form of a radial graph. The method of claim 3,
Group customers and groups in an e-commerce system using the number of connection paths to which at least two of the nodes of the at least one product node layer and nodes of the at least one customer node layer can be connected as the purchaseability index How to recommend products between products. The method of claim 1,
The purchaseability index is a value calculated using a similarity-based product recommendation algorithm, a method for recommending a product between a group customer and a group product in an e-commerce system. The method of claim 1,
Promoting the selected products to the selected customers using an online shopping mall,
And arranging and displaying the selected products in a first area of a first web page of the online shopping mall to the selected customers. The method of claim 1,
Promoting the selected products to the selected customers using an online shopping mall,
A method of recommending a product between a group customer and a group product in an e-commerce system, comprising the step of discounting and selling the selected products only to the selected customers. In the product recommendation device in an e-commerce system,
A communication unit that communicates with at least one of wired and wireless networks;
Through the communication unit, a plurality of products available for purchase in a plurality of online shopping malls and data of a plurality of customers related to purchase through the online shopping mall are received, and a purchase possibility index of individual products and individual customers is calculated based on the received data. Obtained, and a matrix consisting of at least two or more of the plurality of products as a column and at least two or more of the plurality of customers as a row-Some of the plurality of customers are among the plurality of products It has a relationship with a part, and the content included in the blank space of the matrix is a purchaseability index between the product and the customer-and for each row of the matrix, the first subsum of the purchaseability index of at least two customers Calculate and rank products, select products with a first subtotal ranking as high as the number of first products, calculate and rank a second subtotal for each customer for the selected products with respect to individual columns of the matrix, Group customers in an e-commerce system including a processor for performing promotions using an online shopping mall for the selected products for the selected customers by selecting customers with a second subtotal ranking as high as the number of customers A device for recommending a product between a group and a group product. In a product recommendation method between a group customer and a group product in an e-commerce system of a product recommendation device,
Receiving data of a plurality of products available for purchase in a plurality of online shopping malls and a plurality of customers associated with purchases through the online shopping mall;
Obtaining an individual product and a purchaseability index of an individual customer based on the received data;
Generating a matrix by configuring at least two or more of the plurality of products as a column and at least two or more of the plurality of customers as a row, and some of the plurality of customers are the plurality of products Has a relationship with some of them, and the content included in the blanks of the matrix is a purchaseability index between products and customers;
For each column of the matrix, calculating a first subtotal of a purchaseability index for at least two products;
Ranking products having the calculated first subtotal value;
Selecting customers having a higher first subtotal ranking by the number of first customers;
Calculating a second subtotal for each product for the selected customers for individual columns of the matrix;
Ranking the calculated second subtotals;
Selecting products having a higher second subtotal ranking by the number of first products;
A product recommendation method between a group customer and a group product in an e-commerce system comprising the step of performing a promotion on the selected products for the selected customers using an online shopping mall. The method of claim 10,
The selecting of customers having a higher first subtotal ranking as many as the number of first customers comprises reconfiguring a matrix by selecting customers with a higher first subtotal ranking and deleting the remaining products,
The step of selecting the products with the second subtotal ranking as high as the number of the first products includes selecting products with the second subtotal ranking and deleting the remaining products, between a group customer and a group product in an e-commerce system. How to recommend products. The method of claim 10,
The purchaseability index includes the number of connection paths in graph-type data consisting of a product node hierarchy, an order node hierarchy, and a customer node hierarchy, wherein the product recommendation method between a group customer and a group product in an e-commerce system. The method of claim 12, wherein the data in the form of a graph
At least one second node of the second node layer is connected around the first node of the first node layer; And,
As a third node of a third node hierarchy is connected to the at least one second node, a product recommendation method between a group customer and a group product in an e-commerce system is generated in the form of a radial graph. The method of claim 12,
Group customers and groups in an e-commerce system using the number of connection paths to which at least two of the nodes of the at least one product node layer and nodes of the at least one customer node layer can be connected as the purchaseability index How to recommend products between products. The method of claim 10,
The purchaseability index is a value calculated using a similarity-based product recommendation algorithm, a method for recommending a product between a group customer and a group product in an e-commerce system. The method of claim 10,
Promoting the selected products to the selected customers using an online shopping mall,
And arranging and displaying the selected products in a first area of a first web page of the online shopping mall to the selected customers. The method of claim 10,
Promoting the selected products to the selected customers using an online shopping mall,
A method of recommending a product between a group customer and a group product in an e-commerce system, comprising the step of discounting and selling the selected products only to the selected customers. In the product recommendation device in an e-commerce system,
A communication unit that communicates with at least one of wired and wireless networks;
Through the communication unit, a plurality of products available for purchase in a plurality of online shopping malls and data of a plurality of customers related to purchase through the online shopping mall are received, and a purchase possibility index of individual products and individual customers is calculated based on the received data. Obtained, and a matrix consisting of at least two or more of the plurality of products as a column and at least two or more of the plurality of customers as a row-Some of the plurality of customers are among the plurality of products It has a relationship with a part, and the content included in the blank space of the matrix is the purchaseability index between the product and the customer-and, for each column of the matrix, the first subsum of the purchaseability index of at least two product customers Calculate and rank, select customers with a first subtotal ranking as high as the number of first customers, calculate and rank a second subtotal for each product for the selected customers for each row of the matrix, and rank the first product Group customers in an e-commerce system including a processor for performing promotions using an online shopping mall for the selected products targeting the selected customers by selecting products with a higher second subtotal ranking by the number of Product recommendation device between group products.

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