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Method and device for classifying E-commerce customers

A customer and e-commerce technology, applied in the field of big data processing, can solve the problems of inaccurate customer classification results, difficulty in finding distance relationships, etc., to achieve the effect of comprehensive and accurate description, accurate classification, and maintenance of running time

Active Publication Date: 2015-07-15
BEIJING JINGDONG SHANGKE INFORMATION TECH CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The existing technology can discover the structural information contained in natural data through clustering technology, such as group distribution. However, there are still some problems in the traditional clustering method in practice.

Method used

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  • Method and device for classifying E-commerce customers
  • Method and device for classifying E-commerce customers
  • Method and device for classifying E-commerce customers

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0056] figure 2 It is a flowchart of a method for classifying e-commerce customers provided by Embodiment 1 of the present invention. This embodiment is applicable to e-commerce websites to classify customers. The method can be executed by a background server, and specifically includes the following steps:

[0057] Step 210: Establish a weighted network with the customer as the vertex and the product list of the product purchased by the customer as the edge according to the order data, wherein the product list includes the name and price of the product.

[0058] Select the order data within a certain period of time (such as one month), and according to the order data, each customer is regarded as a vertex, and an edge is established between the customer vertices who purchase the same product, and the edge represents the customer represented by the two vertices. A product list for purchasing the same product, wherein the product list includes the name and price of the product....

Embodiment 2

[0082] Figure 4 It is a schematic diagram of a classification method for e-commerce customers provided in Embodiment 2 of the present invention, as shown in Figure 4 As shown, a method for classifying e-commerce customers provided in this embodiment specifically includes the following steps:

[0083] Step 410, preprocessing.

[0084] According to the order data, the order record is converted into a customer-product network, which is an undirected weighted network with the customer as the vertex and the product list as the edge. First of all, according to the order data, the customers who purchase the same product are grouped into one group; for each customer in each group, a vertex is generated for each customer, and an undirected belt with the product name and price as the edge is established between two vertices. Side E, the undirected weighted side E is expressed as (x, y, a, b), where x and y represent the two customers, a is the price of the product, b is the name of ...

Embodiment 3

[0091] Embodiment 3 of the present invention provides a method for classifying e-commerce customers. In this embodiment, five customers are taken as an example to describe in detail, wherein, customer 1 purchased commodities A and B, and customer 2 purchased commodities A, B and C , customer 3 purchased items A, C, and D, customer 4 purchased items D and E, and customer 5 purchased item E.

[0092] Firstly, according to the order data, a weighted network is established with the customer as the vertex and the product list of the products purchased by the customer as the edges. According to the order data, customers who purchase the same product are grouped into one group; for customers in each group, each customer generates a vertex, and an undirected weighted edge with the product name and price as the edge is established between two vertices; Traverse all undirected weighted edges, merge vertices with the same edges, and obtain a weighted network with customers as vertices an...

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Abstract

The invention discloses a method and a device for classifying E-commerce customers. The method comprises steps as follows: a weighted network with the customers serving as vertices and a commodity list of commodities purchased by the customers serving as an edge is established according to order data, wherein the commodity list comprises names and prices of the commodities; a weighted label propagation algorithm is executed on the weighted network, a plurality of customer groups are obtained, and labels are defined for each customer group; each customer vertex in the plurality of customer groups is traversed, and a multi-label classification result of each customer is calculated according to the labels and the weights of all neighbor vertices of each customer vertex. According to the method and the device for classifying the E-commerce customers, a plurality of labels for the same customer can be generated, so that the customer description is more comprehensive and more accurate, the customers are accurately classified, meanwhile, the efficiency in the aspect of operation time is kept, and the method and the device can be applied to a larger scale of dataset.

Description

technical field [0001] Embodiments of the present invention relate to big data processing technology, and in particular to a method and device for classifying e-commerce customers. Background technique [0002] E-commerce websites generally have a large number of registered users. In order to provide better services to these customers, one of the needs of e-commerce companies is to be able to classify customers, that is, to put one or more labels on each customer according to business needs ( category). In the process of classifying customers, a common difficulty is how to establish a classification system so that it has sufficient differentiation and coverage for customer groups at the same time. At present, it is mainly based on the information provided by the collection of commodities purchased by customers to try to classify and label customers. [0003] In the prior art, customers are divided into several groups by clustering technology first, and then these groups ar...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06F17/30
Inventor 林熙东牟川
Owner BEIJING JINGDONG SHANGKE INFORMATION TECH CO LTD
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