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Commodity recommendation method, device, medium and equipment

A product recommendation and customer technology, applied in business, character and pattern recognition, instruments, etc., can solve the problems of personalized customization, inability to combine customers, unclear target user groups, etc., and achieve the effect of high accuracy

Pending Publication Date: 2021-11-30
CHINA CONSTRUCTION BANK
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing product push methods generally simply push by obtaining the user's daily browsing records or purchase history, without personalized customization for the user's consumption habits and different customer groups, making the accuracy of the push information low
[0005] Under the existing technology, commodity recommendation generally has the recommendation of hot-selling commodities or a single product-related recommendation. The existing problems are that the target user group is not clear, and it is impossible to make differentiated product recommendations for different customer groups, which reduces the product quality. Recommended Accuracy
Or use technical means to classify customer groups, but cannot effectively combine with customers' consumption habits, and cannot achieve higher-precision classification results, so they cannot meet the above needs

Method used

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  • Commodity recommendation method, device, medium and equipment
  • Commodity recommendation method, device, medium and equipment
  • Commodity recommendation method, device, medium and equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0091] figure 1 It is a flow chart of a product recommendation method in an embodiment of the present invention. Such as figure 1 As shown, the method includes the following steps:

[0092] S110: Preprocessing the behavior characteristic information and consumption habit data of each of the multiple customers to obtain multiple ordered trees corresponding to the multiple customers one by one;

[0093] S120: Correspondingly determine the similarity of every two customers in the multiple customers according to the similarity of every two ordered trees in the multiple ordered trees;

[0094] S130: classify the multiple customers according to the similarity of every two customers among the multiple customers and a clustering algorithm, and divide the multiple customers into different customer groups;

[0095] S140: Perform differentiated product recommendations for different customer groups.

[0096] The above-mentioned technical scheme is described in detail below:

[0097] ...

Embodiment 2

[0156] Figure 10 It is a functional block diagram of a commodity recommendation device according to an embodiment of the present invention. Based on a similar inventive concept, the product recommendation device 200 includes:

[0157] The data preprocessing module 210 is used to preprocess the behavior characteristic information and consumption habit data of each customer among the multiple customers, and obtain multiple ordered trees corresponding to the multiple customers one by one;

[0158] The similarity calculation module 220 is used to determine the similarity of every two customers in the multiple customers according to the similarity of every two ordered trees in the multiple ordered trees;

[0159] The customer classification module 230 is used to classify multiple customers according to the similarity and clustering algorithm of every two customers in multiple customers, and divide multiple customers into different customer groups;

[0160] The product recommenda...

Embodiment 3

[0174] Figure 11 It is a functional block diagram of a computer-readable storage medium in an embodiment of the present invention. Such as Figure 11 As shown, a computer program 310 is stored in the computer-readable storage medium 300, and when the computer program 310 is executed by a processor, it realizes:

[0175] Preprocessing the behavioral feature information and consumption habit data of each of the multiple customers to obtain multiple ordered trees corresponding to the multiple customers one-to-one;

[0176] According to the similarity of every two ordered trees in the plurality of ordered trees, correspondingly determine the similarity of every two customers in the plurality of customers;

[0177] Classify the multiple customers according to the similarity and clustering algorithm of every two customers in the multiple customers, and divide the multiple customers into different customer groups;

[0178] Carry out differentiated product recommendations for the ...

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PUM

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Abstract

The embodiment of the invention provides a commodity recommendation method, a device, a medium and equipment. The method comprises the steps: carrying out the preprocessing of the behavior feature information and consumption habit data of each customer in a plurality of customers, and obtaining a plurality of ordered trees corresponding to the plurality of customers in a one-to-one manner; according to the similarity of every two ordered trees in the plurality of ordered trees, correspondingly determining the similarity of every two clients in the plurality of clients; classifying the plurality of clients according to the similarity of every two clients in the plurality of clients and a clustering algorithm, and dividing the plurality of clients into different client groups; and carrying out differentiated commodity recommendation on the different customer groups. According to the method, customer classification and commodity recommendation can be performed based on consumption habits, and the commodity recommendation accuracy can be improved.

Description

technical field [0001] The present invention relates to the field of data mining, in particular to a product recommendation method, device, medium and equipment. Background technique [0002] With the continuous development of science and technology, the acceleration of social informatization, and the continuous improvement of e-commerce trading platforms, more and more people use online shopping to obtain the commodities they need. There are many types. When shopping, users often hope to buy what they want in the shortest time. Merchants hope to sell as many of their products as possible. If the e-commerce platform can recommend the products that customers want most to Users can achieve the effect of killing two birds with one stone. [0003] In the course of realizing the present invention, the inventor finds that there are at least the following problems in the prior art: [0004] E-commerce platform users are widely distributed geographically, and their customer charac...

Claims

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Application Information

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IPC IPC(8): G06Q30/06G06K9/62G06F16/9535
CPCG06Q30/0631G06F16/9535G06F18/23G06F18/214
Inventor 邱韬奋罗恕人崔海波罗灿年陈浩然李丹刘晓宇钟思谋徐策
Owner CHINA CONSTRUCTION BANK