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Method and system for individually recommending network commodities

A recommendation method and commodity technology, applied in special data processing applications, instruments, electrical and digital data processing, etc., can solve the problem of centralized recommendation results, and achieve the effect of improving efficiency, high accuracy, and good diversity

Active Publication Date: 2014-01-29
HANGZHOU NORMAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This kind of similarity-based method has a common problem, that is, the recommendation results will be relatively concentrated, and the result is that most users will be recommended some hot-selling products
Of course, recommending hot-selling products may have higher recommendation accuracy, because most people like hot-selling products

Method used

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  • Method and system for individually recommending network commodities
  • Method and system for individually recommending network commodities
  • Method and system for individually recommending network commodities

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Embodiment Construction

[0028] Refer to attached Figure 1-3 :

[0029] In order to facilitate problem description, preferred specific implementations of the present invention will be described below in conjunction with the accompanying drawings. However, it should be noted that in order to avoid diluting the subject matter of the present invention, known technologies and functions adopted in the system will be simplified or omitted in the corresponding description.

[0030] The invention discloses a method for personalized recommendation of network commodities, which comprises the following steps:

[0031] (1) According to the user's operation behavior on the user terminal, record the purchase relationship between the user and the product, and save the data in the database.

[0032] (2) According to the historical purchase relationship of all users and products stored in the database, a bipartite graph with users and products as nodes and their relationship as edges is established. If a user purc...

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Abstract

The invention discloses a method and a system for individually recommending network commodities. The method includes firstly, acquiring purchasing behavior historical data of users and creating user-commodity purchasing relational graphs on the basis of the purchasing behavior historical data; secondly, adding a virtual user who purchases all commodities in the past into each user-commodity purchasing relational graph; thirdly, describing preference degrees of the specific target recommendation users to the commodities and giving new energy values to all the commodities; fourthly, ranking the commodities which are not purchased by the users according to a high-low sequence of the energy values of the commodities to generate individual recommendation lists. Each list can contain one or a plurality of commodities.

Description

technical field [0001] The invention belongs to information personalized recommendation technology in the field of network data mining, and specifically relates to a method and system for personalized recommendation of network commodities. Background technique [0002] With the development and popularization of the Internet, e-commerce is developing at an incredible speed, greatly changing people's life style. According to authoritative statistics, the overall transaction scale of China's e-commerce market in 2012 increased by nearly 2 trillion yuan compared with 2011. In the next 3-5 years, China's e-commerce market will continue to maintain its growth momentum and is expected to exceed 10 trillion yuan in 2013. For many people, online shopping has become an important part of everyday spending. [0003] While e-commerce services improve people's lifestyles, the problem of product information overload is becoming more and more serious, which has become a major problem for ...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06F17/30
Inventor 吕琳媛周艳波
Owner HANGZHOU NORMAL UNIVERSITY
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