Commodity recommendation method based on group community

A product recommendation and product technology, applied in the field of product recommendation based on group community, can solve the problem of insufficient consideration of user interest in cold start, and achieve the effect of improving the quality of recommendation, reducing the amount of calculation, and alleviating the speed bottleneck.

Inactive Publication Date: 2018-01-26
HARBIN INST OF TECH
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Problems solved by technology

[0010] Traditional collaborative filtering recommendation algorithms have probl

Method used

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  • Commodity recommendation method based on group community
  • Commodity recommendation method based on group community
  • Commodity recommendation method based on group community

Examples

Experimental program
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Embodiment Construction

[0038] Such as figure 1 As shown, a commodity recommendation method based on group community includes the following steps:

[0039] S1. Analyze the user's historical shopping behavior, extract shopping operation information, and establish a user interest model;

[0040] S2. Based on the user's historical shopping behavior, perform a cluster analysis based on the user's interest in the extracted shopping operation information, and gather users with common preferences into a community;

[0041] S3. By analyzing the shopping behavior history of a large number of user groups, mining interest circles with similar interests, and recommending online products based on the interest circles;

[0042] S4. Through the user's shopping history, perform reverse commodity category analysis, and recommend products according to the category of the commodity.

[0043] In this embodiment, the specific operation steps of step S2 are:

[0044] S21. According to the extracted shopping operation i...

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Abstract

The invention discloses a commodity recommendation method based on group community. The method comprises the following steps: analyzing historical shopping behaviors of a user, extracting shopping operation information and establishing a user interest model; carrying out clustering analysis based on user interests on the extracted shopping operation information to enable the users having the sameinterests to be grouped into one community; analyzing shopping behavior history to explore interest circles with similar interests, and carrying out on-line commodity recommendation; and based on theuse shopping history, carrying out reverse commodity category analysis, and carrying out commodity recommendation according to the category of the commodities. The method realizes layer-by-layer deepanalysis from three layers, that is, the user layer, the commodity classification layer and the commodity category layer, so that a recommendation system can identify and recommend the favorite commodities of the users more and more accurately according to similarity features between the users, calculation amount is reduced greatly, and the problem of bottleneck of online recommendation speed canbe relieved effectively; and through the identified commodity community, associate shopping information can be identified more effectively, and recommendation quality is improved.

Description

technical field [0001] The invention relates to the technical field, in particular to a group community-based commodity recommendation method. Background technique [0002] With the rapid development of the information technology industry and related fields, more and more people use the Internet, and everyone's life is more and more closely connected with the Internet. The Internet has become an indispensable part of people's lives. In recent years, with the rise of Weibo, WeChat, e-commerce, and group-buying websites, people are more and more inclined to shop, entertain, and communicate on the Internet, making the relationship between people and the Internet more inseparable. Under this trend, more and more people choose to shop online. The "38th Statistical Report on Internet Development in China" released by China Internet Network Information Center (CNNIC) in Beijing shows: Statistical analysis of the development of China's online shopping , as of June 2016, the scale of...

Claims

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Application Information

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IPC IPC(8): G06Q30/02
Inventor 张玥张宏莉洪凯伦翟健宏张羽
Owner HARBIN INST OF TECH
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