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Recommendation method based on user cluster

A technology of user clustering and recommendation methods, applied in the field of recommendation based on user clustering, can solve problems such as not fully considering user needs

Inactive Publication Date: 2015-01-07
WUHAN UNIV OF SCI & TECH
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  • Summary
  • Abstract
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AI Technical Summary

Problems solved by technology

The collaborative filtering recommendation algorithm was first proposed by Goldberg et al., but the system did not fully consider the needs of users and had certain defects.

Method used

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  • Recommendation method based on user cluster
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  • Recommendation method based on user cluster

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Embodiment Construction

[0052] The present invention provides a user clustering-based recommendation method (CCVR), which mainly aims to solve how to make recommendations, and proposes to make effective recommendations by considering how to predict which tags users are interested in.

[0053] First of all, to make a recommendation, you need to select certain attributes for recommendation, whether it is based on relationships, friends, or interests. Since it is hoped that interested content can be recommended to users, the present invention selects interest attributes for recommendation. However, how to obtain the user's interest is the first problem to be solved.

[0054] Secondly, after obtaining the user's interest, what mechanism is used to recommend it, which is the second problem to be solved by the present invention. Because after the user's interest is collected, the interest value of each user is quantified into a specific value, and these values ​​relatively reflect the user's interest in e...

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Abstract

The invention provides a recommendation method based on a user cluster. In order to conduct effective recommendation on users reasonably according to user interest, the interest degree is acquired according to total browsing frequency, browsing time, total browsing time, effectively browsing efficiency and effective browsing time of subject labels of the users to form a user interest characteristic vector; core users are screened according to the user interest characteristic vector to form a core user set, and a K-means cluster algorithm is utilized to cluster all the users; after all user clusters are obtained, the class interest vector of each user cluster on each subject is calculated; the interest value and the class interest vector are compared to conduct recommendation. The CCVR method is better in recommendation effect compared with other recommendation methods, and the method has good accuracy.

Description

technical field [0001] The invention relates to the field of Internet information technology, in particular to a recommendation method based on user clustering. Background technique [0002] With the popularity of Internet users, social networks have gradually replaced traditional information acquisition channels, such as newspapers, magazines, and TV news, and have grown into a way for most people to receive information immediately. For example, facebook, twitter in foreign countries, Weibo in China, Renren and so on. Everyone releases the information they want to express by sending messages and statuses, and spreads the information they get from others by forwarding and sharing other people's messages and statuses. This involves the issue of node influence, that is, a node that is followed by everyone can see the information published by everyone, and a node that follows everyone can see the information published by everyone. Of course, personal energy is limited, and it...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/35G06F16/355G06F16/951G06F16/9535
Inventor 李鹏王娅丹金瑜刘璟刘欣
Owner WUHAN UNIV OF SCI & TECH
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