Collaborative Filtering Recommendation Model Based on User's Credibility Clustering

被引：0

作者：

Zhao Xu ^{[1
]}

Qiao Fuqiang ^{[1
]}

机构：

[1] Tianjin Sino German Vocat Tech Coll, Tianjin, Peoples R China

来源：

PROCEEDINGS OF THIRTEENTH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS TO BUSINESS, ENGINEERING AND SCIENCE, (DCABES 2014) | 2014年

关键词：

Collaborative Filtering; User's Credibility; Dynamic Clustering;

D O I：

10.1109/DCABES.2014.51

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Aiming at the long response time, inaccurate recommendation and cold-start problems that faced by present recommendation algorithm, this paper, taking movie recommendation system as an example, proposes a collaborative filtering recommendation model based on user's credibility clustering. This model divides recommendation process into offline and online phases. Offline, it uses the result of user's credibility for clustering and then writes the clustered information into a table in database. Online, finds the cluster that target user belongs to and then gives recommendation. As a whole, the model reduces the response time, improves the accuracy of the recommendation rate, and solves the new user's cold-start problem.

引用

页码：234 / 238

页数：5

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