文章基本信息

标题：An Improved Collaborative Filtering Algorithm Based on User Interest
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作者：Zhang, Li ; Tao, Qin ; Teng, PiQiang 等
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2014
卷号：9
期号：4
页码：999-1006
DOI：10.4304/jsw.9.4.999-1006
语种：English
出版社：Academy Publisher
摘要：With the development of personalization services, collaborative filtering techniques have been successfully applied to the network recommendation system. But sparse data seriously affect the performance of collaborative filtering algorithms. To alleviate the impact of data sparseness, using user interest information, an improved user-based clustering Collaborative Filtering (CF) algorithm is proposed in this paper, which improves the algorithm in two ways: user similarity calculating method and user-item rating matrix extended. The experimental results show that the algorithm could describe the user similarity more accurately and alleviate the impact of data sparseness in collaborative filtering algorithm, which means it improve the accuracy of the collaborative recommendation algorithm.
关键词：collaborative filtering;data sparsity;user similarity;user interest