User fuzzy similarity-based collaborative filtering recommendation algorithm

被引：1

作者：

Wu Y.-T. ^{[1
]}

Zhang X.-M. ^{[1
]}

Wang X.-M. ^{[1
]}

Li H. ^{[1
]}

机构：

[1] National Digital Switching System Engineering and Technological R&D Center, Zhengzhou

来源：

Tongxin Xuebao/Journal on Communications | 2016年 / 37卷 / 01期

关键词：

Collaborative filtering; Fuzzy distance; Fuzzy similarity; Trapezoid fuzzy model;

D O I：

10.11959/j.issn.1000-436x.2016024

中图分类号：

学科分类号：

摘要：

In order to reflect the actual case of human decisions and solve the data sparseness problem of traditional collaborative filtering recommendation algorithm, a trapezoid fuzzy model based on age fuzzy model was proposed. In this model, crisp point was fuzzified into trapezoid fuzzy number and the fuzziness and information of users' grade was taken into account when calculating user's similarity by trapezoid fuzzy number. Based on this model, the user fuzzy similarity-based collaborative filtering recommendation algorithm was designed. The algorithm was proved to be an extension of traditional collaborative filtering algorithm in fuzzy fields. The experimental results show that, the proposed algorithm performs better when implemented in the sparse dataset with more user than item, and its running time is much less than traditional collaborative filtering algorithm. © 2016, Editorial Board of Journal on Communications. All right reserved.

引用

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