基于用户相似度的协同过滤推荐算法

doi:10.3969/j.issn.1000-436x.2014.02.003

Abstract

Abstract:

Collaborative filtering recommendation algorithms widely used in e-commerce, recommend interesting content for users from massive data resources by studying their preferences and interests. The focus of similarity and evaluation have been changed when applied to social networks, however, they cause low efficiency and accuracy of the recommen-dation algorithms. User similarity was introduced for redefining the attribute similarity and similarity composition as well as the method of similarity calculating, then a new collaborative filtering recommendation algorithm based on user attrib-utes was designed and some methods for user satisfaction and quality of recommendations were presented. The experi-mental result shows that the new algorithm can effectively improve the accuracy, quality and user satisfaction of recom-mendation system in social networks.

Key words: collaborative filtering, user similarity, attribute similarity, interactive similarity, user satisfaction

Hui-gui RONG,Sheng-xu HUO,Chun-hua HU,Jin-xia MO. User similarity-based collaborative filtering recommendation algorithm[J]. Journal on Communications, 2014, 35(2): 16-24.

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References 15

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数据类型	发信人	收信人	S+	R+	R+S+
相似用户集	7 789 252	5 785 978	616 568	224 066	9 680
孤立用户	61 024	114 044	35 808	50 939	4 916
相似度为平均值	127	507	18	47	2
相似度为1	980	957	3 218	3 059	2 931
相似度为2	1 032	1 046	2 837	2 753	992
相似度为3	971	1 081	2 513	2 550	404
相似度为3～50	27 748	32 426	27 347	32 854	993

User similarity-based collaborative filtering recommendation algorithm

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