融合社交网络信息的协同过滤推荐算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201603010

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Abstract To solve the problems of high data sparsity and limited recommendation precision of collaborative filtering recommendation algorithms, a collaborative filtering algorithm incorporating social network information is proposed under the framework of item-based collaborative filtering recommendation. In item similarity calculation period and user rating prediction period, social network information is utilized to fill missing values in rating matrix selectively and thus the existing rating information is utilized as much as possible. Finally, experiment is conducted on Epinions dataset. Results show that the proposed algorithm alleviates the data sparsity problem and outperforms other collaborative filtering algorithms on rating error and precision.

Key words： Collaborative Filtering Social Network Missing Value Filling Data Sparsity

Received: 19 May 2015

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China (No.61573229,61432011,U1435212), Construction Project of Science and Technology Basic Condition Platform of Shanxi Province (No.2012091002-0101), Key Technology R&D Program of Shanxi Province (No.20110321027-01)

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	Articles by authors
	GUO Lanjie
	LIANG Jiye
	ZHAO Xingwang

Cite this article:

GUO Lanjie,LIANG Jiye,ZHAO Xingwang. Collaborative Filtering Recommendation Algorithm Incorporating Social Network Information[J]. , 2016, 29(3): 281-288.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201603010 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2016/V29/I3/281

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