集成社会化标签和用户背景信息的协同过滤推荐方法

doi:10.11772/j.issn.1001-9081.2014.08.2328

摘要
图/表
参考文献(0)
相关文章 (15)

全文: PDF (617 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要

针对传统的协同推荐算法存在数据稀疏和推荐精度低的问题，提出了一种集成社会化标签和用户背景信息的协同过滤(CF)推荐方法。首先，分别计算基于社会化标签和用户背景信息的用户间的相似度；然后，基于用户评分计算用户间的相似度；最后，集成上述3种相似性度量产生用户间综合相似度，并对目标用户进行项目推荐。实验结果表明，与传统的协同过滤推荐算法相比，所提方法在正常数据集和冷启动数据集下的平均绝对误差(MAE)平均降低了16%和22.6%。该方法不仅能有效地提高推荐算法的精度，而且能较好地解决数据稀疏和冷启动的问题。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	蒋胜
	王忠群
	修宇
	皇苏斌

Abstract：

To address the difficulty of data sparsity and lower recommendation precision in the traditional Collaborative Filtering (CF) recommendation algorithm, a new CF recommendation method of integrating social tags and users background information was proposed in this paper. Firstly, the similarities of different social tags and different users background information were calculated respectively. Secondly, the similarities of different users ratings were calculated. Finally, these three similarities were integrated to generate the integrated similarity between users and undertook the recommendations about items for target users. The experimental results show that, compared with the traditional CF recommendation algorithm, the Mean Absolute Error (MAE) of the proposed algorithm respectively reduces by 16% and 22.6% in the normal dataset and cold-start dataset. The new method can not only improve the accuracy of recommendation algorithm, but also solve the problems of data sparsity and cold-start.

收稿日期: 2014-03-06 出版日期: 2014-08-10

基金资助:

国家自然科学基金资助项目;教育部人文社科规划项目

通讯作者: 蒋胜 E-mail: jiangsheng3@163.com

作者简介: 蒋胜（1991-），男，安徽滁州人，硕士研究生，主要研究方向：电子商务；王忠群（1965-），男，安徽芜湖人，教授，主要研究方向：信息管理与信息系统、软件工程、工作流；修宇（1976-），男，安徽芜湖人，讲师，主要研究方向：数据挖掘、机器学习。

引用本文:

蒋胜王忠群修宇皇苏斌. 集成社会化标签和用户背景信息的协同过滤推荐方法[J]. 计算机应用, 2014, 34(8): 2328-2331. JIANG Sheng WANG Zhong-qun XIU Yu HUANG Subin. Collaborative filtering recommendation method of integrating social tags and users background information. Journal of Computer Applications, 2014, 34(8): 2328-2331.

链接本文:

http://www.joca.cn/CN/10.11772/j.issn.1001-9081.2014.08.2328 或 http://www.joca.cn/CN/Y2014/V34/I8/2328

［1］	HUANG L, LI D. A review of information recommendation in social media ［J］. CAAI Transactions on Intelligent Systems, 2012, 7(1): 1-8.(黄立威,李德毅.社交媒体中的信息推荐［J］.智能系统学报,2012,7(1):1-8.)
［2］	RICCI F, ROKACH I, SHAPIRA B, et al. Recommender systems handbook ［M］. Berlin: Springer, 2011: 145-186.
［3］	YU H, LI Z. A collaborative filtering recommendation algorithm based on forgetting curve ［J］. Journal of Nanjing University: Natural Sciences, 2010, 46(5): 520-527.(于洪,李转运.基于遗忘曲线的协同过滤推荐算法［J］.南京大学学报:自然科学版，2010,46(5):520-527.)
［4］	FENG Y, LI J, XU H, et al. Collaborative recommendation method improvement based on social network analysis ［J］. Journal of Computer Applications, 2013, 33(3): 841-844.(冯勇,李军平,徐红艳,等.基于社会网络分析的协同过滤推荐方法改进［J］.计算机应用,2013,33(3):841-844.)
［5］	WANG H, MU Y, YU X. Resource recommendation in social tagging system based on collaborative matrix factorization ［J］. Application Research of Computers, 2013, 30(6): 1739-1741.(王海雷,牟雁超,俞学宁.基于协同矩阵分解的社会化标签系统的资源推荐［J］.计算机应用研究,2013,30(6):1739-1741.)
［6］	SYMEONIDIS P, TIAKAS E, MANOLOPOULOS Y. Product recommendation and rating prediction based on multi-modal social networks ［C］// Proceedings of the 5th ACM Conference on Recommender Systems. New York: ACM Press, 2011: 61-68.
［7］	JIA D, ZENG C, PENG Z, et al. A user preference based automatic potential group generation method for social media sharing and recommendation ［J］. Chinese Journal of Computers, 2012, 35(11): 2381-2391.(贾大文,曾承,彭智勇,等.一种基于用户偏好自动分类的社会媒体共享和推荐方法［J］.计算机学报,2012,35(11):2381-2391.)
［8］	ZHAO Y, DONG J, DONG J. Tag recommendation for blogs based on social tagging ［J］. Computer Engineering and Design, 2012, 33(12): 4609-4613.(赵亚楠,董晶,董佳亮.基于社会化标注的博客标签推荐方法［J］.计算机工程与设计,2012,33(12):4609-4613.)
［9］	JIA D, ZHANG F. A collaborative filtering recommendation algorithm based on double neighbor choosing strategy ［J］. Journal of Computer Research and Development, 2013, 50(5): 1076-1084.(贾冬艳,张付志.基于双重邻居选取策略的协同过滤推荐算法［J］.计算机研究与发展,2013,50(5):1076-1084.)
［10］	ZHUANG J, WANG M, YE M. Agriculture information recom-mendation system based on content filtering ［J］. Computer Engineering, 2012, 38(11): 38-41.(庄景明,王明文,叶茂盛.基于内容过滤的农业信息推荐系统［J］.计算机工程,2012,38(11):38-41.)
［11］	XIA N, SU Y, QIN H, et al. Method for personalized user profiling in social tagging systems ［J］. Journal of Computer Applications, 2011, 31(6): 1667-1670.(夏宁霞,苏一丹,覃华,等.社会化标签系统中个性化的用户建模方法［J］.计算机应用,2011,31(6):1667-1670.)
［12］	ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions ［J］. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.