Trust-based top-k item recommendation in social networks

被引:0
作者
Xing, Xing [1 ]
Zhang, Weishi [1 ]
Jia, Zhichun [1 ,2 ]
Zhang, Xiuguo [1 ]
Xu, Nan [1 ]
机构
[1] School of Information Science and Technology, Dalian Maritime University
[2] Department of Computer Science, University of New Mexico
来源
Journal of Information and Computational Science | 2013年 / 10卷 / 12期
关键词
Collaborative filtering; Latent factor model; Social recommendation; Trust model;
D O I
10.12733/jics20102112
中图分类号
学科分类号
摘要
Collaborative filtering based methods have a low performance in the context of social recommendation due to the data sparsity issue and not considering the social network information that can be exploited to improve the performance. Trust-based methods attempt to reduce the data sparsity by utilizing the social network information. However, most of these methods are based on the explicit trust statements expressed by users, which are not available in the social networks such as Sina Weibo. In this paper, we present a trust metric to quantitatively measure the recommendation trust between pairs of users by aggregating the implicit trust and trust propagation values. We propose a trust-based latent factor model, which incorporates the pairwise recommendation trust values into the probabilistic model for top-k item recommendation. The experiments on Sina Weibo demonstrate that our method outperforms the collaborative filtering based methods and trust-based methods. © 2013 by Binary Information Press.
引用
收藏
页码:3685 / 3696
页数:11
相关论文
共 26 条
[1]  
Adomavicius G., Tuzhilin A., Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, 17, pp. 734-749, (2005)
[2]  
Goldberg D., Et al., Using collaborative filtering to weave an information tapestry, Commun. ACM, 35, pp. 61-70, (1992)
[3]  
Konstan J.A., Et al., GroupLens: Applying collaborative filtering to Usenet news, Commun. ACM, 40, pp. 77-87, (1997)
[4]  
Linden G.G., Et al., Amazon. com recommendations: Item-to-item collaborative filtering, Internet Computing, IEEE, 7, pp. 76-80, (2003)
[5]  
Koren Y., Factorization meets the neighborhood: A multifaceted collaborative filtering model, Proc. of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426-434, (2008)
[6]  
Jamali M., Ester M., A matrix factorization technique with trust propagation for recommendation in social networks, Proc. of the Fourth ACM Conference on Recommender Systems, pp. 135-142, (2010)
[7]  
Kuter U., Golbeck J., Using probabilistic confidence models for trust inference in Web-based social networks, ACM Trans. Internet Technol., 10, pp. 1-23, (2010)
[8]  
Golbeck J., Computing and applying trust in web-based social networks, (2005)
[9]  
Massa P., Avesani P., Trust-aware recommender systems, Proc. of the 2007 ACM Conference on Recommender Systems, pp. 17-24, (2007)
[10]  
Jamali M., Ester M., TrustWalker: A random walk model for combining trust-based and item-based recommendation, Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397-406, (2009)