Computer Science ›› 2015, Vol. 42 ›› Issue (9): 230-234.doi: 10.11896/j.issn.1002-137X.2015.09.044

Previous Articles     Next Articles

Recommender Algorithm Based on Dynamical Trust Relationship between Users

ZHENG Jiong and SHI Gang   

  • Online:2018-11-14 Published:2018-11-14

Abstract: In e-commerce,a user’s selection of item largely depends on the trust relationship of users.Traditional re-commender algorithms usually consider the static relationship between users,that is,the depended relationship for decision is changeless.In order to describe the importance of static trust relationship in recommender system,this paper proposed a dynamical trust relationship based recommender algorithm.First,we proposed a generative model that takes both static user inte-rest and static trust relationship into consideration.Then,we added temporal factor into user inte-rest and trust relationship,and proposed corresponding dynamical generative model.The experiments show that the proposed algorithm can describe the dynamical trust relationship between users,and has better prediction accuracy than related algorithms.

Key words: E-commerce,Recommender system,Trust relationship,Collaborative filtering

[1] 许海玲,吴潇,李晓东,等.互联网推荐系统比较研究[J].软件学报,2009,20(2):350-362 Xu Hai-ling,Wu Xiao,Li Xiao-dong,et al.Comparison study of Internet recommendation system [J].Journal of Software,2009,20(2):350-362
[2] Esparza G S,O’Mahony M P,Smyth B.Mining the real-time web:a novel approach to product recommendation[J].Knowledge-Based Systems,2012,29(3):3-11
[3] Pham M C,Cao Y,Klamma R,et al.A Clustering Approach for Collaborative Filtering Recommendation Using Social Network Analysis[J].J.UCS,2011,17(4):583-604
[4] Lops P,de Gemmis M,Semeraro G.Content-based recommender systems:State of the art and trends[M]∥Recommender Systems Handbook.Springer US,2011:73-105
[5] Koren Y,Bell R.Advances in collaborative filtering[M]∥Recommender Systems Handbook.Springer US,2011:145-186
[6] 罗辛,欧阳元新,熊璋,等.通过相似度支持度优化基于 K 近邻的协同过滤算法[J].计算机学报,2010,33(8):1437-1445 Luo Xin,Ouyang Yuan-xin,Xiong Zhang,et al.The Effect of Similarity Support in k-Nearest-Neighborhood Based Collaborative Filtering[J].Chinese Journal of Computers,2010,33(8):1437-1445
[7] Breese J S,Heckerman D,Kadie C.Empirical analysis of predictive algorithms for collaborative filtering[C]∥Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence.1998:43-52
[8] Porteous I,Bart E,Welling M.Multi-HDP:A Non Parametric Bayesian Model for Tensor Factorization[C]∥AAAI.2008:1487-1490
[9] Blei D M,Ng A Y,Jordan M I.Latent dirichlet allocation[J].The Journal of Machine Learning Research,2003(3):993-1022
[10] Mackey L W,Weiss D,Jordan M I.Mixed membership matrix factorization[C]∥Proceedings of the 27th International Conference on Machine Learning(ICML-10).2010:711-718
[11] Koren Y.Collaborative filtering with temporal dynamics[J].Communications of the ACM,2010,53(4):89-97
[12] Xiong L,Chen X,Huang T K,et al.Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization[C]∥SDM.2010:211-222
[13] Xiang L,Yuan Q,Zhao S,et al.Temporal recommendation ongraphs via long-and short-term preference fusion[C]∥Procee-dings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2010:723-732
[14] Rendle S,Freudenthaler C,Schmidt-Thieme L.Factorizing personalized markov chains for next-basket recommendation[C]∥Proceedings of the 19th International Conference on World Wide Web.ACM,2010:811-820
[15] Ma H,Yang H,Lyu M R,et al.Sorec:social recommendationusing probabilistic matrix factorization[C]∥Proceedings of the 17th ACM Conference on Information and Knowledge Management.ACM,2008:931-940
[16] Yuan Q,Chen L,Zhao S.Factorization vs.regularization:fusing heterogeneous social relationships in top-n recommendation[C]∥Proceedings of the fifth ACM Conference on Recommender Systems.ACM,2011:245-252
[17] Ma H,Zhou D,Liu C,et al.Recommender systems with social regularization[C]∥Proceedings of the fourth ACM InternationalConference on Web Search and Data Mining.ACM,2011:287-296
[18] Jamali M,Ester M.A transitivity aware matrix factorizationmodel for recommendation in social networks[C]∥Proceedings of the Twenty-Second international joint conference on Artificial Intelligence.2011:2644-2649
[19] Shen Y,Jin R.Learning personal+social latent factor model for social recommendation[C]∥Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2012:1303-1311
[20] Ye M,Liu X,Lee W C.Exploring social influence for recommendation:a generative model approach[C]∥Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2012:671-680
[21] Salakhutdinov R,Mnih A.Probabilistic Matrix Factorization.http://www.cs.torontv.edu/~amnih/papers/pmf.pdf
[22] 王越,程昌正.协同过滤算法在电影推荐中的应用[J].四川兵工学报,2014,35(5):86-88 Wang Yue,Cheng Chang-zheng.Application of Collaborative Filtering Algorithms in Movie Recommendation[J].Journal of Sichuan Ordnance,2014,5(5):86-88

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!