基于用户间动态信任关系的推荐算法研究

doi:10.11896/j.issn.1002-137X.2015.09.044

摘要/Abstract

摘要： 在电子商务中,用户对商品的决策很大程度上取决于用户间的社会信任关系。传统的推荐算法往往考虑用户间的静态关系,即决策依赖的社会关系图是不变的。实际上,用户对好友的信任度往往随着时间的变化而变化。为了描述动态的信任关系在推荐系统中的作用,提出了一种基于动态信任关系的推荐算法。首先,提出了一种考虑用户的静态兴趣和静态信任关系的产生式模型；然后,分别将时序因素加入到用户兴趣和信任关系的描述,并提出了相应的动态产生式模型。实验表明,提出的算法能很好地描述用户之间信任关系随时间的变化,并且与其它相关算法相比,评价值的预测准确性得到了明显的提高。

关键词: 电子商务,推荐系统,信任关系,协同过滤

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

郑炅,石刚. 基于用户间动态信任关系的推荐算法研究[J]. 计算机科学, 2015, 42(9): 230-234. https://doi.org/10.11896/j.issn.1002-137X.2015.09.044

ZHENG Jiong and SHI Gang. Recommender Algorithm Based on Dynamical Trust Relationship between Users[J]. Computer Science, 2015, 42(9): 230-234. https://doi.org/10.11896/j.issn.1002-137X.2015.09.044

参考文献

[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

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