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Hybrid Recommendation Algorithm Based on Social Trust Clustering
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National Natural Science Foundation of China (6110048); Natural Science Foundation of Heilongjiang Province, China (F2016034)

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    Abstract:

    Recommender system can solve the information overload problem effectively, and collaborative filtering (CF) is one of the techniques that is widely used in recommendation system. However, the traditional CF technology has problems such as poor scalability, sparse data, and low accuracy of recommendation results. In order to improve the quality of recommendations, this article integrates the trust relationship into the recommendation system in which the trust relationship is clustered by using the clustering (FCM) method. Using the trust cluster to predict implicit trust between users, the trust relationship is finally combined with the user-item relationship to give recommendations. The experimental results on the data set of Douban and Epinions show that compared with traditional CF algorithm, trust based recommendation algorithm and recommendation algorithm for user item clustering, the presented algorithm can greatly improve the recommendation quality and time efficiency.

    Reference
    [1] Heckel R, Ramchandran K. The sample complexity of online one-class collaborative filtering. In:Proc. of the Int'l Conf. on Machine Learning. 2017. 1452-1460.
    [2] Guo G, Zhang J, Yorke-Smith N. TrustSVD:Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In:Proc. of the 29th AAAI Conf. on Artificial Intelligence. AAAI Press, 2015. 123-129.
    [3] Wang H, Wang N, Yeung DY. Collaborative deep learning for recommender systems. In:Proc. of the KDD. 2014. 1235-1244.[doi:10.1145/2783258.2783273]
    [4] Jing L, Wang P, Yang L. Sparse probabilistic matrix factorization by Laplace distribution for collaborative filtering. In:Proc. of the Int'l Conf. on Artificial Intelligence. AAAI Press, 2015. 1771-1777.
    [5] Chen T, Zhu Q, Zhou MX, Wang S. Trust-Based recommendation algorithm in social network. Ruan Jian Xue Bao/Journal of Software, 2017,28(3):721-731(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5159.htm[doi:10.13328/j.cnki. jos.005159]
    [6] Chaney AJB, Blei DM, Eliassi-Rad T. A probabilistic model for using social networks in personalized item recommendation. In:Proc. of the ACM Conf. on Recommender Systems. ACM, 2015. 43-50.[doi:10.1145/2792838.2800193]
    [7] Kakkar S, Beniwal S. Discovering overlapping community structure in networks through co-clustering. In:Proc. of the Int'l Conf. on Inventive Computation Technologies. Coimbatore:IEEE, 2017. 1296-1300.[doi:10.13140/RG.2.2.28132.17282]
    [8] Jin H, Yu W, Li SJ. A clustering algorithm for determining community structure in complex networks. Physica A Statistical Mechanics & Its Applications, 2018, 492.[doi:10.1016/j.physa.2017.11.029]
    [9] Huang S, Zhang J, Lu S, Hua XS. Social friend recommendation based on network correlation and feature co-clustering. In:Proc. fof the ICMR 2015, the 5th ACM on Int'l Conf. on Multimedia Retrieval. Shanghai:ICMR, 2015,18(2):315-322.[doi:10.1145/2671188.2749325]
    [10] Heckel R, Vlachos M, Parnell T, Duenner C. Scalable and interpretable product recommendations via overlapping co-clustering. In:Proc. of the IEEE Int'l Conf. on Data Engineering. San Diego:IEEE, 2017. 1033-1044.[doi:10.1109/ICDE.2017.149]
    [11] Xu B, Bu J, Chen C, Cai D. An exploration of improving collaborative recommender systems via user-item subgroups. In:Proc. of the WWW. 2012. 21-30.[doi:10.1145/2187836.2187840]
    [12] Liang CY, Leng YJ. Collaborative filtering based on information-theoretic co-clustering. Proc. of the Int'l Journal of Systems Science, 2014,45(3):589-597.[doi:10.1080/00207721.2012.724109]
    [13] Du X, Liu H, Jing L. Additive co-clustering with social influence for recommendation. In:Proc. of the 11th ACM Conf. ACM, 2017. 193-200.[doi:10.1145/3109859.3109883]
    [14] Wu Y, Liu X, Xie M, Ester M, Yang Q. CCCF:Improving collaborative filtering via scalable user-item co-clustering. In:Proc. of the WSDM 2016, the 9th ACM Int'l Conf. on Web Search and Data Mining. ACM, 2016. 73-82.[doi:10.1145/2835776.2835836]
    [15] Dubois T, Golbeck J, Kleint J, Srinivasan A. Improving recommendation accuracy by clustering social networks with trust. In:Proc. of the ACM RecSys 2009 Workshop on Recommender Systems & the Social Web. 2009.
    [16] Bezdek JC, Ehrlich R, Full W. FCM:The fuzzy c-means clustering algorithm. Computers & Geosciences, 1984,10(2):191-203.[doi:10.1016/0098-3004(84)90020-7]
    [17] Liao CL, Lee SJ. A clustering based approach to improving the efficiency of collaborative filtering recommendation. Electronic Commerce Research & Applications, 2016,18:1-9.[doi:10.1016/j.elerap.2016.05.001]
    附中文参考文献
    [5] 陈婷,朱青,周梦溪,王珊.社交网络环境下基于信任的推荐算法.软件学报,2017,28(3):721-731. http://www.jos.org.cn/1000-9825/5159.htm[doi:10.13328/j.cnki.jos.005159]
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朱敬华,王超,马胜超.基于社交信任聚类的混合推荐算法.软件学报,2018,29(S1):21-31

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  • Received:May 01,2018
  • Online: November 13,2018
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