JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2018, Vol. 48 ›› Issue (3): 96-102.doi: 10.6040/j.issn.1672-3961.0.2017.404

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An additive co-clustering for recommendation of integrating social network

DU Xixi, LIU Huafeng, JING Liping*   

  1. Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China
  • Received:2017-08-23 Online:2018-06-20 Published:2017-08-23

Abstract: In order to solve the problem of user cold start problem and improve the prediction accuracy of recommendation algorithm, an additive co-clustering recommendation model combining social networks(SN-ACCRec)was proposed, which integrated user social relations into user clustering of rating matrix. According to the social relations theory analysis of users, user blocks was divided with the idea of fuzzy C means clustering, and a co-clustering result was acquired by clusters items on rating matrix according to k-means algorithm. The general and specific categories was gotten by generating the user and item additive co-clustering results in an iterative method and pedict the missing values. The model was evaluated using ten fold cross validation method, and experimental results showed that this model could reduce the average absolute error(MAE)and the root mean square error(RMSE), which also showed a better recommendation performance in the cold start users.

Key words: personalized recommendation, social networks, co-clustering

CLC Number: 

  • TP311
[1] FOGARAS D, RACZ B. Towards scaling fully personalized pagerank[C] //Algorithms and Models for the Web-Graph: Third International Workshop, WAW 2004. Berlin, Germany: Springer, 2004:105-117.
[2] HAN D, LIU B, SUN Y. The research on collaborative filtering in personalization recommendation system[J]. Advanced Materials Research, 2013, 846-847:1137-1140.
[3] KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009,42(8):30-37.
[4] JAMALI M, ESTER M. Trustwalker: a random walk model for combining trustbased and item-based recommendation[C] //Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, France: ACM, 2009:397-406.
[5] HUANG S, ZHANG J, SCHONFELD D, et al. Two-stage friend recommendation based on network alignment and series-expansion of probabilistic topic model[J]. IEEE Transactions on Multimedia, 2017, 19(6)1314-1326.
[6] GUO G, ZHANG J, ZHU F, et al. Factored similarity models with social trust for top-N item recommendation[J]. Knowledge-Based Systems, 2017, 122:17-25.
[7] MASSA P, AVESANI P. Trust-aware recommender systems[C] //Proceedings of the 2007 ACM Conference on Recommender Systems. Minneapolis, USA:ACM, 2007:17-24.
[8] FELÍCIO C, PAIXAO K, ALVES G, et al. Exploiting social information in pairwise preference recommender system[J]. Journal of Information and Data Management, 2017,7(2):99-115.
[9] YANG B, LEI Y, LIU J, et al. Social collaborative filtering by trust[C] //Proceedings of the 23th International Joint Conference on Artificial Intelligence. Beijing, China: AAAI Press, 2013:2747-2753.
[10] BAO Y, FANG H, ZHANG J. Leveraging decomposed trust in probabilistic matrix factorization for effective recommendation[C] //Proceedings of 28th AAAI Conference on Artificial Intelligence. Quebec, Canada: AAAI Press, 2014:30-36.
[11] LIU J, WU C, LIU W. Bayesian probabilistic matrix factorization with social relations and item contents for recommendation[J]. Decision Support Systems, 2013, 55(3):838-850.
[12] MA H, YANG H, LYU M R, et al. Sorec: social recommendation using probabilistic matrix factorization[C] //Proceedings of ACM 17th conference on Information and Knowledge Management CIKM. California, USA: ACM, 2008:931-940.
[13] GUO G, ZHANG J, YORKESMITH N. TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings[C] //Proceedings of 29th AAAI Conference on Artificial Intelligence. Texas, USA: AAAI Press, 2015:123-129.
[14] BEUTEL A, AKOGLU L, FALOUTSOS C. Graph-Based user behavior modeling: from prediction to fraud detection[C] //Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Sydney, Australia: ACM, 2015:2309-2310.
[15] GORI M, PUCCI A, ROMA V, et al. ItemRank: a randomwalk based scoring algorithm for recommendder engines[C] //Proceedings of 20th International Joint Conference on Artificial Intelligence. Hyderabad, India: AAAI Press, 2007,7:2766-2771
[16] MNIH A, SALAKHUTDINOV R. Probabilistic matrix factorization[C] //Proceedings of International Conference on Neural Information Processing Systems, Kitakyushu, Japan:ACM, 2007:1257-1264.
[17] SALAKHUTDINOV R, MNIH A. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo[C] //Proceedings of the 25th International Conference on Machine learning. Helsinki, Finland: ACM, 2008:880-887.
[18] JING LP, WANG P, YANG L. Sparse probabilistic matrix factorization by Laplace distribution for collaborative filtering[C] //Proceedings of 24th International Joint Conference on Artificial Intelligence. Buenos, Aires: AAAI Press, 2015:1771-1777.
[19] BEUTEL A, AHMED A, SMOLA A J. ACCAMS: additive co-clustering to approximate matrices succinctly[C] //Proceedings of the 24th International Conference on World Wide Web. Florence, Italy: ACM, 2015:119-129.
[20] JAMALI M, ESTER M. A matrix factorization technique with trust propagation for recommendation in social networks[C] //Proceedings of the fourth ACM Conference on Recommender Systems. Barcelona, Spain:ACM, 2010:135-142.
[21] TANG J L, Hu X, Gao H, et al. Exploiting local and global social context for recommendation[C] //Proceedings of the 23th International Joint Conference on Artificial Intelligence. Beijing, China: AAAI Press, 2013:264-269.
[22] MARSDEN P V, FRIEDKIN N E. Network studies of social influence[J]. Sociological Methods and Research, 1993, 22(1):127-151.
[23] HATHAWAY R J, Bezdek J C. Fuzzy c-means clustering of incomplete data[J]. IEEE Transactions on Systems, Man and Cybernetics: Part B(Cybernetics), 2001, 31(5):735-744.
[24] MA H, ZHOU D, LIU C, et al. Recommender systems with social regularization[C] //Proceedings of ACM International Conference on Web Search and Data Mining. HongKong, China: ACM, 2011:287-296.
[25] LIU H, JING L, CHENG M. An efficient parallel trustbased recommendation method on multicores[C] //Proceedings of 13th High Performance Graph Data Management and Processing Workshop. Salt Lake, USA: IEEE, 2016:9-16.
[26] HERLOCKER J L, KONSTAN J A, TERVEEN L G, et al. Evaluating collaborative filtering recommender systems[J]. ACM Transactions on Information Systems, 2004, 22(1): 5-53.
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