[1] BARJASTEH I, FORSATI R, ROSS D, et al. Cold-start recommendation with provable guarantees:a decoupled approach[J]. IEEE Transactions on Knowledge & Data Engineering, 2016, 28(6):1462-1474. [2] ADOMAVICIUS G, SANKARANARAYANAN R, SEN S, et al. Incorporating contextual information in recommender systems using a multidimensional approach[J]. ACM Transactions on Information Systems, 2005, 23(1):103-145. [3] CAI Y, LEUNG H F, LI Q, et al. TyCo:towards typicality-based collaborative filtering recommendation[J]. IEEE Transactions on Knowledge & Data Engineering, 2014, 2(3):97-104. [4] HU Y, KOREN Y, VOLINSKY C. Collaborative filtering for implicit feedback datasets[C]//Proceedings of the 8th IEEE International Conference on Data Mining. Washington, DC:IEEE Computer Society, 2008:263-272. [5] HUANG Z, CHEN H, ZENG D. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering[J]. ACM Transactions on Information Systems, 2004, 22(1):116-142. [6] JING H, LIANG A C, LIN S D, et al. A transfer probabilistic collective factorization model to handle sparse data in collaborative filtering[C]//Proceedings of the 2014 IEEE International Conference on Data Mining. Washington, DC:IEEE Computer Society, 2014:250-259. [7] WANG J, DE VRIES A P, REINDERS M J T. Unifying user-based and item-based collaborative filtering approaches by similarity fusion[C]//Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development In Information Retrieval. New York:ACM, 2006:501-508. [8] ZHANG Z, ZHAO K. Low-rank matrix approximation with manifold regularization[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(7):1717-1729. [9] KANNAN R, ISHTEVA M, PARK H. Bounded matrix low rank approximation[C]//Proceedings of the 2013 IEEE 13th International Conference on Data Mining. Washington, DC:IEEE Computer Society, 2012:319-328. [10] KIM J, HE Y, PARK H. Algorithms for non-negative matrix and tensor factorization:a unified view based on block coordinate descent framework[J]. Journal of Global Optimization, 2014, 58(2):285-319. [11] MA H, KING I, LYU M R. Effective missing data prediction for collaborative filtering[C]//Proceedings of the 2007 International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM, 2007:39-46. [12] KANNAN R, ISHTEVA M, DRAKE B, et al. Bounded matrix low rank approximation[C]//Proceedings of the 8th IEEE International Conference on Data Mining. Washington, DC:IEEE Computer Society, 2012:319-328. [13] 陈彦萍,王赛.基于用户-项目的混合协同过滤算法[J].计算机技术与发展,2014,24(12):88-91.(CHEN Y P, WANG S. A hybrid collaborative filtering algorithm based on user-item[J]. Computer Technology and Development, 2014, 24(12):88-91.) |