2MICHAEL J, ANDREAS T, ROBERT L. Combining predictions for accurate recommender systems[ C]//Pmc of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York : ACM Press, 2010:693- 702.
3昊金龙.NetflixPrize中的协同过滤算法[D].北京:北京大学,2010.
4ROBERT B, YEHUDA K, CHRIS V. Modeling relationships at multi- ple scales to improve accuracy of large recommender systems [ C ]//Proc of the 13th ACM SIGKDD International Conference on Knowledge Dis- covery and Data Mining. New York :ACM Press ,2007:95-104.
5SU Xiao-yuan. Collaborative filtering recommendation using machine learning and statistical techniques [D]. Boca Raton:Florida Atlantic University, 2008.
6DAVID P, ERIC H, STEVE L, et al. Collaborative filtering by per- sonality diagnosis: a hybrid memory and model based approach [ C ]// Proc of National Conference on Artificial Intelligence. San Francisco : Morgan Kaufmann Publishers Inc ,2000:473-480.
7RASHID A M, LAM S K, KARYPIS G, et al. ClustK-NN: a highly scalable hybrid model & memory-based CF algorithm [ C ]//Pmc of the 12th ACM SIGKDD International Conference on KDD and Data Mining. New York:ACM Press,2006.
8CHEN Zhi-min, JIANG Yi, ZHAO Yao. A collaborative filtering re- commendation algorithm based on user interest change and trust evalu- ation[ J]. International Journal of Digital Content Technology and its Applications,2010,4 (9) : 106- 113.
9PAUL R, NEOPHYTES L, MITESH S, et al. GroupLens: an open architecture for ,collaborative filtering of netnews [ C ]//Proc of ACM Conference on Computer Supported Cooperative Work. New York: ACM Press, 1994 : 175-186.
10SUN Duo, ZHOU Tao, LIU Jian-guo, et al. Information filtering based on transferring similarity [ J ]. Physical Review E, 2009,80 (1) :017101.