Koren Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, Nevada: ACM, 2008: 426-434.
[2]
Settles B, Craven M, Ray S. Multiple-instance active learning[C]//Advances in Neural Information Processing Systems (NIPS).[S.l]: MIT Press, 2008: 1289-1296.
[3]
Cremonesi P, Koren Y, Turrin R. Performance of recommender algorithms on top-n recommendation tasks[C]//Proceedings of the Fourth ACM Conference on Recommender Systems-RecSys10. New York: ACM, 2010: 39-47.
[4]
Schein A I, Popescul A, Ungar L H, et al. Methods and metrics for cold-start recommendations[C]//Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Tampere: ACM, 2002: 253-260.
[5]
Zhang Z K, Liu C, Zhang Y C, et al. Solving the cold-start problem in recommender systems with social tags[J]. EPL (Europhysics Letters), 2010, 92(28): 2.
[6]
Rubens N, Kaplan D, Sugiyama M. Recommender systems handbook[M]. Berlin: Springer, 2011: 735-767.
[7]
Huang Z. Selectively acquiring ratings for product recommendation[C]//Proceedings of the Ninth International Conference on Electronic Commerce. Minneapolis: ACM, 2007: 379-388.
[8]
Boutilier C, Zemel R S, Marlin B. Active collaborative filtering[C]//Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence. San Francisco: Morgan Kaufman, 2003: 98-106.
[9]
Jin R, Si L. A Bayesian approach toward active learning for collaborative filtering[C]//Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. Banff: AUAI Press, 2004: 278-285.
[10]
Harpale A S, Yang Y. Personalized active learning for collaborative filtering[C]//Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Singapore: ACM, 2008: 91-98.
