| 1 |
Adomavicius G ， Tuzhilin A . Toward the next generation of recommender systems：a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering（TKDE）， 2005，17（6）： 734～749
|
| 2 |
Koren Y . Collaborative filtering with temporal dynamics. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining， Paris，France， 2009： 447～455
|
| 3 |
Jordan M I ， Blei D M ， Ng A Y ，et al. Latent dirichlet allocation. Journal of Machine Learning Research， 2003（3）： 993～1022
|
| 4 |
Hofmann T . Probabilistic latent semantic indexing. Proceedings of the 21th Annual International SIGIR Conference on Research ＆ Development in Information Retrieval， Berkeley，CA，USA， 1999： 457～465
|
| 5 |
Hofmann T . Collaborative filtering via gaussian probabilistic latent semantic analysis. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval， Toronto，Canada， 2003： 259～266
|
| 6 |
Bishop C M . Pattern Recognition and Machine Learning. New York： Springer-Verlag， 2006
|
| 7 |
Lee D D ， Seung H S . Learning the parts of objects by nonnegative matrix factorization. Nature， 1999，401（6755）： 788～791
|
| 8 |
Olshausen B A ， Field D J . Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature， 1996（381）： 607～609
|
| 9 |
Gao S ， Luo H ， Chen D ，et al. Cross-domain recommendation via cluster-level latent factor model. Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database， Prague，Czech Republic， 2013： 161～176
|
| 10 |
Pan W ， Liu N N ， Xiang E W ，et al. Transfer learning to predict missing ratings via heterogeneous user feedbacks. Proceedings of the 22th International Joint Conference on Artificial Intelligence， Barcelona，Spain， 2011： 2318～2323
|
| 11 |
Li B ， Yang Q ， Xue X . Transfer learning for collaborative filtering via a rating-matrix generative model. Proceedings of the 26th International Conference on Machine Learning， Montreal，Canada， 2009： 617～624
|
| 12 |
Singh A P ， Gordon G J . Relational learning via collective matrix factorization. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining， Las Vegas，Nevada，USA， 2008： 650～658
|
| 13 |
Berkovsky S ， Kuflik T ， Ricci F . Mediation of user models for enhanced personalization in recommender systems. User Modeling and User-Adapted Interaction， 2008，18（3）： 345～352
|
| 14 |
Zhang Y ， Koren J . Relational learning via collective matrix factorization. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval， New York，USA， 2007： 47～54
|
| 15 |
Lee D D ， Seung H S . Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems， 2001，13（3）： 556～562
|
| 16 |
Luo S ， Rong J . Flexible mixture model for collaborative filtering. Proceedings of the 20th International Conference on Machine Learning， Washington DC，USA， 2003（3）： 704～711
|
| 17 |
Baeza-Yates R ， Ribeiro-Neto B . Modern information retrieval. Facet Publishing， 1999，20（19）： 305～306
|
| 18 |
Hu Y F ， Koren Y ， Volinsky C . Collaborative filtering for implicit feedback datasets. Proceedings of IEEE 8th Conference on Data Mining， Pisa，Italy， 2008： 263～272
|
