References
1. Cremonesi P, Turrin R, Turrin R. Performance of recommender Algorithms on TopN recommendation tasks. ACM Conference on Recommender Systems 2010: 39 -46
2. Croft W B, Metzler D, Strohman T, et al. Search engines-information retrieval in practice. Computer Journal, 2010, 54(5): 831 -832
3. Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation. J Mach Learn Res, 2003, 3: 993 -1022
4. Lin F, Zhou X, Zeng W. Sparse online learning for collaborative filtering. Int J Comput Commun Control, 2016, 11(2): 248 -258
5. Badrul S, George K, Joseph K. Item-based collaborative filtering recommendation Algorithms. The International World Wide Web Conference, ACM, 2001: 285 -295
6. Bobadilla J, Hernando A, Ortega F. A framework for collaborative filtering recommender systems. Expert Systems with Applications an International Journal 2011, 38(12): 14609 -14623
7. Guo G, Zhang J, Yorke-Smith N. TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI Press, 2015: 123 -129
8. Shi Y, Larson M, Hanjalic A. List-wise learning to rank with matrix factorization for collaborative filtering. ACM Conference on Recommender Systems, Sept, 2010. Recsys, Barcelona, Spain: DBLP, 2010: 269 -272
9. Lee J W, Kim H J, Lee S G. Applying taxonomic knowledge and semantic collaborative filtering to personalized search: a bayesian belief network based approach. Web Conference, IEEE, 2010: 75 -81
10. Liu Q, Xiong Y, Huang W. Combining user-based and item-based models for collaborative filtering using stacked regression, Chinese. J Electron, 2014, 23(4)
11. Mobasher B, Burke R, Sandvig J J. Model-based collaborative filtering as a defense against profile injection attacks. AAAI, 2006, 6: 1388p
12. Jiang S, Qian X, Shen J. Author topic model-based collaborative filtering for personalized POI recommendations. IEEE Transactions on Multimedia, 2015, 17(6): 907 -918
13. Chi Y, Xia F. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 2019, 78(6): 15169 -15211
14. Mcauley J, Leskovec J. Hidden factors and hidden topics: understanding rating dimensions with review text. ACM Conference on Recommender Systems, ACM, 2013: 165 -172
15. Wilson J, Chaudhury S, Lall B. Improving collaborative filtering based recommenders using topic modelling. International Joint Conferences on Web Intelligence, IEEE Computer Society, 2014: 340 -346
16. Liu Q, Chen E, Xiong H. Enhancing collaborative filtering by user interest expansion via personalized ranking. IEEE Transactions on Systems Man and Cybernetics Part B, 2012, 42(1): 218 -233
17. Zhao X, Niu Z, Chen W. A hybrid approach of topic model and matrix factorization based on two-step recommendation framework. Journal of Intelligent Information Systems, 2015, 44(3): 335 -353
18. Cao Y L, Li W L. A hybrid recommendation approach using LDA and probabilistic matrix factorization, 2018: 1 -11
19. Hu S G, Liu Y, Chen T P. Emulating the Ebbinghaus forgetting curve of the human brain with a NiO-based memristor. Applied Physics Letters, 2013, 103(13): 734p
20. Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer, 2009(8): 30 -37 |
