Collaborative filtering is a useful algorithm for problems of personalized recommendation. For these prob-lems, there are many mature collaborative filtering algorithms. One class collaborative filtering is a new field of per-sonalized recommendation. Because of its data characteristics, common collaborative filtering algorithms have a lot of defects in the field of one class collaborative filtering. We studied the algorithm based on weighted matrix decomposi-tion, and optimized this algorithm by transfer learning. We prove the improvement of this optimization by experiments.
P. Turney. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. New Brunswick: Proceedings of the 40th Annual Meeting of the Association of Computational Linguistics, July 2002: 417-424.
R. Pan, M. Scholz. Mind the gaps: Weighting the unknown in large-scale one-class collaborative filtering. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009: 667-676.
W. Pan, E. W. Xiang, N. N. Liu and Q. Yang. Transfer learning in collaborative filtering for sparsity reduction. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10), 2010
Y. Li, J. Hu, C. X. Zhai and Y. Chen. Improving one-class collaborative filtering by incorporating rich user information. Pro- ceedings of the 19th ACM International Conference on Informa- tion and Knowledge Management, 2010: 959-968.