Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering.
Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper, we developed a unified model that combines Multi-task Non-negative Matrix Factorization and Linear Dynamical Systems to capture the evolution of user preferences. Specifically, user and item features are projected into latent factor space by factoring co-occurrence matrices into a common basis item-factor matrix and multiple factor-user matrices. Moreover, we represented both within and between relationships of multiple factor-user matrices using a state transition matrix to capture the changes in user preferences over time. The experiments show that our proposed algorithm outperforms the other algorithms on two real datasets, which were extracted from Netflix movies and Last.fm music. Furthermore, our model provides a novel dynamic topic model for tracking the evolution of the behavior of a user over time.
Institute of Artificial Intelligence, College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, P.R. China; Health Information Center of Zhejiang Province, Hangzhou, Zhejiang, P.R. China.
Bin
B
Ju
Health Information Center of Zhejiang Province, Hangzhou, Zhejiang, P.R. China.
Yuntao
Y
Qian
Institute of Artificial Intelligence, College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, P.R. China.
Minchao
M
Ye
Health Information Center of Zhejiang Province, Hangzhou, Zhejiang, P.R. China.
Rong
R
Ni
Health Information Center of Zhejiang Province, Hangzhou, Zhejiang, P.R. China.
Chenxi
C
Zhu
D000465:Algorithms; D003299:Cooperative Behavior; D016208:Databases, Factual; D006801:Humans; D008962:Models, Theoretical