Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

• Published in: PloS one Vol. 10; no. 8; p. e0135090
• Main Authors: Ju, Bin, Qian, Yuntao, Ye, Minchao, Ni, Rong, Zhu, Chenxi
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.08.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Artificial intelligence; Collaboration; Computer science; Cooperative Behavior; Databases, Factual; Evolution; Factorization; Filtration; Gene expression; Humans; Information processing; International conferences; Mathematical analysis; Matrix methods; Models, Theoretical; Pattern recognition; Preferences; Recommender systems; Researchers; Semantics; Signal processing; User behavior; User feedback; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0135090

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.