Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization
Liang Xiong,Xi Chen,Tzu-Kuo Huang,Jeff Schneider,Jaime G. Carbonell +4 more
- pp 211-222
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TLDR
This work proposes a factor-based algorithm that is able to take time into account, and provides a fully Bayesian treatment to avoid tuning parameters and achieve automatic model complexity control.Abstract:
Real-world relational data are seldom stationary, yet traditional collaborative filtering algorithms generally rely on this assumption. Motivated by our sales prediction problem, we propose a factor-based algorithm that is able to take time into account. By introducing additional factors for time, we formalize this problem as a tensor factorization with a special constraint on the time dimension. Further, we provide a fully Bayesian treatment to avoid tuning parameters and achieve automatic model complexity control. To learn the model we develop an efficient sampling procedure that is capable of analyzing large-scale data sets. This new algorithm, called Bayesian Probabilistic Tensor Factorization (BPTF), is evaluated on several real-world problems including sales prediction and movie recommendation. Empirical results demonstrate the superiority of our temporal model.read more
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