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Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization (2010)

Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, Jaime G. Carbonell

Tags

Collaborate filtering, MCMC, Temporal modeling, Tensor factorization

Abstract

Real-world relational data are seldom stationary, yet traditional collaborative fltering 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.

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Approximate BibTeX Entry

@inproceedings{lxiong:10:bptf,
    Year = {2010},
    Booktitle = {Proceedings of SIAM Data Mining},
    Author = { Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, Jaime G. Carbonell },
    Title = {Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization}
}

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