Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization

  title={Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization},
  author={Liang Xiong and X. Chen and Tzu-Kuo Huang and Jeff G. Schneider and Jaime G. Carbonell},
Real-world relational data are seldom stationary, yet traditional collaborative filtering algorithms generally rely on this assumption. [] Key Method 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…

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