Robust probabilistic tensor analysis for time-variant collaborative filtering

The input data of collaborative filtering, also known as recommendation system, are usually sparse and noisy. In addition, in many cases the data are time-variant and have obvious periodic property. In this paper, we take the two characteristics into account. To utilize the time-variant and periodic properties, we describe the data as a three-order tensor and then formulate the collaborative filtering as a problem of probabilistic tensor decomposition with a time-periodical constraint. The robustness is achieved by employing Tsallis divergence to describe the objective function and q-EM algorithm to find the optimal solution. The proposed method is demonstrated on movie recommendation. Experimental results on two Netflix and Movielens databases show the superiority of the proposed method. (C) 2012 Elsevier B.V. All rights reserved.