کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
410320 | 679137 | 2013 | 5 صفحه PDF | دانلود رایگان |
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.
Journal: Neurocomputing - Volume 119, 7 November 2013, Pages 139–143