Alexandros Karatzoglou, Markus Weimer


Matrix Factorization-based algorithms are among the state-of-the-art in Collaborative Filtering methods. In many of these models, a least squares loss functional is implicitly or explicitly minimized and thus the resulting estimates correspond to the conditional mean of the potential rating a user might give to an item. However they do not provide any information on the uncertainty and the confidence of the Recommendation. We introduce a novel Matrix Factorization algorithm that estimates the conditional quantiles of the ratings. Experimental results demonstrate that the introduced model performs well and can potentially be a very useful tool in Recommender Engines by providing a direct measure of the quality of the prediction.

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  author = {Alexandros Karatzoglou and Markus Weimer}, 
  Booktitle = {Proceedings of the 11th International Conference on Electronic Commerce and Web Technologies (EC-Web)}, 
  Title = {Quantile Matrix Factorization for Collaborative Filtering}, 
  Year = {2010}