Abstract
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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Karatzoglou, A., Weimer, M. (2010). Quantile Matrix Factorization for Collaborative Filtering. In: Buccafurri, F., Semeraro, G. (eds) E-Commerce and Web Technologies. EC-Web 2010. Lecture Notes in Business Information Processing, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15208-5_23
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DOI: https://doi.org/10.1007/978-3-642-15208-5_23
Publisher Name: Springer, Berlin, Heidelberg
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