Kernelized Matrix Factorization for Collaborative Filtering

Abstract

Matrix factorization (MF) methods have shown great promise in collaborative filtering (CF). Conventional MF methods usually assume that the correlated data is distributed on a linear hyperplane, which is not always the case. Kernel methods are used widely in SVMs to classify linearly non-separable data, as well as in PCA to discover the non-linear… (More)
DOI: 10.1137/1.9781611974348.43
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