Kernelized Matrix Factorization for Collaborative Filtering


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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