We study an important recommendation problem with implicit feedback from the perspective of item similarity. We exploit the complementarity of the predefined similarity and the learned similarity via a novel mixed similarity model. We develop a novel recommendation algorithm, i.e., pairwise factored mixed similarity model (P-FMSM), based on the mixed similarity and pairwise preference assumption. We showcase the effectiveness of P-FMSM as compared with several state-of-the-art methods on four public datasets.
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