Mixed similarity learning for recommendation with implicit feedback

In this paper, we exploit the complementarity of the predefined similarity and the learned similarity via a novel mixed similarity model. Furthermore, we develop a novel recommendation algorithm, i.e., pairwise factored mixed similarity model (P-FMSM), based on the mixed similarity and pairwise preference assumption. Our P-FMSM is able to (i) capture the locality of the user-item interactions via the symmetric predefined similarity, (ii) model the global correlations among items via the asymmetric learned similarity, and (iii) digest the uncertain implicit feedback via the pairwise preference assumption. Empirical studies on four public datasets show that our P-FMSM can recommend significantly more accurate than several state-of-the-art methods.