Kernel meets recommender systems: A multi-kernel interpolation for matrix completion

A primary research direction for recommender systems is matrix completion, which attempts to recover the missing values in a user–item rating matrix. There are numerous approaches for rating tasks, which are mainly classified into latent factor models and neighborhood-based models. Most neighborhood-based models seek similar neighbors by computing similarities in the original data space for final predictions. In this paper, we propose a new...