Kernel based collaborative filtering for very large scale top-N item recommendation

The increasing availability of implicit feedback datasets has raised the interest in developing effective collaborative filtering techniques able to deal asymmetrically with unambiguous positive feedback and ambiguous negative feedback. In this paper, we propose a principled kernel-based collaborative filtering method for top-N item recommendation with implicit feedback. We present an efficient implementation using the linear kernel, and how to generalize it to other kernels preserving efficiency. We compare our method with the state-of-the-art algorithm on the Million Songs Dataset achieving an execution about 5 time faster, while having comparable effectiveness.