Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation

TL;DR

This paper introduces an efficient kernel-based collaborative filtering method for top-N item recommendation using implicit feedback, leveraging sparsity and linear kernels to improve performance and scalability.

Contribution

It proposes a novel kernel-based approach optimized for implicit feedback, with an efficient implementation and analysis of kernel sparsity effects.

Findings

The linear kernel implementation is highly efficient.

The method achieves competitive accuracy with state-of-the-art algorithms.

Kernel sparsity is influenced by dataset properties, especially long tail distributions.

Abstract

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 we show how to generalize it to kernels of the dot product family preserving the efficiency. We also investigate on the elements which influence the sparsity of a standard cosine kernel. This analysis shows that the sparsity of the kernel strongly depends on the properties of the dataset, in particular on the long tail distribution. We compare our method with state-of-the-art algorithms achieving good results both in terms of efficiency and effectiveness.