Information filtering via preferential diffusion

TL;DR

This paper introduces a recommendation algorithm based on preferential diffusion on bipartite networks, improving accuracy, diversity, and novelty in recommendations compared to existing methods.

Contribution

It presents a novel preferential diffusion algorithm that enhances recommendation accuracy while increasing diversity and novelty, especially for unpopular objects.

Findings

Outperforms state-of-the-art methods on MovieLens and Netflix datasets.

Provides more accurate recommendations.

Generates more diverse and novel recommendations.

Abstract

Recommender systems have shown great potential to address information overload problem, namely to help users in finding interesting and relevant objects within a huge information space. Some physical dynamics, including heat conduction process and mass or energy diffusion on networks, have recently found applications in personalized recommendation. Most of the previous studies focus overwhelmingly on recommendation accuracy as the only important factor, while overlook the significance of diversity and novelty which indeed provide the vitality of the system. In this paper, we propose a recommendation algorithm based on the preferential diffusion process on user-object bipartite network. Numerical analyses on two benchmark datasets, MovieLens and Netflix, indicate that our method outperforms the state-of-the-art methods. Specifically, it can not only provide more accurate recommendations, but also generate more diverse and novel recommendations by accurately recommending unpopular objects.