Information Filtering via Balanced Diffusion on Bipartite Networks

TL;DR

This paper introduces a balanced diffusion algorithm for bipartite networks that improves recommendation accuracy, diversity, and novelty by optimally combining mass diffusion and heat conduction processes.

Contribution

It proposes a new hybrid diffusion algorithm with balanced weights that outperforms existing methods on multiple recommendation metrics.

Findings

BD algorithm outperforms existing diffusion methods in accuracy, diversity, and novelty.

It effectively recommends unpopular objects, enhancing recommendation quality.

Experimental results on three benchmark datasets validate its superior performance.

Abstract

Recent decade has witnessed the increasing popularity of recommender systems, which help users acquire relevant commodities and services from overwhelming resources on Internet. Some simple physical diffusion processes have been used to design effective recommendation algorithms for user-object bipartite networks, typically mass diffusion (MD) and heat conduction (HC) algorithms which have different advantages respectively on accuracy and diversity. In this paper, we investigate the effect of weight assignment in the hybrid of MD and HC, and find that a new hybrid algorithm of MD and HC with balanced weights will achieve the optimal recommendation results, we name it balanced diffusion (BD) algorithm. Numerical experiments on three benchmark data sets, MovieLens, Netflix and RateYourMusic (RYM), show that the performance of BD algorithm outperforms the existing diffusion-based methods on the three important recommendation metrics, accuracy, diversity and novelty. Specifically, it can not only provide accurately recommendation results, but also yield higher diversity and novelty in recommendations by accurately recommending unpopular objects.