Information Filtering via Balanced Diffusion on Bipartite Networks
This work addresses the challenge of enhancing recommendation quality for users in online platforms, but it is incremental as it builds on existing diffusion methods.
The paper tackled the problem of improving recommendation systems by proposing a balanced diffusion (BD) algorithm that hybridizes mass diffusion and heat conduction with balanced weights. The result showed that BD outperformed existing diffusion-based methods on accuracy, diversity, and novelty across three benchmark datasets, including MovieLens, Netflix, and RateYourMusic.
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.