Information filtering via balanced diffusion on bipartite networks

The 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, such as mass diffusion (MD) and heat conduction (HC) algorithms, which have different advantages respectively on accuracy and diversity. In this paper, we explore how to combine MD and HC processes to get better recommendation performance and propose a new algorithm mimicking the hybrid of MD and HC processes, named balanced diffusion (BD) algorithm. Numerical experiments on three benchmark data sets, MovieLens, Netflix and RateY ourMusic, show that BD algorithm outperforms three typical diffusion-like algorithms on the three important metrics, accuracy, diversity and novelty. Specifically, it not only provides accurate recommendation results, but also yields higher diversity and novelty in recommendations by accurately recommending unpopular objects.