  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.
