  In this paper, based on a weighted projection of the user-object bipartite
network, we study the effects of user tastes on the mass-diffusion-based
personalized recommendation algorithm, where a user's tastes or interests are
defined by the average degree of the objects he has collected. We argue that
the initial recommendation power located on the objects should be determined by
both of their degree and the users' tastes. By introducing a tunable parameter,
the user taste effects on the configuration of initial recommendation power
distribution are investigated. The numerical results indicate that the
presented algorithm could improve the accuracy, measured by the average ranking
score, more importantly, we find that when the data is sparse, the algorithm
should give more recommendation power to the objects whose degrees are close to
the users' tastes, while when the data becomes dense, it should assign more
power on the objects whose degrees are significantly different from user's
tastes.
