  The recommender system is one of the most promising ways to address the
information overload problem in online systems. Based on the personal
historical record, the recommender system can find interesting and relevant
objects for the user within a huge information space. Many physical processes
such as the mass diffusion and heat conduction have been applied to design the
recommendation algorithms. The hybridization of these two algorithms has been
shown to provide both accurate and diverse recommendation results. In this
paper, we proposed two similarity preferential diffusion processes. Extensive
experimental analyses on two benchmark data sets demonstrate that both
recommendation and accuracy and diversity are improved duet to the similarity
preference in the diffusion. The hybridization of the similarity preferential
diffusion processes is shown to significantly outperform the state-of-art
recommendation algorithm. Finally, our analysis on network sparsity show that
there is significant difference between dense and sparse system, indicating
that all the former conclusions on recommendation in the literature should be
reexamined in sparse system.
