  With the rapid growth of the Internet and overwhelming amount of information
and choices that people are confronted with, recommender systems have been
developed to effectively support users' decision-making process in the online
systems. However, many recommendation algorithms suffer from the data sparsity
problem, i.e. the user-object bipartite networks are so sparse that algorithms
cannot accurately recommend objects for users. This data sparsity problem makes
many well-known recommendation algorithms perform poorly. To solve the problem,
we propose a recommendation algorithm based on the semi-local diffusion process
on a user-object bipartite network. The numerical simulation on two sparse
datasets, Amazon and Bookcross, show that our method significantly outperforms
the state-of-the-art methods especially for those small-degree users. Two
personalized semi-local diffusion methods are proposed which further improve
the recommendation accuracy. Finally, our work indicates that sparse online
systems are essentially different from the dense online systems, all the
algorithms and conclusions based on dense data should be rechecked again in
sparse data.
