title: Information filtering in sparse online systems: recommendation via semi-local diffusion.
abstract: 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 the user-object bipartite network. The simulation results 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, so it is necessary to reexamine former algorithms and conclusions based on dense data in sparse systems.
authors.affiliation: Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, People's Republic of China ; Department of Physics, University of Fribourg, Fribourg, Switzerland.
authors.forename: Wei
authors.initials: W
authors.lastname: Zeng
authors.forename: An
authors.initials: A
authors.lastname: Zeng
authors.forename: Ming-Sheng
authors.initials: MS
authors.lastname: Shang
authors.forename: Yi-Cheng
authors.initials: YC
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D006801:Humans; D020407:Internet; D009862:Online Systems; D012984:Software