Information filtering in sparse online systems: recommendation via semi-local diffusion
This addresses the data sparsity issue in online recommender systems, which is a critical bottleneck for accurate recommendations, though it appears incremental as it builds on diffusion-based methods.
The paper tackles the data sparsity problem in recommender systems by proposing a recommendation algorithm based on semi-local diffusion on user-object bipartite networks, showing significant outperformance over state-of-the-art methods on sparse datasets like Amazon and Bookcross, especially for small-degree users.
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.