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.
Highlights
Owing to the rapid development of the Internet, people are confronted with abundant online contents, which makes it very time-consuming to select the needed information
We propose an approach based on the semi-local diffusion process on the user-object bipartite network to solve the data sparsity problem
If macro-step = 1, the method degenerates to the standard Mass diffusion method
Summary
Owing to the rapid development of the Internet, people are confronted with abundant online contents, which makes it very time-consuming to select the needed information. The preferential diffusion [13], the biased heat conduction [14], network manipulation [15] and the item-oriented method [16] are shown to be able to largely improve the recommendation accuracy for small-degree objects. We propose an approach based on the semi-local diffusion process on the user-object bipartite network to solve the data sparsity problem. Given a is an object selected by user i in the probe set, RSia is the rank of a in i’s recommendation list divided by the total number of uncollected items by user i.
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