Structural centrality of networks can improve the diffusion-based recommendation algorithm

Abstract

The recommendation system has become an indispensable information technology in the real world. The recommendation system based on the diffusion model has been widely used because of its simplicity, scalability, interpretability, and many other advantages. However, the traditional diffusion-based recommendation model only uses the nearest neighbor information, which limits its efficiency and performance. Therefore, in this article, we introduce the centralities of complex networks into the diffusion-based recommendation system and test its performance. The results show that the overall performance of heat conduction algorithm can be improved by 184%–280%, using the centrality of complex networks, reaching almost the same accuracy level as the mass diffusion algorithm. Therefore, the recommendation system combining the high-order network structure information is a potentially promising research direction in the future.