1.Business School,
University of Shanghai for Science and Technology, Shanghai 200093,
China；Department of Physics,
University of Fribourg, Chemin du Musée 3, CH-1700, Switzerland; 2.Research Center
of Complex Systems Science, Shanghai University of Science and Technology,
Shanghai 200093, China；Department of Modern
Physics, University of Science and Technology of China, Hefei 230026,
China；Department of Physics,
University of Fribourg, Chemin du Musée 3, CH-1700, Switzerland; 3.Research Center
of Complex Systems Science, Shanghai University of Science and Technology,
Shanghai 200093, China；Department of Modern
Physics, University of Science and Technology of China, Hefei 230026,
China;
Show less
History+
Published Online
2009-09-05
PDF
(301KB)
Abstract
In this paper, we present an improved collaborative filtering (ICF) algorithm by using the heat diffusion process to generate the user correlation. This algorithm has remarkably higher accuracy than the standard collaborative filtering (CF) using Pearson correlation. Furthermore, we introduce a free parameter β to regulate the contributions of objects to user correlation. The numerical simulation results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy and diversity.
PDF
(301KB)
