Effect of initial configuration on network-based recommendation

¹  Department of Physics, University of Fribourg - Chemin du Muse 3, CH-1700 Fribourg, Switzerland
²  Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China Hefei Anhui, 230026, PRC
³  Information Economy and Internet Research Laboratory, University of Electronic Science and Technology of China - Chengdu Sichuan, 610054, PRC

Corresponding authors: zhutou@ustc.edu yi-cheng.zhang@unifr.ch

Received: 15 November 2007
Accepted: 15 January 2008

Abstract

In this paper, based on a weighted object network, we propose a recommendation algorithm, which is sensitive to the configuration of initial resource distribution. Even under the simplest case with binary resource, the current algorithm has remarkably higher accuracy than the widely applied global ranking method and collaborative filtering. Furthermore, we introduce a free parameter to regulate the initial configuration of resource. The numerical results indicate that decreasing the initial resource located on popular objects can further improve the algorithmic accuracy. More significantly, we argue that a better algorithm should simultaneously have higher accuracy and be more personal. According to a newly proposed measure about the degree of personalization, we demonstrate that a degree-dependent initial configuration can outperform the uniform case for both accuracy and personalization strength.