1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 2Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland
Abstract：We employ a bipartite network to describe an online commercial system. Instead of investigating accuracy and diversity in each recommendation, we focus on studying the influence of recommendation on the evolution of the online bipartite network. The analysis is based on two benchmark datasets and several well-known recommendation algorithms. The structure properties investigated include item degree heterogeneity, clustering coefficient and degree correlation. This work highlights the importance of studying the effects and performance of recommendation in long-term evolution.
2013, Vol. 30 
