Long-Term Effects of Recommendation on the Evolution of Online Systems  

作　　者：ZHAO Dan-Dan ZENG An SHANG Ming-Sheng GAO Jian 赵丹丹;曾安;尚明生;高见(School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 610054;Department of Physics,University of Fribourg,Chemin du Musée 3,CH-1700 Fribourg,Switzerland)

机构地区：[1]School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 610054 [2]Department of Physics,University of Fribourg,Chemin du Musée 3,CH-1700 Fribourg,Switzerland

出　　处：《Chinese Physics Letters》2013年第11期211-214,共4页中国物理快报（英文版）

基　　金：Supported by the National Natural Science Foundation of China under Grant No 61370150;the Sichuan Provincial Science and Technology Department(2012FZ0120);the Fundamental Research Fund for the Central Universities under Grant No ZYGX2012J075.

摘　　要：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.

关 键 词：BIPARTITE CORRELATION EVOLUTION 

分 类 号：O41[理学—理论物理]