Long-term effects of recommendation on the evolution of online systems

Zhao, Dan-Dan; Zeng, An; Shang, Ming-Sheng; Gao, Jian

doi:10.1088/0256-307X/30/11/118901

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Journal article

Long-term effects of recommendation on the evolution of online systems

Zhao, Dan-Dan School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
Zeng, An Department of Physics, University of Fribourg, Switzerland
Shang, Ming-Sheng School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
Gao, Jian School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

01.11.2013

Published in:

Chinese Physics Letters. - 2013, vol. 30, no. 11, p. 118901

English 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.

Faculty

Faculté des sciences et de médecine

Department

Département de Physique

Language