CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
|
|
|
|
Long-Term Effects of Recommendation on the Evolution of Online Systems |
ZHAO Dan-Dan1, ZENG An2**, SHANG Ming-Sheng1**, GAO Jian1 |
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
|
|
Cite this article: |
ZHAO Dan-Dan, ZENG An, SHANG Ming-Sheng et al 2013 Chin. Phys. Lett. 30 118901 |
|
|
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.
|
|
Received: 27 May 2013
Published: 30 November 2013
|
|
PACS: |
89.75.Fb
|
(Structures and organization in complex systems)
|
|
89.65.-s
|
(Social and economic systems)
|
|
89.20.Ff
|
(Computer science and technology)
|
|
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|