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

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

Chin. Phys. Lett.

2013, Vol. 30

Issue (11): 118901 DOI: 10.1088/0256-307X/30/11/118901

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 An^2**, SHANG Ming-Sheng^1**, 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

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ZHAO Dan-Dan, ZENG An, SHANG Ming-Sheng et al 2013 Chin. Phys. Lett. 30 118901

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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)

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https://cpl.iphy.ac.cn/10.1088/0256-307X/30/11/118901 OR https://cpl.iphy.ac.cn/Y2013/V30/I11/118901

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	ZHAO Dan-Dan
	ZENG An
	SHANG Ming-Sheng
	GAO Jian

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