Journal article

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

  • Zhao, Dan-Dan (赵丹丹) School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
  • Zeng, An (曾安) Department of Physics, University of Fribourg, Switzerland
  • Shang, Ming-Sheng (尚明生) School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
  • Gao, Jian (高见) School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
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    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
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/303466
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