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

Dan Dan Zhao, An Zeng*, Ming Sheng Shang, Jian Gao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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.

Original languageEnglish (US)
Article number118901
JournalChinese Physics Letters
Issue number11
StatePublished - Nov 2013

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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