Faculté des sciences

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

Zhao, Dan-Dan (赵丹丹) ; Zeng, An (曾安) ; Shang, Ming-Sheng (尚明生) ; Gao, Jian (高见)

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

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... More

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    Summary
    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.