Modeling mutual feedback between users and recommender systems

An ZENG, Chi Ho YEUNG, Matúš MEDO, Yi-Cheng ZHANG

Research output: Contribution to journalArticlespeer-review

14 Citations (Scopus)


Recommender systems daily influence our decisions on the Internet. While considerable attention has been given to issues such as recommendation accuracy and user privacy, the long-term mutual feedback between a recommender system and the decisions of its users has been neglected so far. We propose here a model of network evolution which allows us to study the complex dynamics induced by this feedback, including the hysteresis effect which is typical for systems with non-linear dynamics. Despite the popular belief that recommendation helps users to discover new things, we find that the long-term use of recommendation can contribute to the rise of extremely popular items and thus ultimately narrow the user choice. These results are supported by measurements of the time evolution of item popularity inequality in real systems. We show that this adverse effect of recommendation can be tamed by sacrificing part of short-term recommendation accuracy. Copyright © 2015 IOP Publishing Ltd and SISSA Medialab srl.
Original languageEnglish
Article numberP07020
JournalJournal of Statistical Mechanics: Theory and Experiment
Publication statusPublished - Jul 2015


Zeng, A., Yeung, C. H., Medo, M., & Zhang, Y.-C. (2015). Modeling mutual feedback between users and recommender systems. Journal of Statistical Mechanics: Theory and Experiment, 2015. Retrieved from


  • Analysis of algorithms
  • Network dynamics
  • Online dynamics


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