Modeling mutual feedback between users and recommender systems
- Zeng, An Science, Beijing Normal University, Beijing, China - Department of Physics, University of Fribourg, Switzerland
- Yeung, Chi Ho Department of Science and Environmental Studies, The Hong Kong Institute of Education
- Medo, Matúš Department of Physics, University of Fribourg, Switzerland
- Zhang, Yi-Cheng Department of Physics, University of Fribourg, Switzerland
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17.07.2015
Published in:
- Journal of Statistical Mechanics: Theory and Experiment. - 2015, vol. 2015, no. 7, p. P07020
English
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.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département de Physique
- Language
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- English
- Classification
- Physics
- License
- License undefined
- Identifiers
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- RERO DOC 257367
- DOI 10.1088/1742-5468/2015/07/P07020
- Persistent URL
- https://folia.unifr.ch/unifr/documents/304540
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