Modeling mutual feedback between users and recommender systems
This addresses the problem of unintended long-term effects in recommender systems for users and platforms, highlighting a significant adverse impact beyond short-term accuracy.
The study modeled the long-term mutual feedback between users and recommender systems, finding that this feedback can lead to extreme item popularity and narrow user choice, supported by real-world data showing increased inequality in item popularity over time.
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.