Degenerate Feedback Loops in Recommender Systems
This addresses societal and user-level issues in recommender systems, but it is incremental as it builds on existing concerns in an underexplored area.
The paper tackled the problem of feedback loops in recommender systems, which can lead to echo chambers and filter bubbles, by providing a theoretical analysis to disentangle these effects and offering practical solutions to slow down system degeneracy.
Machine learning is used extensively in recommender systems deployed in products. The decisions made by these systems can influence user beliefs and preferences which in turn affect the feedback the learning system receives - thus creating a feedback loop. This phenomenon can give rise to the so-called "echo chambers" or "filter bubbles" that have user and societal implications. In this paper, we provide a novel theoretical analysis that examines both the role of user dynamics and the behavior of recommender systems, disentangling the echo chamber from the filter bubble effect. In addition, we offer practical solutions to slow down system degeneracy. Our study contributes toward understanding and developing solutions to commonly cited issues in the complex temporal scenario, an area that is still largely unexplored.