Degenerate Feedback Loops in Recommender Systems

Understand

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.