  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.
