Note: This note is a work-in-progress, created for the course CS 329S: Machine Learning Systems Design (Stanford, 2022). For the fully developed text, see th...

Predictive models are rarely static -- operationalized models typically have an update cadence. At Mobilewalla, for instance, our models are updated every 30–180 days. At the end of each update period, the model is revised based on assessing the fidelity of its output since the last update. This is an important component of standard model maintenance practice, and is known as the feedback loop. A degenerate feedback loop (DFL) occurs when this prior output unfairly impacts future outcomes.

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.