# Degenerate Feedback Loops in Recommender Systems

## TL;DR

This paper provides a theoretical analysis of feedback loops in recommender systems, highlighting how they create echo chambers and filter bubbles, and proposes practical solutions to mitigate system degeneracy.

## Contribution

It offers a novel theoretical framework to distinguish between echo chambers and filter bubbles and suggests practical methods to slow system degeneracy.

## Key findings

- Disentangles user dynamics from recommender behavior
- Identifies mechanisms leading to feedback loop degeneracy
- Proposes solutions to mitigate system degeneracy

## Abstract

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.