Breaking Feedback Loops in Recommender Systems with Causal Inference

This academic paper introduces **causal adjustment for feedback loops (cafl)**, an innovative algorithm designed to mitigate the detrimental effects of feedback loops in **recommender systems**. It highlights how these systems, by influencing user behavior and then retraining on that data, can **compromise recommendation quality and homogenize user preferences**. The authors propose that reasoning about **causal quantities**—specifically, intervention distributions of recommendations on user ratings—can break these loops without resorting to random recommendations, preserving utility. Through **empirical studies** in simulated environments, cafl is shown to **improve predictive performance** and **reduce homogenization** compared to existing methods, even under conditions where standard causal assumptions like positivity are violated.