THE FILTER BUBBLE PROBLEM IN RECOMMENDATION SYSTEMS: CAUSES AND MITIGATIONS

Recommendation systems shape what billions of users encounter online, yet their reliance on engagement optimization creates filter bubbles that reinforce existing preferences. This article examines how collaborative filtering, content-based methods, and deep learning produce this narrowing effect, drawing on cases from YouTube, Facebook, TikTok, and e-commerce platforms. A simulation quantifies the diversity decline under engagement-only versus diversity-aware recommendation. The article evaluates mitigation strategies and proposes a three-stage conceptual framework for intervention in the filter bubble lifecycle.