In this episode of The Data Science Podcast, Lucas and Luna explore how recommendation algorithms create filter bubbles that trap users in narrowing loops. Using a real example from a major social media platform's newsfeed algorithm in early 2026, they break down the mechanics behind collaborative filtering, the feedback loop that causes category convergence, and the one metric engineers use to detect overspecialization. Lucas explains the mathematical shift from maximizing engagement to measuring content diversity, and Luna pushes back on whether platforms actually want to fix the problem. The conversation covers edge-case exposure, the cold-start problem for new creators, and why recommending 'boring but diverse' content is harder than optimizing for clicks. A must-hear for anyone working on recommendation systems or thinking about the ethics of personalization.

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