Breaking Recommendation Bias: How Causal RL Delivers Fairness

Modern recommendation algorithms suffer from a critical flaw: they learn from biased data where popular items receive more exposure, reinforcing a self-perpetuating loop that marginalizes niche content. This research combines Graph Neural Networks with Causal Reinforcement Learning to break this cycle and produce fairer, more diverse recommendations. The significance lies in addressing a fundamental fairness issue that affects billions of users' content discovery experiences worldwide.