#fairness-in-ai News & Analysis

Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders

Researchers have identified and addressed popularity bias in Generative Recommenders (GRs), a emerging class of AI systems that use unified end-to-end frameworks for recommendations. The study reveals that this bias stems from token-level optimization flaws and undifferentiated item tokenization, proposing Ghost, a novel system using asymmetric unlikelihood optimization and skeleton-founded tokenization to mitigate the problem while maintaining recommendation quality.