SOURCE-LINKED INTELLIGENCE

MERIT: Mitigating Exposure Bias in Generative XMC for User-Interest Propensity Modeling

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

Matching users to interest categories at scale is central to personalized shopping, but the task is challenging in large e-commerce platforms, where label spaces continually evolve and user-interest signals are sparse and long-tailed. Autoregressive language models are appealing because their world knowledge and semantic priors over descriptors generalize across extreme label spaces and accommodate multiple valid label assignments. Yet under teacher-forced fine-tuning, inference-time predictions become part of the conditioning context: early errors steer later outputs toward co-occurring label

Read original source ↗ Open in workspace

recordType

paper

region

Global