Quality Over Clicks: Iterative Reinforcement Learning for Early-Stage E-Commerce Query Suggestion

arXiv:2603.22922v2 Announce Type: replace Abstract: Existing dialogue systems rely on query suggestion to enhance user engagement. Recent approaches mainly optimize generative models using click-through rate (CTR) models to align with user preferences. However, these methods are less effective in early-stage deployment scenarios, where click feedback is sparse and insufficient for training a reliable CTR model. To bridge this gap, we propose QualEQS, a quality-first iterative reinforcement learning framework for e-commerce query suggestion. We formalize actionable suggestion quality along thre