WeGR: Towards Unified Generative Retrieval via Behavior-aware Tokenization and Preference Alignment

arXiv:2610.16979v2 Announce Type: replace-cross Abstract: Generative retrieval (GR) offers a promising paradigm for industrial search by unifying candidate generation within an autoregressive framework. However, effectively leveraging user behaviors in industrial GR remains challenging: existing SID tokenization methods struggle to preserve collaborative signals while maintaining balanced codebook utilization, whereas reinforcement learning (RL) for prioritizing high-value candidates suffers from sparse rewards when generated SIDs fail to match positive items. To address these challenges, we p

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