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arXiv AI By Jiajun Han, Guoxin Ma, Felix, Liangsheng Xu, Ruiqi Yu, Yingdi Guo, Zongbing Wang, Xinhua Ye, Chenyan Hua, Xin Xie, Pengcheng Yang, Haijun Wu

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

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WeGR is a unified generative retrieval framework that tackles two key challenges in industrial search: preserving collaborative signals in tokenization and mitigating reward sparsity in reinforcement learning. It introduces the Collaborative and Collapse-free Tokenizer (CoCo‑VAE) to embed query‑to‑item and item‑to‑item signals into continuous representations before quantization, while also refreshing underutilized codewords to maintain balanced codebook usage. For preference alignment, Dynamic Horizon Policy Optimization (DHPO) expands reward evaluation horizons to enable coarse‑to‑fine learning from SID prefixes to full items without auxiliary reward models. Extensive offline tests and a six‑week online A/B test on a large‑scale e‑commerce platform demonstrate that WeGR improves retrieval and ranking performance, yielding relative gains of 2.539% in orders, 1.508% in CTR, and 1.381% in CVR, and reduces CPU and GPU usage by 25.5% and 26.1% respectively.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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