WeGR: Unified Generative Retrieval for Industrial Search Systems in WeChat
Generative retrieval (GR) offers a promising paradigm for industrial search by unifying candidate generation within an autoregressive framework. However, its effectiveness is limited by two key challenges. First, existing Semantic ID (SID) tokenizers struggle to incorporate collaborative signals while maintaining effective codebook utilization, limiting their ability to distinguish items and achieve high-quality retrieval. Second, existing reinforcement learning (RL) methods suffer from severe reward sparsity under exact SID matching, leaving many sampled trajectories without effective learning signals and hindering preference optimization. To address these challenges, we propose WeGR, a ...
| Authors | Jiajun Han, Guoxin Ma, Felix et al. |
|---|---|
| Primary category | cs.IR |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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