EAGER：从电商搜索点击商品中富化并对齐的生成式查询推荐
EAGER: Enrich-and-Align Generative Query Recommendation from Clicked Items in E-commerce Search
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- Alibaba International Digital Commerce Group（阿里巴巴国际数字商业集团）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对电商查询推荐中现有方法忽视长尾意图和平台知识的问题，提出EAGER两阶段框架，通过富化阶段课程式SFT和对齐阶段GRPO混合奖励，从点击商品生成个性化查询建议，并经离线实验和在线A/B测试验证有效性。
中文摘要 AI 辅助
电商平台越来越多地在用户信息流中展示可点击的查询建议，使用户无需手动重新表述查询即可细化或扩展其意图。现有方法要么从历史日志中挖掘建议——这受限于过去的行为，且对长尾、个性化意图视而不见——要么依赖现成的LLM，这些模型缺乏平台特定知识，生成的查询虽然流畅但过于通用，与实际点击行为脱节。我们提出EAGER（Enrich-and-AliGn gEnerative Query Recommendation），一个从点击商品中生成查询建议的两阶段框架。在富化阶段，监督微调（SFT）遵循一个四阶段课程，该课程扩展信息丰富度（从仅商品到用户条件）和推理深度（从直接到思维链）。每个阶段都包含理由增强、多样性正则化和自蒸馏。在对齐阶段，我们通过GRPO进行后训练，使用多个基于规则的业务信号和偏好感知点击奖励的混合奖励。广泛的离线实验和在线A/B测试证明了EAGER的有效性，该系统已在一个大型电商平台的生产环境中部署。
英文摘要
E-commerce platforms increasingly display clickable query suggestions alongside items in the user feed, enabling users to refine or expand their intent without manually reformulating queries. Existing approaches either mine suggestions from historical logs -- limited to past behavior and blind to long-tail, personalized intents -- or rely on off-the-shelf LLMs whose lack of platform-specific knowledge yields fluent but generic queries disconnected from real click behavior. We propose EAGER (Enrich-and-AliGn gEnerative Query Recommendation), a two-stage framework for generating query suggestions from clicked items. In the enrichment stage, supervised fine-tuning (SFT) follows a four-stage curriculum that scales information richness (from item-only to user-conditioned) and reasoning depth (from direct to chain-of-thought). Each stage incorporates rationale augmentation, diversity regularization, and self-distillation. In the alignment stage, we post-train via GRPO with a hybrid reward of multiple rule-based business signals and a preference-aware click reward. Extensive offline experiments and online A/B test demonstrate the effectiveness of EAGER, which has been deployed in production at a major e-commerce platform.
