AIGQ用生成式架构提升电商搜索推荐，更懂用户意图。
AIGQ: An End-to-End Hybrid Generative Architecture for E-commerce Query Recommendation
- 采用列表级监督微调与兴趣引导重排，精准捕捉用户深层意图。
- 引入双组件奖励机制，使推荐结果相关性与多样性同步提升。
- 混合部署架构支持实时生成，适合高并发电商场景使用。
预搜索查询推荐（即淘宝首页的HintQ）在用户意图识别与需求发现中至关重要，但传统方法依赖基于ID的匹配和共点击启发式策略，存在语义浅显、冷启动表现差、惊喜度低等问题。为此，我们提出AIGQ（AI生成查询架构），首个面向HintQ场景的端到端生成式框架。AIGQ包含三项核心创新：首先，提出兴趣感知列表监督微调（IL-SFT），通过会话感知行为聚合与兴趣引导重排构建训练样本，真实建模用户细微意图；其次，设计兴趣感知列表组相对策略优化（IL-GRPO），采用双组件奖励机制联合优化单个查询相关性与整体列表特性，并结合线上点击率（CTR）排名模型的模型驱动奖励；最后，为满足严格实时与低延迟要求，开发混合离线-在线架构，包括用于近线个性化用户到查询生成的AIGQ-Direct，以及增强推理能力、生成触发词到查询映射以丰富兴趣多样性的AIGQ-Think。大量离线评估与淘宝大规模在线A/B实验表明，AIGQ在平台效果与用户参与度的关键业务指标上持续实现显著提升。
原文摘要 · Abstract (English)
Pre-search query recommendation, widely known as HintQ on Taobao's homepage, plays a vital role in intent capture and demand discovery, yet traditional methods suffer from shallow semantics, poor cold-start performance and low serendipity due to reliance on ID-based matching and co-click heuristics. To overcome these challenges, we propose AIGQ (AI-Generated Query architecture), the first end-to-end generative framework for HintQ scenario. AIGQ is built upon three core innovations spanning training paradigm, policy optimization and deployment architecture. First, we propose Interest-Aware List Supervised Fine-Tuning (IL-SFT), a list-level supervised learning approach that constructs training samples through session-aware behavior aggregation and interest-guided re-ranking strategy to faithfully model nuanced user intent. Accordingly, we design Interest-aware List Group Relative Policy Optimization (IL-GRPO), a novel policy gradient algorithm with a dual-component reward mechanism that jointly optimizes individual query relevance and global list properties, enhanced by a model-based reward from the online click-through rate (CTR) ranking model. To deploy under strict real-time and low-latency requirements, we further develop a hybrid offline-online architecture comprising AIGQ-Direct for nearline personalized user-to-query generation and AIGQ-Think, a reasoning-enhanced variant that produces trigger-to-query mappings to enrich interest diversity. Extensive offline evaluations and large-scale online A/B experiments on Taobao demonstrate that AIGQ consistently delivers substantial improvements in key business metrics across platform effectiveness and user engagement.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。