让搜索推荐更懂用户，提升点击后转化率。
QueryAgent-R1: Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation
- 用可记忆的智能体框架，边生成查询边验证商品匹配度
- 线上测试点击率升2.9%，引导转化率升3.1%
- 适合电商推荐系统优化与个性化搜索研发
电商平台的查询推荐旨在主动建议符合用户潜在兴趣的搜索词。然而现有方法主要优化查询层面的相关性，忽略了召回商品是否契合用户的后续偏好，导致查询点击率（CTR）高但商品转化率（CVR）低。为此，我们提出QueryAgent-R1，一种基于记忆增强的智能体框架，通过检索链优化实现端到端对齐。该框架将查询生成与真实商品库存检索结合，使智能体能根据召回商品验证并优化查询。我们设计了一致性奖励机制，在智能体强化学习过程中联合优化查询相关性与下游互动效果。此外，构建了高效的用户画像记忆抽象模块。为支持离线评估，我们基于工业数据和公开数据集构建两个新数据集，QueryAgent-R1在多个基准上持续领先。在线上大规模生产平台的A/B测试中，查询CTR提升2.9%，引导转化率提升3.1%。
原文摘要 · Abstract (English)
Query recommendation in e-commerce search aims to proactively suggest queries that match users' potential interests. However, existing methods mainly optimize query-level relevance, while neglecting whether the retrieved products align with users' downstream preferences. This mismatch often leads to high query click through rates (CTR) but low product conversion rates (CVR). To bridge this gap, we propose QueryAgent-R1, a memory-augmented agentic framework that improves end-to-end alignment via chain-of-retrieval optimization. Our QueryAgent-R1 grounds query generation in real inventory retrieval, allowing the agent to validate and refine queries based on retrieved products. We also design a consistency reward in the agentic reinforcement learning (RL) process to jointly optimize query relevance and downstream engagement. In addition, we construct a memory abstraction module for efficient user profiling. To support offline evaluation, we construct two datasets based on both proprietary industrial data and public datasets, on which QueryAgent-R1 consistently outperforms strong baselines. Moreover, on a large scale production platform, QueryAgent-R1 improves Query CTR by 2.9% and guided CVR by 3.1% in online A/B tests.
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