标题： 修订：电商视觉搜索系统优化的反射意图挖掘和在线推理辅助 显示英文标题

标题： REVISION:Reflective Intent Mining and Online Reasoning Auxiliary for E-commerce Visual Search System Optimization

摘要： 在淘宝电商视觉搜索中，用户行为分析揭示了大量无点击请求，表明用户意图多样且隐晦。 这些意图以多种形式表达，难以挖掘和发现，从而导致平台策略的适应性有限和滞后。 这极大地限制了用户表达多样化意图的能力，并阻碍了视觉搜索系统的可扩展性。 用户隐式意图表达与系统响应之间的不匹配定义了User-SearchSys意图差异。 为缓解这一问题，我们提出了一种新框架REVISION。 该框架将离线推理挖掘与在线决策和执行相结合，使策略能够自适应地解决隐式用户需求。 在离线阶段，我们构建了一个周期性流程，从历史无点击请求中挖掘差异。 利用大模型，我们分析隐式意图因素，并通过联合推理查询和产品元数据来推断最佳建议。 这些推断出的建议作为改进平台策略的操作性洞察。 在在线阶段，REVISION-R1-3B在整理好的离线数据上进行训练，对查询图像和相关历史产品进行全面分析，生成优化方案并在搜索流程中自适应地调度策略。 我们的框架提供了一种简化的方法，将大模型与传统搜索系统集成，实现了信息聚合和用户交互中的端到端智能优化。 实验结果表明，我们的方法提高了从大规模搜索日志中挖掘隐式意图的效率，并显著降低了无点击率。 显示英文摘要

摘要： In Taobao e-commerce visual search, user behavior analysis reveals a large proportion of no-click requests, suggesting diverse and implicit user intents. These intents are expressed in various forms and are difficult to mine and discover, thereby leading to the limited adaptability and lag in platform strategies. This greatly restricts users' ability to express diverse intents and hinders the scalability of the visual search system. This mismatch between user implicit intent expression and system response defines the User-SearchSys Intent Discrepancy. To alleviate the issue, we propose a novel framework REVISION. This framework integrates offline reasoning mining with online decision-making and execution, enabling adaptive strategies to solve implicit user demands. In the offline stage, we construct a periodic pipeline to mine discrepancies from historical no-click requests. Leveraging large models, we analyze implicit intent factors and infer optimal suggestions by jointly reasoning over query and product metadata. These inferred suggestions serve as actionable insights for refining platform strategies. In the online stage, REVISION-R1-3B, trained on the curated offline data, performs holistic analysis over query images and associated historical products to generate optimization plans and adaptively schedule strategies across the search pipeline. Our framework offers a streamlined paradigm for integrating large models with traditional search systems, enabling end-to-end intelligent optimization across information aggregation and user interaction. Experimental results demonstrate that our approach improves the efficiency of implicit intent mining from large-scale search logs and significantly reduces the no-click rate.