Proactive intent prediction is a critical capability in modern e-commerce chatbots, enabling "zero-query" recommendations by anticipating user needs from behavioral and contextual signals. However, existing industrial systems face two fundamental challenges: (1) the semantic gap between discrete user features and the semantic intents within the chatbot's Knowledge Base, and (2) the objective misalignment between general-purpose LLM outputs and task-specific ranking utilities. To address these issues, we propose RGAlign-Rec, a closed-loop alignment framework that integrates an LLM-based semantic reasoner with a Query-Enhanced (QE) ranking model. We also introduce Ranking-Guided Alignment (RGA), a multi-stage training paradigm that utilizes downstream ranking signals as feedback to refine the LLM's latent reasoning. Extensive experiments on a large-scale industrial dataset from Shopee demonstrate that RGAlign-Rec achieves a 0.12% gain in GAUC, leading to a significant 3.52% relative reduction in error rate, and a 0.56% improvement in Recall@3. Online A/B testing further validates the cumulative effectiveness of our framework: the Query-Enhanced model (QE-Rec) initially yields a 0.98% improvement in CTR, while the subsequent Ranking-Guided Alignment stage contributes an additional 0.13% gain. These results indicate that ranking-aware alignment effectively synchronizes semantic reasoning with ranking objectives, significantly enhancing both prediction accuracy and service quality in real-world proactive recommendation systems.

翻译：主动意图预测是现代电商聊天机器人的一项关键能力，它能够通过行为与上下文信号预测用户需求，从而实现“零查询”推荐。然而，现有的工业系统面临两个根本性挑战：（1）离散的用户特征与聊天机器人知识库中语义意图之间的语义鸿沟；（2）通用大语言模型输出与特定任务排序效用之间的目标错位。为解决这些问题，我们提出了RGAlign-Rec，一个将基于大语言模型的语义推理器与查询增强排序模型相结合的闭环对齐框架。我们还引入了排序引导对齐，这是一种多阶段训练范式，利用下游排序信号作为反馈来优化大语言模型的潜在推理。在Shopee的大规模工业数据集上进行的大量实验表明，RGAlign-Rec在GAUC指标上实现了0.12%的提升，导致错误率相对显著降低了3.52%，并在Recall@3指标上提高了0.56%。在线A/B测试进一步验证了我们框架的累积有效性：查询增强模型最初带来了0.98%的点击率提升，而随后的排序引导对齐阶段则贡献了额外的0.13%增益。这些结果表明，具备排序感知的对齐能有效同步语义推理与排序目标，显著提升了现实世界主动推荐系统的预测准确性与服务质量。