Cross-domain recommendation is a core problem in content-to-e-commerce platforms. Its objective is to leverage user interactions with content to infer potential purchasing intent on the e-commerce side, thereby enhancing conversion rates and commercial value. However, in real industrial scenarios, cross-domain recommendation faces multiple challenges: significant semantic gaps exist between different domains, and user cross-domain behavior sequences are often massive in scale and rich in noise. Although large language models (LLMs) possess powerful semantic understanding and reasoning capabilities, their millisecond-level inference latency makes direct application in online recommendation systems difficult. To address these issues, this paper introduces AIR (Atomic Intent Reasoning), an LLM-driven cross-domain recommendation framework designed for industrial-grade deployment. By migrating LLM inference to the offline phase and dynamically constructing user intent representations through efficient retrieval and composition during online operations, it achieves approximately 400* inference acceleration while maintaining semantic consistency. Experimental results across multiple public datasets demonstrate that our method achieves state-of-the-art performance in cross-domain recommendation tasks. Furthermore, large-scale online A/B testing conducted in Kuaishou E-commerce's real-world business scenarios shows that our approach delivers stable and significant improvements across multiple core business metrics, including a +3.446% increase in GMV, fully validating its effectiveness and practical value in industrial-scale recommendation systems.

翻译：跨域推荐是内容到电商平台中的核心问题，其目标在于利用用户与内容的交互行为来推断其在电商侧的潜在购买意图，从而提升转化率与商业价值。然而，在真实工业场景中，跨域推荐面临多重挑战：不同域之间存在显著语义鸿沟，用户跨域行为序列通常规模庞大且噪声丰富。尽管大语言模型具备强大的语义理解与推理能力，但其毫秒级的推理延迟使其难以直接应用于在线推荐系统。为解决这些问题，本文提出AIR（原子级意图推理），一种适用于工业级部署的大语言模型驱动的跨域推荐框架。通过将大语言模型推理迁移至离线阶段，并在在线运行中通过高效检索与组合动态构建用户意图表示，该方法在保持语义一致性的同时实现了约400倍的推理加速。多个公开数据集上的实验结果表明，我们的方法在跨域推荐任务中达到了最先进的性能。此外，在快手电商真实业务场景下开展的大规模在线A/B测试显示，我们的方法在多个核心业务指标上带来了稳定且显著的提升，包括GMV增长+3.446%，充分验证了其在工业规模推荐系统中的有效性与实用价值。