标题： 上下文知识的协同集成与差异解决用于个性化推荐 显示英文标题

标题： Synergistic Integration and Discrepancy Resolution of Contextualized Knowledge for Personalized Recommendation

摘要： 大型语言模型（LLMs）与推荐系统的集成通过其提取世界知识以增强推理能力的能力展现了有前景的潜力。 然而，当前采用静态模式提示机制的方法面临显著限制：（1）它们使用通用模板结构，忽视了用户偏好多样性的多方面特性；（2）它们在语义知识表示和行为特征空间之间实现的是表面一致，而未能达到全面的潜在空间整合。 为解决这些挑战，我们引入了CoCo，这是一个通过双机制方法动态构建用户特定上下文知识嵌入的端到端框架。 我们的方法通过自适应知识融合和矛盾解决模块实现了语义和行为潜在维度的深入整合。 在多种基准数据集和一个企业级电子商务平台上的实验评估表明 CoCo的优势，在推荐准确性方面比七种最先进的方法最大提升了8.58%。 该框架在生产广告系统中的部署带来了1.91%的销售增长，验证了其实际有效性。 凭借其模块化设计和与模型无关的架构，CoCo为需要知识增强推理和个性化适应的下一代推荐系统提供了一个多功能解决方案。 显示英文摘要

摘要： The integration of large language models (LLMs) into recommendation systems has revealed promising potential through their capacity to extract world knowledge for enhanced reasoning capabilities. However, current methodologies that adopt static schema-based prompting mechanisms encounter significant limitations: (1) they employ universal template structures that neglect the multi-faceted nature of user preference diversity; (2) they implement superficial alignment between semantic knowledge representations and behavioral feature spaces without achieving comprehensive latent space integration. To address these challenges, we introduce CoCo, an end-to-end framework that dynamically constructs user-specific contextual knowledge embeddings through a dual-mechanism approach. Our method realizes profound integration of semantic and behavioral latent dimensions via adaptive knowledge fusion and contradiction resolution modules. Experimental evaluations across diverse benchmark datasets and an enterprise-level e-commerce platform demonstrate CoCo's superiority, achieving a maximum 8.58% improvement over seven cutting-edge methods in recommendation accuracy. The framework's deployment on a production advertising system resulted in a 1.91% sales growth, validating its practical effectiveness. With its modular design and model-agnostic architecture, CoCo provides a versatile solution for next-generation recommendation systems requiring both knowledge-enhanced reasoning and personalized adaptation.