标题： 通过动态对齐、多模态融合和证据基础解释连接协同过滤与大型语言模型 显示英文标题

标题： Bridging Collaborative Filtering and Large Language Models with Dynamic Alignment, Multimodal Fusion and Evidence-grounded Explanations

摘要： 最近的研究通过将用户交互历史和项目元数据转换为文本提示，然后让大型语言模型生成排名或推荐，探索了使用大型语言模型进行推荐任务。 一种有前景的方法是通过紧凑的适配器网络将协同过滤知识连接到语言模型表示，这在避免昂贵微调的同时保留了两个组件的优势。 然而在实践中仍存在几个挑战：协同过滤模型通常使用静态快照，无法捕捉快速变化的用户偏好；许多现实世界的项目包含超出文本描述的丰富视觉和音频内容；而当前系统难以提供有具体证据支持的可信解释。 我们的工作引入了\model{}，一个通过三个关键创新解决这些限制的框架。 我们开发了一种在线适应机制，通过轻量级模块持续整合新的用户交互，避免重新训练大型模型的需求。 我们创建了一个统一的表示，无缝结合协同信号与视觉和音频特征，处理某些模态可能不可用的情况。 最后，我们设计了一个解释系统，将推荐基于特定的协同模式和项目属性，生成用户可以验证的自然语言推理。 我们的方法保持了冻结基础模型的效率，同时增加了最小的计算开销，使其适用于实际部署。 显示英文摘要

摘要： Recent research has explored using Large Language Models for recommendation tasks by transforming user interaction histories and item metadata into text prompts, then having the LLM produce rankings or recommendations. A promising approach involves connecting collaborative filtering knowledge to LLM representations through compact adapter networks, which avoids expensive fine-tuning while preserving the strengths of both components. Yet several challenges persist in practice: collaborative filtering models often use static snapshots that miss rapidly changing user preferences; many real-world items contain rich visual and audio content beyond textual descriptions; and current systems struggle to provide trustworthy explanations backed by concrete evidence. Our work introduces \model{}, a framework that tackles these limitations through three key innovations. We develop an online adaptation mechanism that continuously incorporates new user interactions through lightweight modules, avoiding the need to retrain large models. We create a unified representation that seamlessly combines collaborative signals with visual and audio features, handling cases where some modalities may be unavailable. Finally, we design an explanation system that grounds recommendations in specific collaborative patterns and item attributes, producing natural language rationales users can verify. Our approach maintains the efficiency of frozen base models while adding minimal computational overhead, making it practical for real-world deployment.