何处与何物：在情境对话推荐中推理动态和隐性偏好
Where and What: Reasoning Dynamic and Implicit Preferences in Situated Conversational Recommendation
AI总结：
本文提出SiPeR框架，通过场景转换估计和贝叶斯逆推推理，提升情境对话推荐的准确性和响应质量。
摘要：
情境对话推荐（SCR）通过结合视觉场景和自然语言对话提供上下文合适的推荐，因其与现实场景的紧密关联而成为研究热点。与传统推荐相比，SCR需要更深入理解动态和隐性用户偏好，因为周围场景常影响用户兴趣，且两者可能在对话中演变。为解决这一问题，本文提出SiPeR框架，整合场景转换估计和贝叶斯逆推推理，通过估计当前场景是否满足用户需求并引导用户至更合适的场景，以及利用多模态大语言模型的似然性预测候选物品的用户偏好。实验表明SiPeR在推荐准确性和响应生成质量上均优于现有方法。代码和数据可在https://github.com/DongdingLin/SiPeR获取。
英文摘要：
Situated conversational recommendation (SCR), which utilizes visual scenes grounded in specific environments and natural language dialogue to deliver contextually appropriate recommendations, has emerged as a promising research direction due to its close alignment with real-world scenarios. Compared to traditional recommendations, SCR requires a deeper understanding of dynamic and implicit user preferences, as the surrounding scene often influences users' underlying interests, while both may evolve across conversations. This complexity significantly impacts the timing and relevance of recommendations. To address this, we propose situated preference reasoning (SiPeR), a novel framework that integrates two core mechanisms: (1) Scene transition estimation, which estimates whether the current scene satisfies user needs, and guides the user toward a more suitable scene when necessary; and (2) Bayesian inverse inference, which leverages the likelihood of multimodal large language models (MLLMs) to predict user preferences about candidate items within the scene. Extensive experiments on two representative benchmarks demonstrate SiPeR's superiority in both recommendation accuracy and response generation quality. The code and data are available at https://github.com/DongdingLin/SiPeR.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
