Conversational recommendation systems (CRS) aim to elicit user preferences and provide satisfying recommendations through natural language interactions. Existing CRS methods often assume that users have clear and consistent preferences, which may not reflect the reality of user decision-making processes. In this paper, we introduce a novel scenario called Vague Preference Multi-round Conversational Recommendation (VPMCR), which considers users' vague and dynamic preferences in CRS. In the VPMCR setting, we propose a solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two components: Ambiguity-aware Soft Estimation (ASE) and Dynamism-aware Policy Learning (DPL). ASE estimates the vagueness of user feedback and captures their dynamic preferences using a choice-based preference extraction module and a time-aware decaying strategy. DPL leverages the preference distribution estimated by ASE to guide the conversation and adapt to changes in user preferences using a graph-based conversation modeling module and a vague preference policy learning module. We conduct extensive experiments on four real-world datasets and demonstrate the effectiveness of our method in the VPMCR scenario, setting a new benchmark for future research in CRS.

翻译：对话式推荐系统（CRS）旨在通过自然语言交互挖掘用户偏好并提供满意的推荐结果。现有CRS方法通常假定用户具有清晰且一致的偏好，但这往往无法反映用户决策过程的实际情况。本文提出一种名为模糊偏好多轮对话式推荐（VPMCR）的新场景，该场景考虑了CRS中用户模糊且动态的偏好。在VPMCR设定下，我们提出名为自适应模糊偏好策略学习（AVPPL）的解决方案，包含两个核心组件：歧义感知软估计（ASE）与动态感知策略学习（DPL）。ASE通过基于选择的偏好提取模块与时序衰减策略，估计用户反馈的模糊性并捕捉其动态偏好。DPL利用ASE估计的偏好分布引导对话进程，并通过基于图的对话建模模块与模糊偏好策略学习模块适应偏好变化。我们在四个真实数据集上开展大量实验，验证了所提方法在VPMCR场景中的有效性，并为CRS领域的未来研究确立了新基准。