  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.
