Towards Personalized Conversational Sales Agents with Contextual User Profiling for Strategic Action

Conversational Recommender Systems (CRSs) aim to engage users in dialogue to provide tailored recommendations. While traditional CRSs focus on eliciting preferences and retrieving items, real-world e-commerce interactions involve more complex decision-making, where users consider multiple factors beyond simple attributes. To bridge this gap, we introduce Conversational Sales (CSales), a novel task that unifies preference elicitation, recommendation, and persuasion to better support user decision-making. For a realistic evaluation of CSales, we present CSUser, an LLM-based user simulator constructed from real-world data, modeling diverse user profiles with needs and personalities. Additionally, we propose CSI, a conversational sales agent that proactively infers contextual profiles through dialogue for personalized action planning. Extensive experiments demonstrate that CSUser effectively replicates real-world users and emphasize the importance of contextual profiling for strategic action selection, ultimately driving successful purchases in e-commerce.