From Dialogue to Destination: Geography-Aware Large Language Models with Multimodal Fusion for Conversational Recommendation

Conversational Recommender Systems (CRS) aim to provide personalized recommendations by interacting with users through natural language dialogue. However, in scenarios requiring deep geospatial awareness, existing methods, including those based on Large Language Models (LLMs), still face significant challenges in effectively fusing heterogeneous, multimodal geographic information with dynamic dialogue context. Simple fusion strategies struggle to resolve the asymmetric dependencies between dynamic user intent and static geographic context and fail to bridge the semantic gap between LLMs and structured geospatial data. To address these issues, we propose a framework for geography-aware CRS, named GeoCRS. Our core idea is to empower a frozen LLM with powerful geospatial reasoning capabilities by conditioning it on a dynamic, multimodal guidance signal generated by an external fusion architecture, all without altering the LLM's internal parameters. Specifically, we first design a hierarchical geographical encoder to uniformly represent heterogeneous geographic data. Subsequently, we introduce a contextual feature modulation module that asymmetrically injects the geographic context into the user's dialogue intent via a novel modulation mechanism to improve conversational recommendation via both geographic and dialogue context. Extensive experiments on public benchmark datasets demonstrate that our proposed GeoCRS significantly outperforms state-of-the-art baselines on the geography-aware conversational recommendation task.