Conversational Recommendation with Long-Term User Memory for Personalized Travel Planning
Keywords:
conversational recommendation; long-term user memory; travel planning; personalization; dialogue systems; system architecture; governance; fairness; sustainabilityAbstract
Travel planning is a complex decision process characterized by interdependent choices, heterogeneous constraints, and deeply personal preferences that evolve over time. Conversational recommender systems have become a promising paradigm for interactive travel assistance because they support iterative preference elicitation, clarification, and explanation in natural language. However, many existing conversational recommenders are designed as session-bound systems that treat each dialogue as an isolated episode. This paper argues that effective personalized travel planning requires the integration of long-term user memory across sessions, enabling the system to maintain stable preferences, contextual constraints, and interaction histories while adapting to changing user circumstances. The paper examines the architectural foundations of memory-augmented conversational recommendation, including modular dialogue management, memory encoding and retrieval, and recommendation policy design. It presents a system-level analysis of structural trade-offs related to scalability, latency, robustness, privacy, fairness, and sustainability. The discussion emphasizes the need to balance recommendation quality with user autonomy, data governance, and computational cost. Travel planning is used as a representative domain because it exposes the tensions between personalization and persistent user modeling, between rich contextual inference and data minimization, and between model capability and operational efficiency. The paper concludes by outlining future research directions for longitudinal evaluation, adaptive forgetting, federated memory architectures, and governance mechanisms that can support trustworthy long-term user memory in conversational recommender systems.
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