Personalized Conversational Search with Large Language Models for Long-Term User Preference Modeling
Keywords:
conversational search, large language models, user modeling, personalization, retrieval-augmented generation, algorithmic governanceAbstract
The design of personalized conversational search systems is undergoing a fundamental shift as large language models become central components of query interpretation, response generation, and retrieval planning. Although these models provide strong semantic generalization, they do not automatically maintain durable, user-specific knowledge over long horizons. This paper develops a system-level analysis of large language model based conversational search architectures that incorporate long-term user preference modeling as an explicit infrastructure concern. It examines the decomposition of user knowledge into episodic interaction state and persistent preference memory, and it discusses how retrieval-augmented generation, dense retrieval, and reranking can interact with governed user models. The paper highlights architectural trade-offs in memory placement, representation granularity, update frequency, latency, index freshness, and forgetting. It then addresses evaluation weaknesses in offline metrics for personalization, the delayed nature of preference feedback, and the challenges of aligning user models with privacy, consent, auditability, and fairness requirements. Through cross-domain comparisons with recommendation and enterprise knowledge systems, the paper argues that sustainable personalization depends less on model scale than on the organizational and technical capacity to reconcile conflicting preference signals, support correction, and maintain audit trails. The discussion concludes with policy implications for multi-tenant deployments under emerging artificial intelligence governance frameworks.
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