Cross-Domain User Intent Modeling for Intelligent Conversational Search Systems
Keywords:
conversational search; cross-domain intent modeling; neural information retrieval; personalization; context-aware retrieval; fairness; system governanceAbstract
Intelligent conversational search systems must interpret user intent across multiple interaction turns and across heterogeneous application domains. This paper develops a system-level analysis of cross-domain user intent modeling for such systems, focusing on structural trade-offs rather than on a single algorithmic solution. The discussion begins with the conceptual foundations of user intent in conversational and contextual information retrieval, emphasizing that intent is dynamically negotiated through dialogue and is shaped by domain-specific constraints. It then examines architectural strategies for shared representation learning, domain adaptation, and conversational state management. A central concern is the tension between model sharing and domain isolation, because shared encoders improve cross-domain transfer while domain-specific adapters improve accountability and maintainability. The paper further discusses personalization and context-aware retrieval in local categorical query settings, drawing attention to the importance of context signals and dialogue history. Robustness, fairness, and governance are analyzed as first-order design constraints rather than post-deployment afterthoughts. The paper considers bias amplification, transparency, auditability, and the risks of large pretrained models. Deployment and operational sustainability are addressed through modular architectures, learning-to-rank components, and continuous evaluation. Finally, the policy implications of cross-domain intent modeling are discussed, including data governance, disparate impact, and the need for accountability frameworks. The paper argues that sustainable conversational search systems require coordinated attention to representation, infrastructure, fairness, deployment, and governance rather than isolated optimization of predictive accuracy.
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