Cross-Domain Transfer Learning for Personalized Recommendation and Dialogue State Prediction
Keywords:
cross-domain transfer learning, personalized recommendation, dialogue state prediction, system architecture, fairness, data governance, sustainable machine learningAbstract
Modern human-centered artificial intelligence systems increasingly depend on the ability to model user intent across heterogeneous interaction channels. Personalized recommendation and dialogue state prediction are two tasks that, despite their distinct output spaces, share substantial representational structure related to preference inference, sequential context, and user modeling. Cross-domain transfer learning offers a unifying systems perspective for reusing knowledge across these tasks, but it also introduces architectural complexity, governance challenges, and operational risk. This paper presents a system-level examination of cross-domain transfer learning as applied to personalized recommendation and dialogue state prediction. It considers foundational modeling approaches, architectural design spaces, data governance requirements, fairness constraints, sustainability concerns, and deployment trade-offs. Rather than focusing on a single algorithmic contribution, the analysis emphasizes structural choices such as shared encoders, modular heads, domain adaptation mechanisms, knowledge distillation, and gateway-based orchestration. The paper further discusses how cross-domain transfer can amplify bias, create hidden technical dependencies, complicate privacy compliance, and alter carbon footprints. Policy implications are examined through the lens of trustworthy artificial intelligence, data protection law, and institutional accountability. The discussion highlights that successful cross-domain systems require coordinated governance across data, model, infrastructure, and evaluation layers, not merely improved predictive accuracy.
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