Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomy

The paper surveys how AI copilots—AI-powered assistants for knowledge workers and developers—can personalize their behavior by optimizing user preferences. It reviews how preference signals are collected, modeled at different interaction stages, and refined through feedback loops, and introduces a taxonomy of optimization techniques for pre-, mid-, and post-interaction phases. The study evaluates each technique’s strengths, limitations, and design implications, aiming to unify efforts across AI personalization, human‑AI interaction, and language model adaptation.

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