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

TL;DR

This paper provides a comprehensive survey and taxonomy of preference optimization techniques in AI copilots, highlighting how personalization enhances user experience and outlining methods across interaction stages.

Contribution

It introduces a unified taxonomy of preference optimization in AI copilots, synthesizing diverse approaches and offering practical design insights for user-aligned AI systems.

Findings

Defined a taxonomy of preference optimization techniques

Analyzed advantages and limitations of each technique

Provided design implications for personalized AI copilots

Abstract

AI copilots represent a new generation of AI-powered systems designed to assist users, particularly knowledge workers and developers, in complex, context-rich tasks. As these systems become more embedded in daily workflows, personalization has emerged as a critical factor for improving usability, effectiveness, and user satisfaction. Central to this personalization is preference optimization: the system's ability to detect, interpret, and align with individual user preferences. While prior work in intelligent assistants and optimization algorithms is extensive, their intersection within AI copilots remains underexplored. This survey addresses that gap by examining how user preferences are operationalized in AI copilots. We investigate how preference signals are sourced, modeled across different interaction stages, and refined through feedback loops. Building on a comprehensive literature review, we define the concept of an AI copilot and introduce a taxonomy of preference optimization techniques across pre-, mid-, and post-interaction phases. Each technique is evaluated in terms of advantages, limitations, and design implications. By consolidating fragmented efforts across AI personalization, human-AI interaction, and language model adaptation, this work offers both a unified conceptual foundation and a practical design perspective for building user-aligned, persona-aware AI copilots that support end-to-end adaptability and deployment.