Preference Elicitation

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Preference elicitation asks how a system can infer what an individual or group values from limited answers, then use that information to recommend items, optimize decisions, or guide AI behavior. Common approaches include Bayesian active learning that chooses informative follow-up questions, pairwise-comparison and discrete-choice models that estimate utilities, and conversational systems that extract preferences from dialogue; these methods are tested in settings such as product search, portfolio choice, robotics, and human-feedback alignment. A central challenge is balancing accurate, personalized models against the time and cognitive burden of asking questions, while ensuring that minority preferences are not lost in aggregation. Elicited answers can also be inconsistent, instrument-dependent, or disconnected from later behavior, so a key open issue is when a measured preference reliably represents what someone wants in the decision at hand.

Latest papers

69 papers13 papers in the last four weeks, up 117% on the four weeks before. 0.1% of all new papers.

Linear Social Choice with Few Queries: A Moment-Based Approach
Mar 19, 2026Luise Ge, Daniel Halpern, Gregory Kehne +1Social Choice Theory Preference Elicitation
Washington University in St. Louis. · Google Research.
Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems
Mar 12, 2026Dat Tran, Yongce Li, Hannah Clay +3Recommender Systems Preference Elicitation
Stanford University · Massachusetts Institute of Technology · Amazon
Improving through Interaction: Searching Behavioral Representation Spaces with CMA-ES-IG
Mar 9, 2026Nathaniel Dennler, Zhonghao Shi, Yiran Tao +3Active Preference Learning Preference Learning
CSAIL/AeroAstro, Massachusetts Institute of Technology. · Department of Computer Science, University of Southern California.
Your Reviews Replicate You: LLM-Based Agents as Customer Digital Twins for Conjoint Analysis
Mar 6, 2026Bin Xuan, Jungmin Hwang, Hakyeon LeePreference Elicitation Digital Twins
Department of Data Science Seoul National University of Science and Technology Seoul, Republic of Korea · Department of Industrial Engineering Seoul National University of Science and Technology Seoul, Republic of Korea
Bayesian Preference Elicitation: Human-In-The-Loop Optimization of An Active Prosthesis
Feb 26, 2026Sophia Taddei, Wouter Koppen, Eligia Alfio +8Preference Elicitation Human-in-the-Loop AI
Federated Labs Robotics & AI, Vrije Universiteit Brussel. · Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel. · IDLab, Ghent University – imec. +1
Identifying two piecewise linear additive value functions from anonymous preference information
Feb 24, 2026Vincent Auriau, Khaled Belahcene, Emmanuel Malherbe +2Preference Elicitation Preference Modeling
Heudiasyc · MICS, CentraleSupélec, Université Paris Saclay, France · Artefact Research Center, France +2
OPRIDE: Offline Preference-based Reinforcement Learning via In-Dataset Exploration
Feb 19, 2026Yiqin Yang, Hao Hu, Yihuan Mao +10Preference-Based RL Offline RL
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences · Moonshot AI · Tsinghua University +3
Cold-Start Personalization via Training-Free Priors from Structured World Models
Feb 16, 2026Avinandan Bose, Shuyue Stella Li, Faeze Brahman +6Preference Elicitation User Preference Modeling
Meta · University of Washington · Allen Institute for AI
It's My Agent, Not Me: Personal Privacy Boundaries in AI-Delegated Information Sharing
Sep 26, 2025Bingcan Guo, Eryue Xu, Zhiping Zhang +1Preference Elicitation Human-AI Decision Making
Department of Human Centered Design & Engineering, University of Washington, United States · School of Information Sciences, University of Illinois Urbana-Champaign, United States · Khoury College of Computer Sciences, Northeastern University, United States

Mar 19, 2026J/K move · Enter open · S save

Linear Social Choice with Few Queries: A Moment-Based Approach

Luise Ge, Daniel Halpern, Gregory Kehne, Yevgeniy Vorobeychik

Washington University in St. Louis. · Google Research.

Social Choice Theory Preference Elicitation Social Welfare Maximization Pairwise Comparison

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Most social choice rules assume access to full rankings, while current alignment practice -- despite aiming for diversity -- typically treats voters as anonymous and comparisons as independent, effectively extracting only about one bit per voter. Motivated by this gap, we study social choice under an extreme communication budget in the linear social choice model, where each voter's utility is the inner product between a latent voter type and the embedding of the context and candidate. The candidate and voter spaces may be very large or even infinite. Our core idea is to model the electorate as an unknown distribution over voter types and to recover its moments as informative summary statistics for candidate selection. We show that one pairwise comparison per voter already suffices to select a candidate that maximizes social welfare, but this elicitation cannot identify the second moment and therefore cannot support objectives that account for inequality. We prove that two pairwise comparisons per voter, or alternatively a single graded comparison, identify the second moment; moreover, these richer queries suffice to identify all moments, and hence the entire voter-type distribution. These results enable principled solutions to a range of social choice objectives including inequality-aware welfare criteria such as taking into account the spread of voter utilities and choosing a representative subset.