# Preference Elicitation For General Random Utility Models

arXiv: https://arxiv.org/abs/1309.6864 · HTML: https://mlsanity.com/paper/1309.6864/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2013.

## Built on by (most cited first, 3 of 3)

- [ANACONDA: An Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits](https://mlsanity.com/paper/2210.14322/) (2022)
- [Dueling Bandits with Adversarial Sleeping](https://mlsanity.com/paper/2107.02274/) (2021)
- [Robust Voting Rules from Algorithmic Robust Statistics](https://mlsanity.com/paper/2112.06380/) (2021)

## Abstract (arXiv)

This paper discusses {General Random Utility Models (GRUMs)}. These are a class of parametric models that generate partial ranks over alternatives given attributes of agents and alternatives. We propose two preference elicitation scheme for GRUMs developed from principles in Bayesian experimental design, one for social choice and the other for personalized choice. We couple this with a general Monte-Carlo-Expectation-Maximization (MC-EM) based algorithm for MAP inference under GRUMs. We also prove uni-modality of the likelihood functions for a class of GRUMs. We examine the performance of various criteria by experimental studies, which show that the proposed elicitation scheme increases the precision of estimation.