Preference Elicitation For General Random Utility Models
  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.
