  We propose a cost-effective framework for preference elicitation and
aggregation under the Plackett-Luce model with features. Given a budget, our
framework iteratively computes the most cost-effective elicitation questions in
order to help the agents make a better group decision.
  We illustrate the viability of the framework with experiments on Amazon
Mechanical Turk, which we use to estimate the cost of answering different types
of elicitation questions. We compare the prediction accuracy of our framework
when adopting various information criteria that evaluate the expected
information gain from a question. Our experiments show carefully designed
information criteria are much more efficient, i.e., they arrive at the correct
answer using fewer queries, than randomly asking questions given the budget
constraint.
