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.

本文探讨了基于参数化建模的 {General Random Utility Models (GRUMs)}，并基于贝叶斯实验设计的原则提出了两种 GRUMs 的偏好引导方案，其中之一适用于社会选择，另一种适用于个性化选择。我们还结合 Monte-Carlo-Expectation-Maximization (MC-EM) 方法提出了一种进行 GRUMs 下的 MAP 推断的算法。同时，我们证明了一类 GRUMs 的似然函数单峰性，并通过实验研究的性能表现证明了所提出引导方案提高了估计的精度。