A Cost-Effective Framework for Preference Elicitation and Aggregation

With the aid of an intelligent system, a group of people (the key group) faces a hiring decision about many candidates who are characterized by attributes, such as experiences, technical skills, communication skills, etc. The goal is to help the key group make a group decision without directly eliciting their full preferences over all candidates, which is often infeasible given the vast number of candidates. Instead, the intelligent system may ask fellow employees (the regular group) about their preferences in order to learn about the key group's preferences. How can the intelligent system decide which member in the regular group to ask and which question should be asked? This example illustrates the preference elicitation problem, which has been widely studied in the field of recommender systems [Loepp et al., 2014], healthcare [Erdem and Campbell, 2017, Weernink et al., 2014], marketing [Huang and Luo, 2016], stable matching [Drummond and Boutilier, 2014, Rastegari et al., 2016], etc. Most previous works studied a special case of the aforementioned scenario, in which the regular group is the key group. The objective of preference elicitation is to achieve some goal using as few samples (data) as possible. A common approach is to adaptively ask questions that maximize expected information gain, measured by some information criteria. Moreover, most previous work focused on a specific type of elicitation questions, e.g.