  In this work, we present a novel human-in-the-loop framework to help the
human user understand the decision making process that involves choosing
preferred options. We focus on qualitative preference models over alternatives
from combinatorial domains. This framework is interactive: the user provides
her behavioral data to the framework, and the framework explains the learned
model to the user. It is iterative: the framework collects feedback on the
learned model from the user and tries to improve it accordingly till the user
terminates the iteration. In order to communicate the learned preference model
to the user, we develop visualization of intuitive and explainable graphic
models, such as lexicographic preference trees and forests, and conditional
preference networks. To this end, we discuss key aspects of our framework for
lexicographic preference models.
