  This paper introduces the Bradley-Terry Regression Trunk model, a novel
probabilistic approach for the analysis of preference data expressed through
paired comparison rankings. In some cases, it may be reasonable to assume that
the preferences expressed by individuals depend on their characteristics.
Within the framework of tree-based partitioning, we specify a tree-based model
estimating the joint effects of subject-specific covariates over and above
their main effects. We combine a tree-based model and the log-linear
Bradley-Terry model using the outcome of the comparisons as response variable.
The proposed model provides a solution to discover interaction effects when no
a-priori hypotheses are available. It produces a small tree, called trunk, that
represents a fair compromise between a simple interpretation of the interaction
effects and an easy to read partition of judges based on their characteristics
and the preferences they have expressed. We present an application on a real
data set following two different approaches, and a simulation study to test the
model's performance. Simulations showed that the quality of the model
performance increases when the number of rankings and objects increases. In
addition, the performance is considerably amplified when the judges'
characteristics have a high impact on their choices.
