  Identifying personalized interventions for an individual is an important
task. Recent work has shown that interventions that do not consider the
demographic background of individual consumers can, in fact, produce the
reverse effect, strengthening opposition to electric vehicles. In this work, we
focus on methods for personalizing interventions based on an individual's
demographics to shift the preferences of consumers to be more positive towards
Battery Electric Vehicles (BEVs). One of the constraints in building models to
suggest interventions for shifting preferences is that each intervention can
influence the effectiveness of later interventions. This, in turn, requires
many subjects to evaluate effectiveness of each possible intervention. To
address this, we propose to identify personalized factors influencing BEV
adoption, such as barriers and motivators. We present a method for predicting
these factors and show that the performance is better than always predicting
the most frequent factors. We then present a Reinforcement Learning (RL) model
that learns the most effective interventions, and compare the number of
subjects required for each approach.

Information Retrieval;Artificial Intelligence;Machine Learning