  Preference elicitation plays a central role in interactive recommender
systems. Most preference elicitation approaches use either item queries that
ask users to select preferred items from a slate, or attribute queries that ask
them to express their preferences for item characteristics. Unfortunately,
users often wish to describe their preferences using soft attributes for which
no ground-truth semantics is given. Leveraging concept activation vectors for
soft attribute semantics, we develop novel preference elicitation methods that
can accommodate soft attributes and bring together both item and
attribute-based preference elicitation. Our techniques query users using both
items and soft attributes to update the recommender system's belief about their
preferences to improve recommendation quality. We demonstrate the effectiveness
of our methods vis-a-vis competing approaches on both synthetic and real-world
datasets.
