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Preference elicitation plays a central role in interactive recommendersystems. Most preference elicitation approaches use either item queries thatask users to select preferred items from a slate, or attribute queries that askthem to express their preferences for item characteristics. Unfortunately,users often wish to describe their preferences using soft attributes for whichno ground-truth semantics is given. Leveraging concept activation vectors forsoft attribute semantics, we develop novel preference elicitation methods thatcan accommodate soft attributes and bring together both item andattribute-based preference elicitation. Our techniques query users using bothitems and soft attributes to update the recommender system's belief about theirpreferences to improve recommendation quality. We demonstrate the effectivenessof our methods vis-a-vis competing approaches on both synthetic and real-worlddatasets.

Preference Elicitation with Soft Attributes in Interactive Recommendation