  Interactive recommender systems have emerged as a promising paradigm to
overcome the limitations of the primitive user feedback used by traditional
recommender systems (e.g., clicks, item consumption, ratings). They allow users
to express intent, preferences, constraints, and contexts in a richer fashion,
often using natural language (including faceted search and dialogue). Yet more
research is needed to find the most effective ways to use this feedback. One
challenge is inferring a user's semantic intent from the open-ended terms or
attributes often used to describe a desired item, and using it to refine
recommendation results. Leveraging concept activation vectors (CAVs) [26], a
recently developed approach for model interpretability in machine learning, we
develop a framework to learn a representation that captures the semantics of
such attributes and connects them to user preferences and behaviors in
recommender systems. One novel feature of our approach is its ability to
distinguish objective and subjective attributes (both subjectivity of degree
and of sense), and associate different senses of subjective attributes with
different users. We demonstrate on both synthetic and real-world data sets that
our CAV representation not only accurately interprets users' subjective
semantics, but can also be used to improve recommendations through interactive
item critiquing.
