Interactive recommender systems (RSs) allow users to express intent,
preferences and contexts in a rich fashion, often using natural language. One
challenge in using such feedback is inferring a user's semantic intent from the
open-ended terms used to describe an item, and using it to refine
recommendation results. Leveraging concept activation vectors (CAVs) [21], we
develop a framework to learn a representation that captures the semantics of
such attributes and connects them to user preferences and behaviors in RSs. A
novel feature of our approach is its ability to distinguish objective and
subjective attributes and associate different senses with different users.
Using synthetic and real-world datasets, we show that our CAV representation
accurately interprets users' subjective semantics, and can improve
recommendations via interactive critiquing

利用概念激活向量（CAVs）来学习用户语义意图，将其与用户喜好和行为联系起来，并能够通过互动评论来提高推荐准确性的交互式推荐系统。