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.
CITATION STYLE
Göpfert, C., Chow, Y., Hsu, C. W., Vendrov, I., Lu, T., Ramachandran, D., & Boutilier, C. (2022). Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors. In WWW 2022 - Proceedings of the ACM Web Conference 2022 (pp. 2411–2421). Association for Computing Machinery, Inc. https://doi.org/10.1145/3485447.3512113
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