Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities

Authors

  • Filip Radlinski Google Inc.
  • Craig Boutilier Google Inc.
  • Deepak Ramachandran Google Inc.
  • Ivan Vendrov Omni Labs

DOI:

https://doi.org/10.1609/aaai.v36i11.21492

Keywords:

Subjectivity, Conversational Recommenders, Natural Language Understanding, Attribute Interpretation, Preference Elicitation

Abstract

The ubiquity of recommender systems has increased the need for higher-bandwidth, natural and efficient communication with users. This need is increasingly filled by recommenders that support natural language interaction, often conversationally. Given the inherent semantic subjectivity present in natural language, we argue that modeling subjective attributes in recommenders is a critical, yet understudied, avenue of AI research. We propose a novel framework for understanding different forms of subjectivity, examine various recommender tasks that will benefit from a systematic treatment of subjective attributes, and outline a number of research challenges.

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Published

2022-06-28

How to Cite

Radlinski, F., Boutilier, C., Ramachandran, D., & Vendrov, I. (2022). Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12287-12293. https://doi.org/10.1609/aaai.v36i11.21492