Towards Fair Conversational Recommender Systems Institutional Repository Document uri icon

abstract

  • Conversational recommender systems have demonstrated great success. They can accurately capture a user's current detailed preference -- through a multi-round interaction cycle -- to effectively guide users to a more personalized recommendation. Alas, conversational recommender systems can be plagued by the adverse effects of bias, much like traditional recommenders. In this work, we argue for increased attention on the presence of and methods for counteracting bias in these emerging systems. As a starting point, we propose three fundamental questions that should be deeply examined to enable fairness in conversational recommender systems.

altmetric score

  • 2.35

author list (cited authors)

  • Lin, A., Zhu, Z., Wang, J., & Caverlee, J.

citation count

  • 0

complete list of authors

  • Lin, Allen||Zhu, Ziwei||Wang, Jianling||Caverlee, James

Book Title

  • arXiv

publication date

  • August 2022