Fairness-aware Recommendation with librec-auto

Nasim Sonboli, Robin Burke, Zijun Liu, Masoud Mansoury

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Comparative experimentation is important for studying reproducibility in recommender systems. This is particularly true in areas without well-established methodologies, such as fairness-aware recommendation. In this paper, we describe fairness-aware enhancements to our recommender systems experimentation tool librec-auto. These enhancements include metrics for various classes of fairness definitions, extension of the experimental model to support result re-ranking and a library of associated re-ranking algorithms, and additional support for experiment automation and reporting. The associated demo will help attendees move quickly to configuring and running their own experiments with librec-auto.

Original languageEnglish
Title of host publicationRecSys 2020 - 14th ACM Conference on Recommender Systems
Pages594-596
Number of pages3
ISBN (Electronic)9781450375832
DOIs
Publication statusPublished - 2020

Keywords

  • Experimentation
  • Fairness
  • Librec
  • Recommender Systems Frameworks
  • Reranking

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