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Creators/Authors contains: "Liu, Zijun"

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  1. ; ; ; ; ; ; ; (, 30th ACM International Conference on Information and Knowledge Management)
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  2. ; ; ; (, RecSys '20: Fourteenth ACM Conference on Recommender Systems)
    null (Ed.)
    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. 
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