Fairness is a critical problem not only in scientific research but also in many real-life applications. Recent work in recommender systems mainly focuses on fairness in recommendations as an important aspect of measuring recommendations quality. This paper presents PyCPFair, a Python-based framework for consumer and producer fairness in recommender systems. The ease-of-use and flexibility of the presented framework have allowed reducing the development time and increased evaluation strategies of fairness models for recommender systems. The PyCPFair is written mainly in Python and the optimization solution is provided using MIP interface and Gurobi solver.

PyCPFair: A framework for consumer and producer fairness in recommender systems

Deldjoo, Yashar
2022

Abstract

Fairness is a critical problem not only in scientific research but also in many real-life applications. Recent work in recommender systems mainly focuses on fairness in recommendations as an important aspect of measuring recommendations quality. This paper presents PyCPFair, a Python-based framework for consumer and producer fairness in recommender systems. The ease-of-use and flexibility of the presented framework have allowed reducing the development time and increased evaluation strategies of fairness models for recommender systems. The PyCPFair is written mainly in Python and the optimization solution is provided using MIP interface and Gurobi solver.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/243860
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