Recommender systems fairness evaluation via generalized cross entropy
Entidad
UAM. Departamento de Ingeniería InformáticaEditor
CEUR Workshop ProceedingsFecha de edición
2019Cita
Proceedings of the Workshop on Recommendation in Multi-stakeholder Environments co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019). Ed. Robert Burke, Himan Abdollahpouri, Edward Malthouse, K.P. Thai & Yongfeng Zhang. CEUR Workshop Proceedings, Volumen 2440, 2019. Short 3ISSN
1613-0073Financiado por
This work was supported in part by the Center for Intelligent Information Retrieval and in part by project TIN2016-80630-P (MINECO)Proyecto
Gobierno de España. TIN2016-80630-PVersión del editor
http://ceur-ws.org/Vol-2440/Materias
recommender systems; fairness; metric; Generalized cross entropy; evaluation; InformáticaDerechos
© 2019 The AuthorsResumen
Fairness in recommender systems has been considered with respect
to sensitive attributes of users (e.g., gender, race) or items (e.g., revenue
in a multistakeholder setting). Regardless, the concept has been
commonly interpreted as some form of equality – i.e., the degree to
which the system is meeting the information needs of all its users in
an equal sense. In this paper, we argue that fairness in recommender
systems does not necessarily imply equality, but instead it should
consider a distribution of resources based on merits and needs.We
present a probabilistic framework based ongeneralized cross entropy
to evaluate fairness of recommender systems under this perspective,
wherewe showthat the proposed framework is flexible and explanatory
by allowing to incorporate domain knowledge (through an ideal
fair distribution) that can help to understand which item or user aspects
a recommendation algorithm is over- or under-representing.
Results on two real-world datasets show the merits of the proposed
evaluation framework both in terms of user and item fairness
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Google Scholar:Deldjoo, Yashar
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Anelli, Vito Walter
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Zamani, Hamed
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Bellogin Kouki, Alejandro
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Di Noia, Tommaso
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