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Recommender systems fairness evaluation via generalized cross entropy

Author
Deldjoo, Yashar; Anelli, Vito Walter; Zamani, Hamed; Bellogin Kouki, Alejandrountranslated; Di Noia, Tommaso
Entity
UAM. Departamento de Ingeniería Informática
Publisher
CEUR Workshop Proceedings
Date
2019
Citation
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 3
 
 
 
ISSN
1613-0073
Funded by
This work was supported in part by the Center for Intelligent Information Retrieval and in part by project TIN2016-80630-P (MINECO)
Project
Gobierno de España. TIN2016-80630-P
Editor's Version
http://ceur-ws.org/Vol-2440/
Subjects
recommender systems; fairness; metric; Generalized cross entropy; evaluation; Informática
URI
http://hdl.handle.net/10486/691058
Rights
© 2019 The Authors

Licencia Creative Commons
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.

Abstract

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 - Anelli, Vito Walter - Zamani, Hamed - Bellogin Kouki, Alejandro - Di Noia, Tommaso

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