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Published in: World Wide Web 6/2021

22-09-2021

A fairness-aware multi-stakeholder recommender system

Authors: Naime Ranjbar Kermany, Weiliang Zhao, Jian Yang, Jia Wu, Luiz Pizzato

Published in: World Wide Web | Issue 6/2021

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Abstract

Traditional recommender systems mainly focus on the accuracy of recommendation, which lead to recommender systems reinforcing popular items and ignoring lesser-known items. There is increasing evidence that providing good recommendations of surprising items can lead to better user satisfaction. Users may be delightfully surprised if long-tail items are brought to them. Marketplaces need to keep providers satisfied by making sure that their items get enough exposure. In this work, we propose a fairness-aware multi-stakeholder recommender system that uses a multi-objective evolutionary algorithm to make a trade-off between provider coverage, long-tail inclusion, personalized diversity, and recommendation accuracy. Experimental results against real-world datasets show that the proposed method significantly improves the diversity of recommended items in a personalized matter and the coverage of providers with no or minor loss of accuracy.
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Footnotes
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Metadata
Title
A fairness-aware multi-stakeholder recommender system
Authors
Naime Ranjbar Kermany
Weiliang Zhao
Jian Yang
Jia Wu
Luiz Pizzato
Publication date
22-09-2021
Publisher
Springer US
Published in
World Wide Web / Issue 6/2021
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-021-00946-8

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