A fairness-aware multi-stakeholder recommender system

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1995-2018
Number of pages24
JournalWorld Wide Web
Volume24
Issue number6
Early online date22 Sep 2021
DOIs
Publication statusPublished - Nov 2021

Keywords

  • Long-tail recommendation
  • Multi-objective evolutionary optimization
  • Multi-stakeholder recommender systems
  • P-fairness
  • Personalized diversity

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