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A fairness-aware multi-stakeholder recommender system


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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Correspondence to Naime Ranjbar Kermany.

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Ranjbar Kermany, N., Zhao, W., Yang, J. et al. A fairness-aware multi-stakeholder recommender system. World Wide Web (2021).

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