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Article

Provider Fairness for Diversity and Coverage in Multi-Stakeholder Recommender Systems

School of Electrical & Computer Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Zografou, Greece
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Author to whom correspondence should be addressed.
Academic Editors: Alessandro Micarelli, Giuseppe Sansonetti and Giuseppe D’Aniello
Received: 21 March 2022 / Revised: 6 May 2022 / Accepted: 13 May 2022 / Published: 14 May 2022
(This article belongs to the Special Issue Human and Artificial Intelligence)
Nowadays, recommender systems (RS) are no longer evaluated only for the accuracy of their recommendations. Instead, there is a requirement for other metrics (e.g., coverage, diversity, serendipity) to be taken into account as well. In this context, the multi-stakeholder RS paradigm (MSRS) has gained significant popularity, as it takes into consideration all beneficiaries involved, from item providers to simple users. In this paper, the goal is to provide fair recommendations across item providers in terms of diversity and coverage for users to whom each provider’s items are recommended. This is achieved by following the methodology provided by the literature for solving the recommendation problem as an optimization problem under constraints for coverage and diversity. As the constraints for diversity are quadratic and cannot be solved in sufficient time (NP-Hard problem), we propose a heuristic approach that provides solutions very close to the optimal one, as the proposed approach in the literature for solving diversity constraints was too generic. As a next step, we evaluate the results and identify several weaknesses in the problem formulation as provided in the literature. To this end, we introduce new formulations for diversity and provide a new heuristic approach for the solution of the new optimization problem. View Full-Text
Keywords: multi-stakeholder recommender systems; diversity; fairness; coverage; optimization multi-stakeholder recommender systems; diversity; fairness; coverage; optimization
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MDPI and ACS Style

Karakolis, E.; Kokkinakos, P.; Askounis, D. Provider Fairness for Diversity and Coverage in Multi-Stakeholder Recommender Systems. Appl. Sci. 2022, 12, 4984. https://0-doi-org.brum.beds.ac.uk/10.3390/app12104984

AMA Style

Karakolis E, Kokkinakos P, Askounis D. Provider Fairness for Diversity and Coverage in Multi-Stakeholder Recommender Systems. Applied Sciences. 2022; 12(10):4984. https://0-doi-org.brum.beds.ac.uk/10.3390/app12104984

Chicago/Turabian Style

Karakolis, Evangelos, Panagiotis Kokkinakos, and Dimitrios Askounis. 2022. "Provider Fairness for Diversity and Coverage in Multi-Stakeholder Recommender Systems" Applied Sciences 12, no. 10: 4984. https://0-doi-org.brum.beds.ac.uk/10.3390/app12104984

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