An Ethical Multi-Stakeholder Recommender System Based on Evolutionary Multi-Objective Optimization
| Autor: | Jia Wu, Jian Yang, Naime Ranjbar Kermany, Weiliang Zhao, Luiz Augusto Pizzato |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Evolutionary algorithm Novelty 02 engineering and technology Recommender system Machine learning computer.software_genre Multi-objective optimization 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Multi stakeholder Artificial intelligence business computer |
| Zdroj: | SCC |
| DOI: | 10.1109/scc49832.2020.00074 |
| Popis: | In this work, we propose an ethical multi-stakeholder recommender system that uses a multi-objective evolutionary algorithm to make a trade-off between provider coverage, long-tail services inclusion, and recommendation accuracy. Experimental results on real-world datasets show that the proposed method significantly improves the novelty and diversity of recommended services and the coverage of providers with minor loss of accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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