An ethical multi-stakeholder recommender system based on evolutionary multi-objective optimization

Kermany, NR; Zhao, W; Yang, J; Wu, J; Pizzato, L

An ethical multi-stakeholder recommender system based on evolutionary multi-objective optimization

Kermany, NR Zhao, W Yang, J Wu, J Pizzato, L

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - 2020 IEEE 13th International Conference on Services Computing, SCC 2020, 2020, 00, pp. 478-480
Issue Date:: 2020-11-01

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Field	Value	Language
dc.contributor.author	Kermany, NR
dc.contributor.author	Zhao, W
dc.contributor.author	Yang, J
dc.contributor.author	Wu, J
dc.contributor.author	Pizzato, L
dc.date	2020-11-07
dc.date.accessioned	2021-05-07T05:43:58Z
dc.date.available	2021-05-07T05:43:58Z
dc.date.issued	2020-11-01
dc.identifier.citation	Proceedings - 2020 IEEE 13th International Conference on Services Computing, SCC 2020, 2020, 00, pp. 478-480
dc.identifier.isbn	9781728187891
dc.identifier.uri	http://hdl.handle.net/10453/148788
dc.description.abstract	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.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - 2020 IEEE 13th International Conference on Services Computing, SCC 2020
dc.relation.ispartof	2020 IEEE International Conference on Services Computing (SCC)
dc.relation.isbasedon	10.1109/SCC49832.2020.00074
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	An ethical multi-stakeholder recommender system based on evolutionary multi-objective optimization
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Beijing, China
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2021-05-07T05:43:57Z
pubs.finish-date	2020-11-11
pubs.publication-status	Published
pubs.start-date	2020-11-07
pubs.volume	00

Abstract:

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.

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http://hdl.handle.net/10453/148788