A hybrid recommendation system with many-objective evolutionary algorithm

Cai, X; Hu, Z; Zhao, P; Zhang, WS; Chen, J

A hybrid recommendation system with many-objective evolutionary algorithm

Cai, X Hu, Z Zhao, P Zhang, WS Chen, J

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2020, 159, pp. 113648-113648
Issue Date:: 2020-11-30

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Field	Value	Language
dc.contributor.author	Cai, X
dc.contributor.author	Hu, Z
dc.contributor.author	Zhao, P
dc.contributor.author	Zhang, WS
dc.contributor.author	Chen, J
dc.date.accessioned	2021-05-24T06:51:37Z
dc.date.available	2021-05-24T06:51:37Z
dc.date.issued	2020-11-30
dc.identifier.citation	Expert Systems with Applications, 2020, 159, pp. 113648-113648
dc.identifier.issn	0957-4174
dc.identifier.uri	http://hdl.handle.net/10453/149154
dc.description.abstract	Recommendation system (RS) is a technology that provides accurate recommendations to users. However, it is not comprehensive to only consider the accuracy of the recommendation because users have different requirements. To improve the comprehensive performance, this paper presents a hybrid recommendation model based on many-objective optimization, which can simultaneously optimize the accuracy, diversity, novelty and coverage of recommendation. This model enhances the robustness of recommendations by mixing three different basic recommendation technologies. Additionally, we solve it with many-objective evolutionary algorithm (MaOEA) and test it extensively. Experimental results demonstrate the effectiveness of the presented model, which can provide the recommendations with more and novel items on the basis of accurate and diverse.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Expert Systems with Applications
dc.relation.isbasedon	10.1016/j.eswa.2020.113648
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A hybrid recommendation system with many-objective evolutionary algorithm
dc.type	Journal Article
utslib.citation.volume	159
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-24T06:51:35Z
pubs.publication-status	Published
pubs.volume	159

Abstract:

Recommendation system (RS) is a technology that provides accurate recommendations to users. However, it is not comprehensive to only consider the accuracy of the recommendation because users have different requirements. To improve the comprehensive performance, this paper presents a hybrid recommendation model based on many-objective optimization, which can simultaneously optimize the accuracy, diversity, novelty and coverage of recommendation. This model enhances the robustness of recommendations by mixing three different basic recommendation technologies. Additionally, we solve it with many-objective evolutionary algorithm (MaOEA) and test it extensively. Experimental results demonstrate the effectiveness of the presented model, which can provide the recommendations with more and novel items on the basis of accurate and diverse.

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http://hdl.handle.net/10453/149154