A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

Altulyan, M; Yao, L; Wang, X; Huang, C; Kanhere, SS; Sheng, QZ

A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

Altulyan, M Yao, L Wang, X

Huang, C Kanhere, SS Sheng, QZ

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Publisher:: Oxford University Press (OUP)
Publication Type:: Journal Article
Citation:: The Computer Journal, 2021
Issue Date:: 2021-05-19

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Field	Value	Language
dc.contributor.author	Altulyan, M
dc.contributor.author	Yao, L
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9582-3445
dc.contributor.author	Huang, C
dc.contributor.author	Kanhere, SS
dc.contributor.author	Sheng, QZ
dc.date.accessioned	2021-09-17T09:24:54Z
dc.date.available	2021-09-17T09:24:54Z
dc.date.issued	2021-05-19
dc.identifier.citation	The Computer Journal, 2021
dc.identifier.issn	0010-4620
dc.identifier.issn	1460-2067
dc.identifier.uri	http://hdl.handle.net/10453/150624
dc.description.abstract	<jats:title>Abstract</jats:title> <jats:p>Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, traditional recommender system approaches fail to exploit ever-growing, dynamic and heterogeneous IoT data in building recommender systems for the IoT (RSIoT). This article aims to provide a comprehensive review of state-of-the-art RSIoT, including the related techniques, applications and a discussion on the limitations of applying recommendation systems to IoT. Finally, we propose a reference framework for comparing existing studies to guide future research and practices.</jats:p>
dc.language	en
dc.publisher	Oxford University Press (OUP)
dc.relation.ispartof	The Computer Journal
dc.relation.isbasedon	10.1093/comjnl/bxab049
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Computation Theory & Mathematics
dc.title	A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions
dc.type	Journal Article
utslib.for	08 Information and Computing Sciences
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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-05-31T00:00:00+1000Z
dc.date.updated	2021-09-17T09:24:45Z
pubs.publication-status	Published online

Abstract:

Abstract Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, traditional recommender system approaches fail to exploit ever-growing, dynamic and heterogeneous IoT data in building recommender systems for the IoT (RSIoT). This article aims to provide a comprehensive review of state-of-the-art RSIoT, including the related techniques, applications and a discussion on the limitations of applying recommendation systems to IoT. Finally, we propose a reference framework for comparing existing studies to guide future research and practices.

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http://hdl.handle.net/10453/150624