A survey for trust-aware recommender systems: A deep learning perspective

Dong, M; Yuan, F; Yao, L; Wang, X; Xu, X; Zhu, L

A survey for trust-aware recommender systems: A deep learning perspective

Dong, M Yuan, F Yao, L Wang, X

Xu, X Zhu, L

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2022, 249
Issue Date:: 2022-08-05

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Field	Value	Language
dc.contributor.author	Dong, M
dc.contributor.author	Yuan, F
dc.contributor.author	Yao, L
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9582-3445
dc.contributor.author	Xu, X
dc.contributor.author	Zhu, L
dc.date.accessioned	2022-10-07T00:58:41Z
dc.date.available	2022-10-07T00:58:41Z
dc.date.issued	2022-08-05
dc.identifier.citation	Knowledge-Based Systems, 2022, 249
dc.identifier.issn	0950-7051
dc.identifier.issn	1872-7409
dc.identifier.uri	http://hdl.handle.net/10453/162377
dc.description.abstract	A significant remaining challenge for existing recommender systems is that users may not trust recommender systems for either inaccurate recommendation or lack of explanation. Thus, it becomes critical to embrace a trustworthy recommender system. This survey provides a systematic summary of three categories of trust issues in recommender systems: social-aware recommender systems, which leverage users’ social trust relationships; robust recommender systems, which filter untruthful information, noises and enhance attack resistance; and explainable recommender systems, which provide explanations of the recommended items. We focus on the work based on deep learning techniques, which is an emerging area in the recommendation research.
dc.language	English
dc.publisher	ELSEVIER
dc.relation	http://purl.org/au-research/grants/arc/DE180100251
dc.relation.ispartof	Knowledge-Based Systems
dc.relation.isbasedon	10.1016/j.knosys.2022.108954
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences, 15 Commerce, Management, Tourism and Services, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A survey for trust-aware recommender systems: A deep learning perspective
dc.type	Journal Article
utslib.citation.volume	249
utslib.for	08 Information and Computing Sciences
utslib.for	15 Commerce, Management, Tourism and Services
utslib.for	17 Psychology and Cognitive 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	2024-08-01T00:00:00+1000Z
dc.date.updated	2022-10-07T00:58:39Z
pubs.publication-status	Published
pubs.volume	249

Abstract:

A significant remaining challenge for existing recommender systems is that users may not trust recommender systems for either inaccurate recommendation or lack of explanation. Thus, it becomes critical to embrace a trustworthy recommender system. This survey provides a systematic summary of three categories of trust issues in recommender systems: social-aware recommender systems, which leverage users’ social trust relationships; robust recommender systems, which filter untruthful information, noises and enhance attack resistance; and explainable recommender systems, which provide explanations of the recommended items. We focus on the work based on deep learning techniques, which is an emerging area in the recommendation research.

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http://hdl.handle.net/10453/162377