Trustworthy Recommender Systems

Wang, S; Zhang, X; Wang, Y; Ricci, F

Trustworthy Recommender Systems

Wang, S

Zhang, X Wang, Y Ricci, F

Permalink

Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Transactions on Intelligent Systems and Technology, 2023
Issue Date:: 2023-10-01

Closed Access

	Filename	Description	Size
	41. Trustworthy Recommender Systems. TIST 2023. Shoujin.pdf		372.53 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.contributor.author	Zhang, X
dc.contributor.author	Wang, Y
dc.contributor.author	Ricci, F
dc.date.accessioned	2023-10-31T23:35:25Z
dc.date.available	2023-10-31T23:35:25Z
dc.date.issued	2023-10-01
dc.identifier.citation	ACM Transactions on Intelligent Systems and Technology, 2023
dc.identifier.issn	2157-6904
dc.identifier.issn	2157-6912
dc.identifier.uri	http://hdl.handle.net/10453/172913
dc.description.abstract	<jats:p>Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, and various types of biases. As a result, it has become clear that the focus on RS accuracy is too narrow and the research must consider other important factors, and in particular, trustworthiness. A trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased, fair, as well as robust to noise and attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, there is a lack of a systematic overview and discussion of the literature in this novel and fast developing field of TRSs. To this end, in this paper, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs.</jats:p>
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM Transactions on Intelligent Systems and Technology
dc.relation.isbasedon	10.1145/3627826
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4611 Machine learning
dc.title	Trustworthy Recommender Systems
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
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	*
dc.date.updated	2023-10-31T23:35:24Z
pubs.publication-status	Published online

Abstract:

Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, and various types of biases. As a result, it has become clear that the focus on RS accuracy is too narrow and the research must consider other important factors, and in particular, trustworthiness. A trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased, fair, as well as robust to noise and attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, there is a lack of a systematic overview and discussion of the literature in this novel and fast developing field of TRSs. To this end, in this paper, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/172913