FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems

Wu, Y; Cao, J; Xu, G

FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems

Wu, Y Cao, J Xu, G

Permalink

Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Journal Article
Citation:: ACM Transactions on Information Systems, 2023, 42, (1)
Issue Date:: 2023-08-18

Closed Access

	Filename	Description	Size
	Faster A Dynamic Fairness.pdf	Published version	2.4 MB	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	Wu, Y
dc.contributor.author	Cao, J
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.date.accessioned	2024-03-28T03:32:30Z
dc.date.available	2024-03-28T03:32:30Z
dc.date.issued	2023-08-18
dc.identifier.citation	ACM Transactions on Information Systems, 2023, 42, (1)
dc.identifier.issn	1046-8188
dc.identifier.issn	1558-2868
dc.identifier.uri	http://hdl.handle.net/10453/177334
dc.description.abstract	When only users' preferences and interests are considered by a recommendation algorithm, it will lead to the severe long-tail problem over items. Therefore, the unfair exposure phenomenon of recommended items caused by this problem has attracted widespread attention in recent years. For the first time, we reveal the fact that there is a more serious unfair exposure problem in session-based recommender systems (SRSs), which learn the short-term and dynamic preferences of users from anonymous sessions. Considering the fact that in SRSs, recommendations are provided multiple times and item exposures are accumulated over interactions in a session, we define new metrics both for the fairness of item exposure and recommendation quality among sessions. Moreover, we design a dynamic Fairness-Assurance STrategy for sEssion-based Recommender systems (FASTER). FASTER is a post-processing strategy that tries to keep a balance between item exposure fairness and recommendation quality. It can also maintain the fairness of recommendation quality among sessions. The effectiveness of FASTER is verified on three real-world datasets and five original algorithms. The experiment results show that FASTER can generally reduce the unfair exposure of different session-based recommendation algorithms while still ensuring a high level of recommendation quality.
dc.language	English
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	ACM Transactions on Information Systems
dc.relation.isbasedon	10.1145/3586993
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0806 Information Systems, 0807 Library and Information Studies
dc.subject.classification	Information Systems
dc.subject.classification	4605 Data management and data science
dc.title	FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems
dc.type	Journal Article
utslib.citation.volume	42
utslib.for	0806 Information Systems
utslib.for	0807 Library and Information Studies
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 - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-28T03:32:28Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	42
utslib.citation.issue	1

Abstract:

When only users' preferences and interests are considered by a recommendation algorithm, it will lead to the severe long-tail problem over items. Therefore, the unfair exposure phenomenon of recommended items caused by this problem has attracted widespread attention in recent years. For the first time, we reveal the fact that there is a more serious unfair exposure problem in session-based recommender systems (SRSs), which learn the short-term and dynamic preferences of users from anonymous sessions. Considering the fact that in SRSs, recommendations are provided multiple times and item exposures are accumulated over interactions in a session, we define new metrics both for the fairness of item exposure and recommendation quality among sessions. Moreover, we design a dynamic Fairness-Assurance STrategy for sEssion-based Recommender systems (FASTER). FASTER is a post-processing strategy that tries to keep a balance between item exposure fairness and recommendation quality. It can also maintain the fairness of recommendation quality among sessions. The effectiveness of FASTER is verified on three real-world datasets and five original algorithms. The experiment results show that FASTER can generally reduce the unfair exposure of different session-based recommendation algorithms while still ensuring a high level of recommendation quality.

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

http://hdl.handle.net/10453/177334