Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based Recommendation

Wan, Z; Liu, X; Wang, B; Qiu, J; Li, B; Guo, T; Chen, G; Wang, Y

Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based Recommendation

Wan, Z Liu, X Wang, B Qiu, J Li, B Guo, T

Chen, G Wang, Y

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Journal Article
Citation:: ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2024, 42, (2)
Issue Date:: 2024-03

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Field	Value	Language
dc.contributor.author	Wan, Z
dc.contributor.author	Liu, X
dc.contributor.author	Wang, B
dc.contributor.author	Qiu, J
dc.contributor.author	Li, B
dc.contributor.author	Guo, T https://orcid.org/0000-0001-5130-3237
dc.contributor.author	Chen, G
dc.contributor.author	Wang, Y https://orcid.org/0000-0002-6815-0879
dc.date.accessioned	2024-02-28T05:15:08Z
dc.date.available	2024-02-28T05:15:08Z
dc.date.issued	2024-03
dc.identifier.citation	ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2024, 42, (2)
dc.identifier.issn	1046-8188
dc.identifier.issn	1558-2868
dc.identifier.uri	http://hdl.handle.net/10453/175925
dc.description.abstract	<jats:p> Session-based recommendation (SBR) systems aim to utilize the user’s short-term behavior sequence to predict the next item without the detailed user profile. <jats:styled-content style="color:#000000">Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to encode the representations of pair-wise relations among items and their neighbors. Some of the existing GNN-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. Other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. To address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called Session-based Recommendation with Spatio-temporal Contrastive Learning-enhanced GNNs (RESTC). The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. Furthermore, a novel global collaborative filtering graph embedding is leveraged to enhance the spatial view in the main task.</jats:styled-content> Extensive experiments demonstrate the significant performance of RESTC compared with the state-of-the-art baselines. We release our source code at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/SUSTechBruce/RESTC-Source-code">https://github.com/SUSTechBruce/RESTC-Source-code</jats:ext-link> . </jats:p>
dc.language	English
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation	Smartcrete CRC
dc.relation	iMOVE Australia Limited1-019
dc.relation.ispartof	ACM TRANSACTIONS ON INFORMATION SYSTEMS
dc.relation.isbasedon	10.1145/3626091
dc.rights	info:eu-repo/semantics/restrictedAccess
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	Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based Recommendation
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	in_progress	*
dc.date.updated	2024-02-28T05:15:06Z
pubs.issue	2
pubs.publication-status	Accepted
pubs.volume	42
utslib.citation.issue	2

Abstract:

Session-based recommendation (SBR) systems aim to utilize the user’s short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to encode the representations of pair-wise relations among items and their neighbors. Some of the existing GNN-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. Other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. To address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called Session-based Recommendation with Spatio-temporal Contrastive Learning-enhanced GNNs (RESTC). The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. Furthermore, a novel global collaborative filtering graph embedding is leveraged to enhance the spatial view in the main task. Extensive experiments demonstrate the significant performance of RESTC compared with the state-of-the-art baselines. We release our source code at https://github.com/SUSTechBruce/RESTC-Source-code .

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

http://hdl.handle.net/10453/175925