Cascading Hypergraph Convolution Networks for Multi-Behavior Sequential Recommendation*

Lu, D; Zhang, H; Li, L; Wu, S; Xu, G

Cascading Hypergraph Convolution Networks for Multi-Behavior Sequential Recommendation*

Lu, D Zhang, H Li, L Wu, S Xu, G

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2024 11th International Conference on Behavioural and Social Computing (BESC), 2024, 00, pp. 1-7
Issue Date:: 2024-12-12

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Field	Value	Language
dc.contributor.author	Lu, D
dc.contributor.author	Zhang, H
dc.contributor.author	Li, L
dc.contributor.author	Wu, S
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.date	2024-08-16
dc.date.accessioned	2025-02-27T22:05:30Z
dc.date.available	2025-02-27T22:05:30Z
dc.date.issued	2024-12-12
dc.identifier.citation	2024 11th International Conference on Behavioural and Social Computing (BESC), 2024, 00, pp. 1-7
dc.identifier.isbn	979-8-3315-3191-1
dc.identifier.issn	2689-8306
dc.identifier.uri	http://hdl.handle.net/10453/185408
dc.description.abstract	In current research on recommendation systems exploring multi behavior sequence recommendation has become a crucial topic It is well known that user interactions on online platforms such as social media websites news aggregation applications involve not only singular actions like reading or clicking but also various other behaviors such as commenting sharing and bookmarking These diverse interactions reflect different facets of user preferences in item interaction sequences Therefore understanding and integrating these diverse behaviors to effectively represent user preferences personalized is essential To better utilize temporal information we propose a Cascading Hypergraph Convolution Networks For Multi Behavior Sequential Recommendation CHMSR framework CHMSR aims to capture the underlying behavioral preferences in user sequence interactions and predict future user behaviors through cascading neural networks Specifically CHMSR first independently encodes each behavior sequence to extract user interests from the complex behavior relationships in the sequences Then it aggregates user preferences for different items through hypergraph convolution to capture global preferences Furthermore we employ cascading neural networks in behavior chains to capture directional feature dependencies in multi behavior sequences propagating messages from upstream to downstream behaviors In this way CHMSR can comprehensively consider user interests in multi behavior sequences and global preferences for items thus providing more accurate and personalized recommendations Our experimental results demonstrate that CHMSR significantly outperforms existing recommendation methods on various datasets validating its effectiveness and practicality
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2024 11th International Conference on Behavioural and Social Computing (BESC)
dc.relation.ispartof	2024 11th International Conference on Behavioural and Social Computing
dc.relation.isbasedon	10.1109/besc64747.2024.10780584
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Cascading Hypergraph Convolution Networks for Multi-Behavior Sequential Recommendation*
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Harbin, China
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/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Manufacturing (CAM)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Manufacturing (CAM)/Centre for Advanced Manufacturing (CAM) Associate Members
utslib.copyright.status	in_progress	*
dc.date.updated	2025-02-27T22:05:29Z
pubs.finish-date	2024-08-18
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2024-08-16
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

In current research on recommendation systems exploring multi behavior sequence recommendation has become a crucial topic It is well known that user interactions on online platforms such as social media websites news aggregation applications involve not only singular actions like reading or clicking but also various other behaviors such as commenting sharing and bookmarking These diverse interactions reflect different facets of user preferences in item interaction sequences Therefore understanding and integrating these diverse behaviors to effectively represent user preferences personalized is essential To better utilize temporal information we propose a Cascading Hypergraph Convolution Networks For Multi Behavior Sequential Recommendation CHMSR framework CHMSR aims to capture the underlying behavioral preferences in user sequence interactions and predict future user behaviors through cascading neural networks Specifically CHMSR first independently encodes each behavior sequence to extract user interests from the complex behavior relationships in the sequences Then it aggregates user preferences for different items through hypergraph convolution to capture global preferences Furthermore we employ cascading neural networks in behavior chains to capture directional feature dependencies in multi behavior sequences propagating messages from upstream to downstream behaviors In this way CHMSR can comprehensively consider user interests in multi behavior sequences and global preferences for items thus providing more accurate and personalized recommendations Our experimental results demonstrate that CHMSR significantly outperforms existing recommendation methods on various datasets validating its effectiveness and practicality

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

http://hdl.handle.net/10453/185408