Hierarchical Fuzzy Graph Attention Network for Group Recommendation

Liang, R; Zhang, Q; Wang, J

Hierarchical Fuzzy Graph Attention Network for Group Recommendation

Liang, R Zhang, Q

Wang, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2021, 2021-July, pp. 1-6
Issue Date:: 2021-07-11

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Field	Value	Language
dc.contributor.author	Liang, R
dc.contributor.author	Zhang, Q https://orcid.org/0000-0001-9977-8418
dc.contributor.author	Wang, J
dc.date	2021-07-11
dc.date.accessioned	2022-07-02T07:58:59Z
dc.date.available	2022-07-02T07:58:59Z
dc.date.issued	2021-07-11
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2021, 2021-July, pp. 1-6
dc.identifier.isbn	9781665444071
dc.identifier.issn	1098-7584
dc.identifier.issn	1558-4739
dc.identifier.uri	http://hdl.handle.net/10453/158528
dc.description.abstract	Human's group activities have contributed to the development of group recommender systems. The group recommender system can provide personalised services for various online user groups through analysing groups' preferences. However, current group recommendation methods have failed to exploit complex relationships among users, groups and items when extracting groups' preferences. Meanwhile, most previous works are based on crisp techniques, which result in rigid preference profiling. Benefiting from the development of graph attention networks, this paper represents the complex relationships among users, groups and items as various graphs, including user-/group-item graph, user-group graph and user-user graph, and proposes a hierarchical fuzzy graph attention network (HGAT-F) to enhance fuzzy profiling for both groups and items. Experiments results on real world datasets show that HGAT-F has enhanced group recommendation than previous works.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems
dc.relation.isbasedon	10.1109/FUZZ45933.2021.9494581
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Hierarchical Fuzzy Graph Attention Network for Group Recommendation
dc.type	Conference Proceeding
utslib.citation.volume	2021-July
utslib.location.activity	Luxembourg, Luxembourg
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 - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-07-02T07:58:58Z
pubs.finish-date	2021-07-14
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-07-11
pubs.volume	2021-July
dc.location	Piscataway, USA

Abstract:

Human's group activities have contributed to the development of group recommender systems. The group recommender system can provide personalised services for various online user groups through analysing groups' preferences. However, current group recommendation methods have failed to exploit complex relationships among users, groups and items when extracting groups' preferences. Meanwhile, most previous works are based on crisp techniques, which result in rigid preference profiling. Benefiting from the development of graph attention networks, this paper represents the complex relationships among users, groups and items as various graphs, including user-/group-item graph, user-group graph and user-user graph, and proposes a hierarchical fuzzy graph attention network (HGAT-F) to enhance fuzzy profiling for both groups and items. Experiments results on real world datasets show that HGAT-F has enhanced group recommendation than previous works.

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http://hdl.handle.net/10453/158528