HSR: Hyperbolic Social Recommender

Li, A; Yang, B; Hussain, FK; Huo, H

HSR: Hyperbolic Social Recommender

Li, A Yang, B Hussain, FK Huo, H

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Information Sciences, 2022, 585, pp. 275-288
Issue Date:: 2022-03-01

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Field	Value	Language
dc.contributor.author	Li, A
dc.contributor.author	Yang, B
dc.contributor.author	Hussain, FK
dc.contributor.author	Huo, H https://orcid.org/0000-0003-2440-714X
dc.date.accessioned	2023-03-24T00:42:28Z
dc.date.available	2023-03-24T00:42:28Z
dc.date.issued	2022-03-01
dc.identifier.citation	Information Sciences, 2022, 585, pp. 275-288
dc.identifier.issn	0020-0255
dc.identifier.uri	http://hdl.handle.net/10453/168296
dc.description.abstract	With the prevalence of online social media, users’ social connections have been widely studied and utilized to enhance the performance of recommender systems. In this paper, we explore the use of hyperbolic geometry for social recommendation. We present the Hyperbolic Social Recommender (HSR), a novel social recommendation framework that utilizes hyperbolic geometry to boost the performance. With the help of hyperbolic space, HSR can learn high-quality user and item representations to better model user-item interaction and user-user social relations. Through extensive experiments on four real-world datasets, we show that our proposed HSR outperforms its Euclidean counterpart and state-of-the-art social recommenders in click-through rate prediction and top-K recommendation, demonstrating the effectiveness of social recommendation in the hyperbolic space.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2021.11.040
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	HSR: Hyperbolic Social Recommender
dc.type	Journal Article
utslib.citation.volume	585
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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
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	2023-03-24T00:42:27Z
pubs.publication-status	Published
pubs.volume	585

Abstract:

With the prevalence of online social media, users’ social connections have been widely studied and utilized to enhance the performance of recommender systems. In this paper, we explore the use of hyperbolic geometry for social recommendation. We present the Hyperbolic Social Recommender (HSR), a novel social recommendation framework that utilizes hyperbolic geometry to boost the performance. With the help of hyperbolic space, HSR can learn high-quality user and item representations to better model user-item interaction and user-user social relations. Through extensive experiments on four real-world datasets, we show that our proposed HSR outperforms its Euclidean counterpart and state-of-the-art social recommenders in click-through rate prediction and top-K recommendation, demonstrating the effectiveness of social recommendation in the hyperbolic space.

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http://hdl.handle.net/10453/168296