Exploiting intra- and inter-session dependencies for session-based recommendations

Wang, N; Wang, S; Wang, Y; Sheng, QZ; Orgun, MA

Exploiting intra- and inter-session dependencies for session-based recommendations

Wang, N Wang, S

Wang, Y Sheng, QZ Orgun, MA

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: World Wide Web, 2022, 25, (1), pp. 425-443
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Wang, N
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.contributor.author	Wang, Y
dc.contributor.author	Sheng, QZ
dc.contributor.author	Orgun, MA
dc.date.accessioned	2022-11-12T10:32:31Z
dc.date.available	2022-11-12T10:32:31Z
dc.date.issued	2022-01-01
dc.identifier.citation	World Wide Web, 2022, 25, (1), pp. 425-443
dc.identifier.issn	1386-145X
dc.identifier.issn	1573-1413
dc.identifier.uri	http://hdl.handle.net/10453/163400
dc.description.abstract	Session-based recommender systems (SBRSs) aim at predicting the next item via learning the dynamic and short-term preferences of users. Most of the existing SBRSs usually make predictions based on the intra-session dependencies embedded in session information only, ignoring more complex inter-session dependencies and other available side information (e.g., item attributes, users), which in turn greatly limits the improvement of the recommendation accuracy. In order to effectively extract both intra- and inter-session dependencies from not only the session information but also the side information, to further improve the accuracy of next-item recommendations, we propose a novel hypergraph learning (HL) framework. The HL framework mainly contains three modules, i.e., a hypergraph construction module, a hypergraph learning module, and a next-item prediction module. The hypergraph construction module constructs a hypergraph to connect the users, items and item attributes together in a unified way. Then, the hypergraph learning module learns the informative latent representation for each item by extracting both intra- and inter-session dependencies embedded in the constructed hypergraph. Also, a latent representation for each user is learned. After that, the learned latent representations are fed into the next-item prediction module for next-item recommendations. We conduct extensive experiments on two real-world datasets. The experimental results show that our HL framework outperforms the state-of-the-art approaches.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	World Wide Web
dc.relation.isbasedon	10.1007/s11280-021-00930-2
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0804 Data Format, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Information Systems
dc.title	Exploiting intra- and inter-session dependencies for session-based recommendations
dc.type	Journal Article
utslib.citation.volume	25
utslib.for	0804 Data Format
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-11-12T10:32:30Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	25
utslib.citation.issue	1

Abstract:

Session-based recommender systems (SBRSs) aim at predicting the next item via learning the dynamic and short-term preferences of users. Most of the existing SBRSs usually make predictions based on the intra-session dependencies embedded in session information only, ignoring more complex inter-session dependencies and other available side information (e.g., item attributes, users), which in turn greatly limits the improvement of the recommendation accuracy. In order to effectively extract both intra- and inter-session dependencies from not only the session information but also the side information, to further improve the accuracy of next-item recommendations, we propose a novel hypergraph learning (HL) framework. The HL framework mainly contains three modules, i.e., a hypergraph construction module, a hypergraph learning module, and a next-item prediction module. The hypergraph construction module constructs a hypergraph to connect the users, items and item attributes together in a unified way. Then, the hypergraph learning module learns the informative latent representation for each item by extracting both intra- and inter-session dependencies embedded in the constructed hypergraph. Also, a latent representation for each user is learned. After that, the learned latent representations are fed into the next-item prediction module for next-item recommendations. We conduct extensive experiments on two real-world datasets. The experimental results show that our HL framework outperforms the state-of-the-art approaches.

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http://hdl.handle.net/10453/163400