Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation

Chen, X; Zhang, Y; Tsang, IW; Pan, Y; Su, J

Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation

Chen, X Zhang, Y Tsang, IW Pan, Y Su, J

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Journal Article
Citation:: ACM Transactions on Information Systems, 2023, 41, (1)
Issue Date:: 2023-01-09

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Field	Value	Language
dc.contributor.author	Chen, X
dc.contributor.author	Zhang, Y
dc.contributor.author	Tsang, IW
dc.contributor.author	Pan, Y
dc.contributor.author	Su, J
dc.date.accessioned	2024-03-03T23:27:13Z
dc.date.available	2024-03-03T23:27:13Z
dc.date.issued	2023-01-09
dc.identifier.citation	ACM Transactions on Information Systems, 2023, 41, (1)
dc.identifier.issn	1046-8188
dc.identifier.issn	1558-2868
dc.identifier.uri	http://hdl.handle.net/10453/176024
dc.description.abstract	Cross domain recommendation (CDR) is one popular research topic in recommender systems. This article focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have explored the shared-user representation to transfer knowledge across domains. However, the idea of shared-user representation resorts to learning the overlapped features of user preferences and suppresses the domain-specific features. Other works try to capture the domain-specific features by an MLP mapping but require heuristic human knowledge of choosing samples to train the mapping. In this article, we attempt to learn both features of user preferences in a more principled way. We assume that each user's preferences in one domain can be expressed by the other one, and these preferences can be mutually converted to each other with the so-called equivalent transformation. Based on this assumption, we propose an equivalent transformation learner (ETL), which models the joint distribution of user behaviors across domains. The equivalent transformation in ETL relaxes the idea of shared-user representation and allows the learned preferences in different domains to preserve the domain-specific features as well as the overlapped features. Extensive experiments on three public benchmarks demonstrate the effectiveness of ETL compared with recent state-of-The-Art methods. Codes and data are available online: https://github.com/xuChenSJTU/ETL-master.
dc.language	English
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation	http://purl.org/au-research/grants/arc/DP180100106
dc.relation	http://purl.org/au-research/grants/arc/DP200101328
dc.relation.ispartof	ACM Transactions on Information Systems
dc.relation.isbasedon	10.1145/3522762
dc.rights	info:eu-repo/semantics/closedAccess
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	Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation
dc.type	Journal Article
utslib.citation.volume	41
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/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-03T23:27:07Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	41
utslib.citation.issue	1

Abstract:

Cross domain recommendation (CDR) is one popular research topic in recommender systems. This article focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have explored the shared-user representation to transfer knowledge across domains. However, the idea of shared-user representation resorts to learning the overlapped features of user preferences and suppresses the domain-specific features. Other works try to capture the domain-specific features by an MLP mapping but require heuristic human knowledge of choosing samples to train the mapping. In this article, we attempt to learn both features of user preferences in a more principled way. We assume that each user's preferences in one domain can be expressed by the other one, and these preferences can be mutually converted to each other with the so-called equivalent transformation. Based on this assumption, we propose an equivalent transformation learner (ETL), which models the joint distribution of user behaviors across domains. The equivalent transformation in ETL relaxes the idea of shared-user representation and allows the learned preferences in different domains to preserve the domain-specific features as well as the overlapped features. Extensive experiments on three public benchmarks demonstrate the effectiveness of ETL compared with recent state-of-The-Art methods. Codes and data are available online: https://github.com/xuChenSJTU/ETL-master.

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http://hdl.handle.net/10453/176024