AMT-CDR: A Deep Adversarial Multi-channel Transfer Network for Cross-domain Recommendation

Lu, K; Zhang, Q; Hughes, D; Zhang, G; Lu, J

AMT-CDR: A Deep Adversarial Multi-channel Transfer Network for Cross-domain Recommendation

Lu, K Zhang, Q

Hughes, D Zhang, G Lu, J

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Transactions on Intelligent Systems and Technology

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Field	Value	Language
dc.contributor.author	Lu, K
dc.contributor.author	Zhang, Q https://orcid.org/0000-0001-9977-8418
dc.contributor.author	Hughes, D
dc.contributor.author	Zhang, G
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.date.accessioned	2024-03-11T05:41:56Z
dc.date.available	2024-03-11T05:41:56Z
dc.identifier.citation	ACM Transactions on Intelligent Systems and Technology
dc.identifier.issn	2157-6904
dc.identifier.issn	2157-6912
dc.identifier.uri	http://hdl.handle.net/10453/176482
dc.description.abstract	<jats:p> Recommender systems are one of the most successful applications of using AI for providing personalized e-services to customers. However, data sparsity is presenting enormous challenges that are hindering the further development of advanced recommender systems. Although cross-domain recommendation partly overcomes data sparsity by transferring knowledge from a source domain with relatively dense data to augment data in the target domain, the current methods do not handle heterogeneous data very well. For example, using today’s cross-domain transfer learning schemes with data comprising clicks, ratings, user reviews, item meta data, and knowledge graphs will likely result in a poorly-performing model. User preferences will not be comprehensively profiled, and accurate recommendations will not be generated. To solve these three challenges – i.e., handling heterogeneous data, avoiding negative transfer, and dealing with data sparsity – we designed a new end-to-end deep <jats:bold>a</jats:bold> dversarial <jats:bold>m</jats:bold> ulti-channel <jats:bold>t</jats:bold> ransfer network for <jats:bold>c</jats:bold> ross- <jats:bold>d</jats:bold> omain <jats:bold>r</jats:bold> ecommendation named AMT-CDR. Heterogeneous data is handled by constructing a cross-domain graph based on real-world knowledge graphs – we used Freebase and YAGO. Negative transfer is prevented through an adversarial learning strategy that maintains consistency across the different data channels. And data sparsity is addressed with an end-to-end neural network that considers data across multiple channels and generates accurate recommendations by leveraging knowledge from both the source and target domains. Extensive experiments on three dual-target cross-domain recommendation tasks demonstrate the superiority of AMT-CDR compared to eight state-of-the-art methods. All source code is available at https://github.com/bjtu-lucas-nlp/AMT-CDR. </jats:p>
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	http://purl.org/au-research/grants/arc/DP220102635
dc.relation.ispartof	ACM Transactions on Intelligent Systems and Technology
dc.relation.isbasedon	10.1145/3641286
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4611 Machine learning
dc.title	AMT-CDR: A Deep Adversarial Multi-channel Transfer Network for Cross-domain Recommendation
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
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
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-11T05:41:55Z
pubs.publication-status	Published online

Abstract:

Recommender systems are one of the most successful applications of using AI for providing personalized e-services to customers. However, data sparsity is presenting enormous challenges that are hindering the further development of advanced recommender systems. Although cross-domain recommendation partly overcomes data sparsity by transferring knowledge from a source domain with relatively dense data to augment data in the target domain, the current methods do not handle heterogeneous data very well. For example, using today’s cross-domain transfer learning schemes with data comprising clicks, ratings, user reviews, item meta data, and knowledge graphs will likely result in a poorly-performing model. User preferences will not be comprehensively profiled, and accurate recommendations will not be generated. To solve these three challenges – i.e., handling heterogeneous data, avoiding negative transfer, and dealing with data sparsity – we designed a new end-to-end deep a dversarial m ulti-channel t ransfer network for c ross- d omain r ecommendation named AMT-CDR. Heterogeneous data is handled by constructing a cross-domain graph based on real-world knowledge graphs – we used Freebase and YAGO. Negative transfer is prevented through an adversarial learning strategy that maintains consistency across the different data channels. And data sparsity is addressed with an end-to-end neural network that considers data across multiple channels and generates accurate recommendations by leveraging knowledge from both the source and target domains. Extensive experiments on three dual-target cross-domain recommendation tasks demonstrate the superiority of AMT-CDR compared to eight state-of-the-art methods. All source code is available at https://github.com/bjtu-lucas-nlp/AMT-CDR.

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http://hdl.handle.net/10453/176482