Motif-Based Graph Attentional Neural Network for Web Service Recommendation

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aut.relation.articlenumber110512
aut.relation.endpage110512
aut.relation.journalKnowledge-Based Systems
aut.relation.startpage110512
dc.contributor.authorWang, Guiling
dc.contributor.authorYu, Jian
dc.contributor.authorNguyen, Mo
dc.contributor.authorZhang, Yuqi
dc.contributor.authorYongchareon, Sira
dc.contributor.authorHan, Yanbo
dc.date.accessioned2023-04-12T22:27:57Z
dc.date.available2023-04-12T22:27:57Z
dc.date.issued2023-03-27
dc.description.abstractDeep Neural Networks (DNN) based collaborative filtering has been successful in recommending services by effectively generalizing graph-structured data. However, most existing approaches focus on first-order interactions. Although recent approaches have utilized high-order connectivity, they still limit themselves to simple interactions and ignore the pattern of structural sub-graphs/motifs. In this study, we first explore the commonly used motifs in the Mashup-API interaction bipartite graph and propose a dedicated algorithm to generate the motif adjacency matrix. We then propose a Motif-based Graph Attention Network for service recommendation (MGSR) that utilizes a motif-based attention mechanism to capture the high-order information of various motifs, and a Collaborative Filtering model to generate the recommendation prediction. We have conducted extensive experiments on ProgrammableWeb dataset and our results demonstrate the superior performance of our proposed framework over some state-of-the-art approaches.
dc.identifier.citationKnowledge-Based Systems, ISSN: 0950-7051 (Print), Elsevier BV, 110512-110512. doi: 10.1016/j.knosys.2023.110512
dc.identifier.doi10.1016/j.knosys.2023.110512
dc.identifier.issn0950-7051
dc.identifier.urihttps://hdl.handle.net/10292/16076
dc.languageen
dc.publisherElsevier BV
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0950705123002629
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4611 Machine Learning
dc.subjectNeurosciences
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subject08 Information and Computing Sciences
dc.subject15 Commerce, Management, Tourism and Services
dc.subject17 Psychology and Cognitive Sciences
dc.subjectArtificial Intelligence & Image Processing
dc.subject4602 Artificial intelligence
dc.subject4605 Data management and data science
dc.subject4611 Machine learning
dc.titleMotif-Based Graph Attentional Neural Network for Web Service Recommendation
dc.typeJournal Article
pubs.elements-id498376
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