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Ripple knowledge graph convolutional networks for recommendation systems

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dc.contributor.authorLi, C.
dc.contributor.authorCao, Y.
dc.contributor.authorZhu, Y.
dc.contributor.authorCheng, D.
dc.contributor.authorLi, C.
dc.contributor.authorMorimoto, Y.
dc.date.issued2024
dc.description.abstractUsing knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model’s interpretability and accuracy. This paper introduces an end-to-end deep learning model, named representation-enhanced knowledge graph convolutional networks (RKGCN), which dynamically analyses each user’s preferences and makes a recommendation of suitable items. It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information in knowledge graphs. RKGCN is able to offer more personalized and relevant recommendations in three different scenarios. The experimental results show the superior effectiveness of our model over 5 baseline models on three real-world datasets including movies, books, and music.
dc.identifier.citationMachine Intelligence Research, 2024; 21(3):481-494
dc.identifier.doi10.1007/s11633-023-1440-x
dc.identifier.issn2731-538X
dc.identifier.issn2731-5398
dc.identifier.urihttps://hdl.handle.net/11541.2/37579
dc.language.isoen
dc.publisherZhongguo Kexue Zazhishe, Science in China Press
dc.rightsCopyright 2024 The Authors (https://link.springer.com/journal/11633) Access Condition Notes: Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
dc.source.urihttps://doi.org/10.1007/s11633-023-1440-x
dc.subjectdeep learning
dc.subjectgraph convolutional networks (GCNs)
dc.subjectgraph neural networks (GNNs)
dc.subjectknowledge graph
dc.subjectrecommendation systems
dc.titleRipple knowledge graph convolutional networks for recommendation systems
dc.typeJournal article
pubs.publication-statusPublished
ror.fileinfo12286210280001831 13286210270001831 Open Access Published Version
ror.mmsid9916828131201831

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