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dc.contributor.authorXi, Dongbo
dc.contributor.authorZhuang, Fuzhen
dc.contributor.authorZhu, Yongchun
dc.contributor.authorZhao, Pengpeng
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorHe, Qing
dc.date.accessioned2020-12-01T12:32:18Z
dc.date.available2020-12-01T12:32:18Z
dc.date.issued2020-07-12
dc.identifier.urihttp://hdl.handle.net/10754/666196
dc.description.abstractRecently, graph neural networks (GNNs) have been successfully applied to recommender systems. In recommender systems, the user's feedback behavior on an item is usually the result of multiple factors acting at the same time. However, a long-standing challenge is how to effectively aggregate multi-order interactions in GNN. In this paper, we propose a Graph Factorization Machine (GFM) which utilizes the popular Factorization Machine to aggregate multi-order interactions from neighborhood for recommendation. Meanwhile, cross-domain recommendation has emerged as a viable method to solve the data sparsity problem in recommender systems. However, most existing cross-domain recommendation methods might fail when confronting the graph-structured data. In order to tackle the problem, we propose a general cross-domain recommendation framework which can be applied not only to the proposed GFM, but also to other GNN models. We conduct experiments on four pairs of datasets to demonstrate the superior performance of the GFM. Besides, based on general cross-domain recommendation experiments, we also demonstrate that our cross-domain framework could not only contribute to the cross-domain recommendation task with the GFM, but also be universal and expandable for various existing GNN models.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2007.05911
dc.rightsArchived with thanks to arXiv
dc.titleGraph Factorization Machines for Cross-Domain Recommendation
dc.typePreprint
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.contributor.institutionThe Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China.
dc.contributor.institutionThe University of Chinese Academy of Sciences, Beijing 100049, China.
dc.contributor.institutionSoochow University.
dc.identifier.arxivid2007.05911
kaust.personZhang, Xiangliang
refterms.dateFOA2020-12-01T12:32:55Z


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