Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81232
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorFan, W-
dc.creatorMa, Y-
dc.creatorLi, Q-
dc.creatorHe, Y-
dc.creatorZhao, E-
dc.creatorTang, J-
dc.creatorYin, D-
dc.date.accessioned2019-08-23T08:29:51Z-
dc.date.available2019-08-23T08:29:51Z-
dc.identifier.isbn9781450366748-
dc.identifier.urihttp://hdl.handle.net/10397/81232-
dc.description2019 World Wide Web Conference, WWW 2019, United States, 13-17 May 2019en_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machinery, Incen_US
dc.rights© 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.en_US
dc.rightsThe following publication Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., & Yin, D. (2019, May). Graph Neural Networks for Social Recommendation. In The World Wide Web Conference (pp. 417-426). ACM, is available at https://doi.org/10.1145/3308558.3313488en_US
dc.subjectGraph neural networksen_US
dc.subjectNeural networksen_US
dc.subjectRecommender systemsen_US
dc.subjectSocial networken_US
dc.subjectSocial recommendationen_US
dc.titleGraph neural networks for social recommendationen_US
dc.typeConference Paperen_US
dc.identifier.spage417-
dc.identifier.epage426-
dc.identifier.doi10.1145/3308558.3313488-
dcterms.abstractIn recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationThe Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, 2019, p. 417-426-
dcterms.issued2019-
dc.identifier.scopus2-s2.0-85066890405-
dc.relation.conferenceWorld Wide Web Conference-
dc.description.validate201908 bcma-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
Appears in Collections:Conference Paper
Files in This Item:
File Description SizeFormat 
Fan_Graph_neural_networks.pdf4.18 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

181
Citations as of Jul 3, 2022

Downloads

1,081
Citations as of Jul 3, 2022

SCOPUSTM   
Citations

377
Citations as of Jun 30, 2022

WEB OF SCIENCETM
Citations

270
Citations as of Jun 30, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.