Towards better graph representation: Two-branch collaborative graph neural networks for multimodal marketing intention detection

Zhang, L; Zhang, J; Li, Z; Xu, J

Towards better graph representation: Two-branch collaborative graph neural networks for multimodal marketing intention detection

Zhang, L Zhang, J

Li, Z Xu, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Multimedia and Expo, 2020, 2020-July, pp. 1-6
Issue Date:: 2020-07-01

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Field	Value	Language
dc.contributor.author	Zhang, L
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541
dc.contributor.author	Li, Z
dc.contributor.author	Xu, J
dc.date	2020-07-06
dc.date.accessioned	2020-10-06T02:04:29Z
dc.date.available	2020-10-06T02:04:29Z
dc.date.issued	2020-07-01
dc.identifier.citation	Proceedings - IEEE International Conference on Multimedia and Expo, 2020, 2020-July, pp. 1-6
dc.identifier.isbn	9781728113319
dc.identifier.issn	1945-7871
dc.identifier.issn	1945-788X
dc.identifier.uri	http://hdl.handle.net/10453/143099
dc.description.abstract	© 2020 IEEE. Inspired by the fact that spreading and collecting information through the Internet becomes the norm, more and more people choose to post for-profit contents (images and texts) in social networks. Due to the difficulty of network censors, malicious marketing may be capable of harming the society. Therefore, it is meaningful to detect marketing intentions online automatically. However, gaps between multimodal data make it difficult to fuse images and texts for content marketing detection. To this end, this paper proposes Two-Branch Collaborative Graph Neural Networks to collaboratively represent multimodal data by Graph Convolution Networks (GCNs) in an end-to-end fashion. We first separately embed groups of images and texts by GCNs layers from two views and further adopt the proposed multimodal fusion strategy to learn the graph representation collaboratively. Experimental results demonstrate that our proposed method achieves superior graph classification performance for marketing intention detection.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - IEEE International Conference on Multimedia and Expo
dc.relation.ispartof	2020 IEEE International Conference on Multimedia and Expo (ICME)
dc.relation.isbasedon	10.1109/ICME46284.2020.9102918
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Towards better graph representation: Two-branch collaborative graph neural networks for multimodal marketing intention detection
dc.type	Conference Proceeding
utslib.citation.volume	2020-July
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-10-06T02:04:25Z
pubs.finish-date	2020-07-10
pubs.publication-status	Published
pubs.start-date	2020-07-06
pubs.volume	2020-July

Abstract:

© 2020 IEEE. Inspired by the fact that spreading and collecting information through the Internet becomes the norm, more and more people choose to post for-profit contents (images and texts) in social networks. Due to the difficulty of network censors, malicious marketing may be capable of harming the society. Therefore, it is meaningful to detect marketing intentions online automatically. However, gaps between multimodal data make it difficult to fuse images and texts for content marketing detection. To this end, this paper proposes Two-Branch Collaborative Graph Neural Networks to collaboratively represent multimodal data by Graph Convolution Networks (GCNs) in an end-to-end fashion. We first separately embed groups of images and texts by GCNs layers from two views and further adopt the proposed multimodal fusion strategy to learn the graph representation collaboratively. Experimental results demonstrate that our proposed method achieves superior graph classification performance for marketing intention detection.

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http://hdl.handle.net/10453/143099