An Inferable Representation Learning for Fraud Review Detection with Cold-start Problem

Li, Q; Wu, Q; Zhu, C; Zhang, J; Zhao, W

An Inferable Representation Learning for Fraud Review Detection with Cold-start Problem

Li, Q

Wu, Q

Zhu, C Zhang, J Zhao, W

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2019, 2019-July
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Li, Q https://orcid.org/0000-0002-6149-5081	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Zhu, C	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Zhao, W	en_US
dc.date.available	2021-08-12T19:00:33Z
dc.date.issued	2019-07-01	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2019, 2019-July	en_US
dc.identifier.isbn	9781728119854	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133898
dc.description.abstract	© 2019 IEEE. Fraud review significantly damages the business reputation and also customers' trust to certain products. It has become a serious problem existing on the current social media. Various efforts have been put in to tackle such problems. However, in the case of cold-start where a review is posted by a new user who just pops up on the social media, common fraud detection methods may fail because most of them are heavily depended on the information about the user's historical behavior and its social relation to other users, yet such information is lacking in the cold-start case. This paper presents a novel Joint-bEhavior-and-Social-relaTion-infERable (JESTER) embedding method to leverage the user reviewing behavior and social relations for cold-start fraud review detection. JESTER embeds the deep characteristics of existing user behavior and social relations of users and items in an inferable user-item-review-rating representation space where the representation of a new user can be efficiently inferred by a closed-form solution and reflects the user's most probable behavior and social relations. Thus, a cold-start fraud review can be effectively detected accordingly. Our experiments show JESTER (i) performs significantly better in detecting fraud reviews on four real-life social media data sets, and (ii) effectively infers new user representation in the cold-start problem, compared to three state-of-the-art and two baseline competitors.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2019.8852437	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	An Inferable Representation Learning for Fraud Review Detection with Cold-start Problem	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-July	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
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/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2019-July	en_US

Abstract:

© 2019 IEEE. Fraud review significantly damages the business reputation and also customers' trust to certain products. It has become a serious problem existing on the current social media. Various efforts have been put in to tackle such problems. However, in the case of cold-start where a review is posted by a new user who just pops up on the social media, common fraud detection methods may fail because most of them are heavily depended on the information about the user's historical behavior and its social relation to other users, yet such information is lacking in the cold-start case. This paper presents a novel Joint-bEhavior-and-Social-relaTion-infERable (JESTER) embedding method to leverage the user reviewing behavior and social relations for cold-start fraud review detection. JESTER embeds the deep characteristics of existing user behavior and social relations of users and items in an inferable user-item-review-rating representation space where the representation of a new user can be efficiently inferred by a closed-form solution and reflects the user's most probable behavior and social relations. Thus, a cold-start fraud review can be effectively detected accordingly. Our experiments show JESTER (i) performs significantly better in detecting fraud reviews on four real-life social media data sets, and (ii) effectively infers new user representation in the cold-start problem, compared to three state-of-the-art and two baseline competitors.

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http://hdl.handle.net/10453/133898