Opinion fraud detection via neural autoencoder decision forest

Dong, M; Yao, L; Wang, X; Benatallah, B; Huang, C; Ning, X

Opinion fraud detection via neural autoencoder decision forest

Dong, M Yao, L Wang, X

Benatallah, B Huang, C Ning, X

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Pattern Recognition Letters, 2020, 132, (10), pp. 21-29
Issue Date:: 2020-04-01

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Field	Value	Language
dc.contributor.author	Dong, M
dc.contributor.author	Yao, L
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9582-3445
dc.contributor.author	Benatallah, B
dc.contributor.author	Huang, C
dc.contributor.author	Ning, X
dc.date.accessioned	2021-04-10T01:35:21Z
dc.date.available	2021-04-10T01:35:21Z
dc.date.issued	2020-04-01
dc.identifier.citation	Pattern Recognition Letters, 2020, 132, (10), pp. 21-29
dc.identifier.issn	0167-8655
dc.identifier.issn	1872-7344
dc.identifier.uri	http://hdl.handle.net/10453/147978
dc.description.abstract	Online reviews play an important role in influencing buyers’ daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users’ genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.
dc.language	English
dc.publisher	ELSEVIER
dc.relation	http://purl.org/au-research/grants/arc/DE180100251
dc.relation.ispartof	Pattern Recognition Letters
dc.relation.isbasedon	10.1016/j.patrec.2018.07.013
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Opinion fraud detection via neural autoencoder decision forest
dc.type	Journal Article
utslib.citation.volume	132
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
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 Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2021-04-10T01:35:20Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	132
utslib.citation.issue	10

Abstract:

Online reviews play an important role in influencing buyers’ daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users’ genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.

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http://hdl.handle.net/10453/147978