CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors
Wang Xuepeng1,2; Liu Kang1; Zhao Jun1,2
2017-07
Conference Namethe Annual Meeting of the Association for Computational Linguistics (ACL-2017)
Source Publicationproceedings of the Annual Meeting of the Association for Computational Linguistics (ACL-2017)
Conference Date2017-7
Conference PlaceVancouver, Canada
AbstractSolving the cold-start problem in review spam detection is an urgent and significant task. It can help the on-line review websites to relieve the damage of spammers in time, but has never been investigated by previous work. This paper proposes a novel neural network model to detect review spam for the cold-start problem, by learning to represent the new reviewers’ review with jointly embedded textual and behavioral information. Experimental results prove the proposed model achieves an effective performance and possesses preferable domain-adaptability. It is also applicable to a large-scale dataset in an unsupervised way.
KeywordCold-start Review Spam Jointly Embedding
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14497
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorLiu Kang
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang Xuepeng,Liu Kang,Zhao Jun. Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors[C],2017.
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