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
会议名称the Annual Meeting of the Association for Computational Linguistics (ACL-2017)
会议录名称proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL-2017)
会议日期2017-7
会议地点Vancouver, Canada
摘要Solving 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.
关键词Cold-start Review Spam Jointly Embedding
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/14497
专题模式识别国家重点实验室_自然语言处理
通讯作者Liu Kang
作者单位1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
推荐引用方式
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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