Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors

	Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors
	Wang Xuepeng; Liu Kang; Zhao Jun
	2017-07
会议日期	2017-7
会议地点	Vancouver, Canada
关键词	Cold-start Review Spam Jointly Embedding
英文摘要	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.
会议录	proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL-2017)
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/41020]
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
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]. 见:. Vancouver, Canada. 2017-7.