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

Abstract

Solving 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 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.

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Reproductions