Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors
Xuepeng Wang, Kang Liu, Jun Zhao · 2017
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.