@InProceedings{wang-liu-zhao:2017:Long,
  author    = {Wang, Xuepeng  and  Liu, Kang  and  Zhao, Jun},
  title     = {Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {366--376},
  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.},
  url       = {http://aclweb.org/anthology/P17-1034}
}