2017
ACL
ACL 2017
Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors
Abstract
AbstractSolving 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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Interdisciplinary Bridge
— Deep Learning and Machine Learning
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Trend Setter
— Text Classification
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Keyword Pioneer
— review spam detection
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Embedding Learning
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Learning Paradigms > Unsupervised Learning
Deep Learning > Learning Types > Domain Adaptation
Machine Learning > Application Areas > Text Classification