Journal
DATABASE SYSTEMS FOR ADVANCED APPLICATIONS
Volume 11448, Issue -, Pages 324-328Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-18590-9_38
Keywords
Spam review detection; Cold-start problem; Generative adversarial network
Funding
- NSFC [61572376]
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Existing studies on spam detection show that behavior features are effective in distinguishing spam and legitimate reviews. However, it usually takes a long time to collect such features and is hard to apply them to cold-start spam review detection tasks. In this paper, we exploit the generative adversarial network for addressing this problem. The key idea is to generate synthetic behavior features (SBFs) for new users from their easily accessible features (EAFs). We conduct extensive experiments on two Yelp datasets. Experimental results demonstrate that our proposed framework significantly outperforms the state-of-the-art methods.
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