Anzeige
Erschienen in:
2019 | OriginalPaper | Buchkapitel
Generating Behavior Features for Cold-Start Spam Review Detection
verfasst von : Xiaoya Tang, Tieyun Qian, Zhenni You
Erschienen in: Database Systems for Advanced Applications
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
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.