Fraud Detection in Online Product Review Systems via Heterogeneous Graph Transformer

Abstract

In online product review systems, users are allowed to submit reviews about their purchased items or services. However, fake reviews posted by fraudulent users often mislead consumers and bring losses to enterprises. Traditional fraud detection algorithm mainly utilizes rule-based methods, which is insufficient for the rich user interactions and graph-structured data. In recent years, graph-based methods  have  been  proposed  to handle this situation, but few prior works have noticed the camouflage fraudster’s behavior and inconsistency heterogeneous nature. Existing methods have either not addressed these two problems or only partially, which results in poor performance. Alternatively, we propose a new model named Fraud Aware Heterogeneous Graph Transformer (FAHGT), to address camouflages and inconsistency problems in a unified manner. FAHGT adopts a type-aware feature mapping mechanism to handle heterogeneous graph data, then implementing various relation scoring methods to alleviate inconsistency and discover camouflage. Finally, the neighbors’ features are aggregated together to build an informative representation. Experimental results on different types of real-world datasets demonstrate  that FAHGT outperforms the state-of-the-art baselines.