CATeye: Coupled Attribute-Topology Invariance Learning for Voucher Abuse Detection
Read the original on arXiv Machine Learning →The paper introduces CATeye, a Coupled Attribute-Topology Invariance Learning framework designed to detect voucher abuse in e-commerce. It addresses coupled attribute-topology shifts by employing an Attribute Invariance Selector to mask non-invariant attributes and an Edge Invariance Selector to sample invariant subgraphs, thereby creating multiple views that emphasize domain-invariant representations. Experiments on a Lazada dataset and a public benchmark demonstrate that CATeye outperforms nine baseline methods, achieving up to an 8.61% improvement in average F1 score.
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