Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap. Extensive experiments on both proprietary and public datasets demonstrate the strength of VPGNN in both few-shot and semi-supervised scenarios. Moreover, an online deployment of VPGNN in a production environment shows a 23.4% improvement over two existing deployed models.

翻译：优惠券滥用检测是电子商务中一项重要的异常检测问题。尽管已有许多基于图神经网络（GNN）的解决方案，但监督式范式依赖大量标注数据。一种常见的替代方法是采用无标签数据自监督预训练，并进一步在标注数据有限的特定下游任务上进行微调。然而，“预训练-微调”范式常因预训练任务与下游任务之间的目标差距而受到困扰。为此，我们提出VPGNN——一种基于提示微调的图神经网络框架，用于优惠券滥用检测。我们设计了一种新颖的图提示函数，将下游任务重构为与预训练中的前置任务相似的模式，从而缩小目标差距。在私有数据集和公开数据集上的大量实验表明，VPGNN在少样本和半监督场景下均表现出色。此外，在生产环境中在线部署VPGNN后，相比两个现有已部署模型取得了23.4%的提升。