Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2023

Abstract

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.

Keywords

anomaly detection, graph neural networks, pre-training, prompt

Discipline

Artificial Intelligence and Robotics

Research Areas

Data Science and Engineering

Publication

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

First Page

4864

Last Page

4870

Identifier

10.1145/3583780.3615505

Publisher

Association for Computing Machinery (ACM)

City or Country

Birmingham, UK

Additional URL

https://doi.org/10.1145/3583780.3615505

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