English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Voucher abuse detection is an important anomaly detection problem inE-commerce. While many GNN-based solutions have emerged, the supervisedparadigm depends on a large quantity of labeled data. A popular alternative isto adopt self-supervised pre-training using label-free data, and furtherfine-tune on a downstream task with limited labels. Nevertheless, the"pre-train, fine-tune" paradigm is often plagued by the objective gap betweenpre-training and downstream tasks. Hence, we propose VPGNN, a prompt-basedfine-tuning framework on GNNs for voucher abuse detection. We design a novelgraph prompting function to reformulate the downstream task into a similartemplate as the pretext task in pre-training, thereby narrowing the objectivegap. Extensive experiments on both proprietary and public datasets demonstratethe 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.

Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks