Multimodal and Contrastive Learning for Click Fraud Detection

• URL: http://arxiv.org/abs/2105.03567v1
• Date: Sat, 8 May 2021 03:03:11 GMT
• Title: Multimodal and Contrastive Learning for Click Fraud Detection
• Authors: Weibin Li, Qiwei Zhong, Qingyang Zhao, Hongchun Zhang, Xiaonan Meng
• Abstract summary: We propose a Multimodal and Contrastive learning network for Click Fraud detection (MCCF) MCCF jointly utilizes wide and deep features, behavior sequence and heterogeneous network to distill click representations. With the real-world datasets containing 2.54 million clicks on Alibaba platform, we investigate the effectiveness of MCCF.
• Score: 3.958603405726725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advertising click fraud detection plays one of the vital roles in current E-commerce websites as advertising is an essential component of its business model. It aims at, given a set of corresponding features, e.g., demographic information of users and statistical features of clicks, predicting whether a click is fraudulent or not in the community. Recent efforts attempted to incorporate attributed behavior sequence and heterogeneous network for extracting complex features of users and achieved significant effects on click fraud detection. In this paper, we propose a Multimodal and Contrastive learning network for Click Fraud detection (MCCF). Specifically, motivated by the observations on differences of demographic information, behavior sequences and media relationship between fraudsters and genuine users on E-commerce platform, MCCF jointly utilizes wide and deep features, behavior sequence and heterogeneous network to distill click representations. Moreover, these three modules are integrated by contrastive learning and collaboratively contribute to the final predictions. With the real-world datasets containing 2.54 million clicks on Alibaba platform, we investigate the effectiveness of MCCF. The experimental results show that the proposed approach is able to improve AUC by 7.2% and F1-score by 15.6%, compared with the state-of-the-art methods.

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