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.

"Machine learning" is a computer science discipline that refers to the ability for machines to learn with data and carry out tasks that would typically require human intelligence. The technology is growing quickly: according to Gartner, more than half of data and analytics services will be performed by machines rather than human beings by 2022, which is 10 percent more than today. The emergence of machine learning and its implementation into consumer facing applications coincides conveniently with today's real-time economy. Machine learning drives a decrease in fraud before it impacts the victim, just as our society has become as impatient as ever. In fact, more than 60 percent of people increasingly feel that waiting for something that should happen instantaneously impacts their perception of the underlying brand -- which is especially true when it comes to identity or financial fraud.

"Machine learning" is not just a buzzword for futuristic applications; it is the concept of machines carrying out tasks on their own that would typically require human intelligence. Its emergence is very much happening now. It is at the top of Gartner's hype cycle. In fact, Gartner predicts that by 2022, more than half of data and analytics services will be performed by machines instead of human beings, up from 10 percent today. And while not all machine learning use cases include real-time analytics, there is a definitive growth trend in the market for real-time decision making powered by machine learning.

It is part of a new breed of A.I. solutions that could change the way we do business forever. Fresh from the stage at NASDAQ Marketsite -- as part of AdWeek New York -- Steve Gold, CMO at IBM Watson, spoke with me about the future of A.I., machine-to-human interaction, and advertising technology. The first question most people ask about A.I. is, "Will it replace humans?" "What's interesting about job displacement, not only in A.I. but in technology in general, is it's the same conversation we've been having since the dawn of the computing age," Gold told me. "IBM mainframes created jobs, rather than removing them." And while some jobs will be replaced, just as they have been throughout the course of time, new roles appear every year.