Rahul Agarwal (ShareChat), Sarang Brahme (ShareChat), Abhishek Srivastava (IIM Visakhapatnam), Liu Yong (Huawei), Athirai Irissappane (Amazon)

This year’s challenge will focus on online advertising, improving deep funnel optimization, and user privacy. Online advertising has been a multi-billion-dollar industry since the early 2000 and has played a significant role in the growth of the internet. The key advantage of online advertising over conventional mass advertising is its inherent ability to personalize to users, democratizing advertising and enabling businesses of all sizes to participate, and providing the measurable impact of money spent to the advertisers. Over the past two decades, the nature of online advertising has also evolved tremendously from pure banner-based advertising, where advertisers were charged based on the number of ad impressions, to deep funnel optimizations, where advertisers can optimize for eventual sales.The efficacy of deep funnel optimization required extensive personalization and opened up rich problems in real-time auction design, large-scale machine learning, modeling delayed feedback, and behavioral understanding. As these systems matured, we also started developing a rich understanding of the need to preserve user privacy, ensure AI fairness, and prevent adversarial exploitation of the platform. In this challenge, we aim to provide a real-world ad dataset from the Sharechat and Moj apps to act as a benchmark for research into deep funnel optimization with a focus on user privacy. As part of the challenge, ShareChat will be releasing an anonymized dataset corresponds to roughly 10M random users who visited the ShareChat + Moj app over three months.