Dream11 takes pride in being a unique platform that enables over 190 million fantasy sports users to demonstrate their skills and connect deeper with their favorite sports. While managing such a scale, one issue we are faced with is duplicate/multiple account creation in the system. This is done by some users with the intent of abusing the platform, typically for bonus offers. The challenge is to detect these multiple accounts before it is too late. We propose a graph-based solution to solve this problem in which we first predict edges/associations between users. Using the edge information we highlight clusters of colluding multiple accounts. In this paper, we talk about our distributed ML system which is deployed to serve and support the inferences from our detection models. The challenge is to do this in real-time in order to take corrective actions. A core part of this setup also involves human-in-the-loop components for validation, feedback, and ground-truth labeling.

翻译：摘要：Dream11以构建独特平台为傲，该平台支持逾1.9亿奇幻体育用户展现技能并与挚爱运动建立更深联结。在管理如此大规模系统的过程中，我们面临的一个核心问题是系统中重复/多重账户的创建。部分用户出于滥用平台的目的（通常涉及优惠奖励获取）实施上述行为。挑战在于如何在造成严重后果前对多重账户进行检测。我们提出了一种基于图的解决方案：首先预测用户间的边/关联关系，继而利用边信息凸显共谋多重账户的聚类簇。本文阐述了为实现检测模型推理服务的分布式机器学习系统部署方案。核心挑战在于需实时完成推理以采取纠正措施。该架构的关键环节还包含人与系统协同的验证、反馈及基准标注组件。