NetDP：用于蚂蚁信用支付中的默认预测的工业规模分布式网络表示框架(CS.LG)---蔡小雪7100294

蚂蚁信用支付是蚂蚁商业风险组中的消费信贷服务。与信用卡类似，贷款违约是该信贷产品的主要风险之一。因此，有效的违约预测算法是公司减少损失和增加利润的关键。但是，我们所面临的挑战与传统信用卡服务所面临的挑战不同。第一个是可伸缩性。蚂蚁商业中的大量用户及其行为要求能够处理工业规模的数据并有效地进行模型训练。第二个挑战是冷启动问题。与传统银行对信用卡申请的人工审核不同，蚂蚁信用支付的信用额度是根据从大数据中学到的知识自动提供给用户的。但是，由于缺乏足够的信用行为，因此会为新用户提供默认预测。它要求该提案应利用其他新的数据源来缓解冷启动问题。考虑到上述挑战和蚂蚁商业中的特殊情况，我们尝试将默认预测与网络信息结合起来，以缓解冷启动问题。在本文中，我们提出了一个称为NetDP的工业规模分布式网络表示框架，用于在蚂蚁信用支付中进行默认预测。该提案探讨了由用户之间的各种交互作用生成的网络信息，并将无监督和受监督的网络表示混合在一个用于默认预测问题的统一框架中。此外，我们提出了我们的提案的基于参数服务器的分布式实现，以应对可伸缩性挑战。实验结果证明了我们的建议的有效性，特别是在冷启动问题上，以及工业规模数据集的效率。

原文标题：NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay

原文：Ant Credit Pay is a consumer credit service in Ant Financial Service Group. Similar to credit card, loan default is one of the major risks of this credit product. Hence, effective algorithm for default prediction is the key to losses reduction and profits increment for the company. However, the challenges facing in our scenario are different from those in conventional credit card service. The first one is scalability. The huge volume of users and their behaviors in Ant Financial requires the ability to process industrial-scale data and perform model training efficiently. The second challenges is the cold-start problem. Different from the manual review for credit card application in conventional banks, the credit limit of Ant Credit Pay is automatically offered to users based on the knowledge learned from big data. However, default prediction for new users is suffered from lack of enough credit behaviors. It requires that the proposal should leverage other new data source to alleviate the cold-start problem. Considering the above challenges and the special scenario in Ant Financial, we try to incorporate default prediction with network information to alleviate the cold-start problem. In this paper, we propose an industrial-scale distributed network representation framework, termed NetDP, for default prediction in Ant Credit Pay. The proposal explores network information generated by various interaction between users, and blends unsupervised and supervised network representation in a unified framework for default prediction problem. Moreover, we present a parameter-server-based distributed implement of our proposal to handle the scalability challenge. Experimental results demonstrate the effectiveness of our proposal, especially in cold-start problem, as well as the efficiency for industrial-scale dataset.

原文作者：Jianbin Lin, Zhiqiang Zhang, Jun Zhou, Xiaolong Li, Jingli Fang, Yanming Fang, Quan Yu, Yuan Qi

原文地址：https://arxiv.org/abs/2004.00201