With the development of social media sites, user credit grading, which served as an important and fashionable problem, has attracted substantial attention from a slew of developers and operators of mobile applications. In particular, multi-grades of user credit aimed to achieve (1) anomaly detection and risk early warning and (2) personalized information and service recommendation for privileged users. The above two goals still remained as up-to-date challenges. To these ends, in this article, we propose a novel regression-based method. Technically speaking, we define three natural ordered categories including BlockList , GeneralList , and AllowList according to users’ registration and behavior information, which preserve both the global hierarchical relationship of user credit and the local coincident features of users, and hence formulate user credit grading as the ordinal regression problem. Our method is inspired by KDLOR ( kernel discriminant learning for ordinal regression ), which is an effective and efficient model to solve ordinal regression by mapping high-dimension samples to the discriminant region with supervised conditions. However, the performance of KDLOR is fragile to the extreme imbalanced distribution of users. To address this problem, we propose a robust sampling model to balance distribution and avoid overfit or underfit learning, which induces the triplet metric constraint to obtain hard negative samples that well represent the latent ordered class information. A step further, another salient problem lies in ambiguous samples that are noises or located in the classification boundary to impede optimized mapping and embedding. To this problem, we improve sampling by identifying and evading noises in triplets to obtain hard negative samples to enhance robustness and effectiveness for ordinal regression. We organized training and testing datasets for user credit grading by selecting limited items from real-life huge tables of users in the mobile application, which are used in similar problems; moreover, we theoretically and empirically demonstrate the advantages of the proposed model over established datasets.

中文翻译：

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稳健的序数回归：基于三重损失抽样的用户信用评分

随着社交媒体网站的发展，用户信用评级作为一个重要且时尚的问题，引起了众多移动应用开发商和运营商的广泛关注。特别是多级用户信用，旨在实现（1）异常检测和风险预警；（2）针对特权用户的个性化信息和服务推荐。上述两个目标仍然是最新的挑战。为此，在本文中，我们提出了一种新颖的基于回归的方法。从技术上讲，我们定义了三个自然有序的类别，包括黑名单,一般清单， 和允许列表根据用户的注册信息和行为信息，既保留了用户信用的全局层次关系，又保留了用户的局部重合特征，从而将用户信用分级制定为序数回归问题。我们的方法受到 KDLOR 的启发（序数回归的核判别学习)，这是一种通过将高维样本映射到具有监督条件的判别区域来解决序数回归的有效且高效的模型。然而，KDLOR 的性能对于极端不平衡的用户分布来说是脆弱的。为了解决这个问题，我们提出了一种稳健的采样模型来平衡分布并避免过拟合或欠拟合学习，这会导致三重度量约束来获得能够很好地代表潜在有序类信息的硬负样本。更进一步，另一个突出的问题在于模棱两可的样本，这些样本是噪声或位于分类边界，阻碍优化映射和嵌入。针对这个问题，我们通过识别和规避三元组中的噪声来改进采样以获得硬负样本，以增强序数回归的鲁棒性和有效性。我们通过在移动应用程序中从现实生活中巨大的用户表中选择有限的项目来组织用户信用评分的训练和测试数据集，这些项目用于类似的问题；此外，我们从理论上和经验上证明了所提出的模型相对于已建立的数据集的优势。