In this work, we introduce an innovative autoregressive model leveraging
Generative Pretrained Transformer (GPT) architectures, tailored for fraud
detection in payment systems. Our approach innovatively confronts token
explosion and reconstructs behavioral sequences, providing a nuanced
understanding of transactional behavior through temporal and contextual
analysis. Utilizing unsupervised pretraining, our model excels in feature
representation without the need for labeled data. Additionally, we integrate a
differential convolutional approach to enhance anomaly detection, bolstering
the security and efficacy of one of the largest online payment merchants in
China. The scalability and adaptability of our model promise broad
applicability in various transactional contexts.

介绍了一种创新的自回归模型，利用了 Generative Pretrained Transformer (GPT) 架构，专为支付系统中的欺诈检测而设计。我们的方法创新性地解决了令牌爆炸问题，并通过时间和上下文分析重构行为序列，提供了对交易行为的细致理解。利用无监督预训练，我们的模型在特征表示方面表现突出，无需有标签的数据。此外，我们整合了一种差分卷积方法来增强异常检测，提高了中国最大在线支付商的安全性和效果。我们模型的可扩展性和适应性在各种交易背景下有着广泛的适用性。

规模化生成预训练：基于 Transformer 的交易行为编码用于欺诈检测

Generative Pretraining at Scale: Transformer-Based Encoding of  Transactional Behavior for Fraud Detection

An essential topic in online social network security is how to accurately
detect bot accounts and relieve their harmful impacts (e.g., misinformation,
rumor, and spam) on genuine users. Based on a real-world data set, we construct
behavioral sequences from raw event logs. After extracting critical
characteristics from behavioral time series, we observe differences between
bots and genuine users and similar patterns among bot accounts. We present a
novel social bot detection system BotShape, to automatically catch behavioral
sequences and characteristics as features for classifiers to detect bots. We
evaluate the detection performance of our system in ground-truth instances,
showing an average accuracy of 98.52% and an average f1-score of 96.65% on
various types of classifiers. After comparing it with other research, we
conclude that BotShape is a novel approach to profiling an account, which could
improve performance for most methods by providing significant behavioral
features.

本研究基于真实数据集构建了行为序列，从中提取关键特征，分析了社交机器人和真实用户之间的差异性，提出了一种新型社交机器人检测系统 - BotShape，通过行为序列和特征分类器自动捕获机器人。该系统在对比其他研究后表现更好，通过提供重要的行为特征可提高大多数方法的性能。评测结果表明，BotShape 检测系统在各种分类器中的平均准确率为 98.52％，平均 f1-score 为 96.65％。

BotShape：一种基于行为模式的新型社交机器人检测方法

BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns

Click-through rate prediction plays an important role in the field of
recommender system and many other applications. Existing methods mainly extract
user interests from user historical behaviors. However, behavioral sequences
only contain users' directly interacted items, which are limited by the
system's exposure, thus they are often not rich enough to reflect all the
potential interests. In this paper, we propose a novel method, named Dynamic
Sequential Graph Learning (DSGL), to enhance users or items' representations by
utilizing collaborative information from the local sub-graphs associated with
users or items. Specifically, we design the Dynamic Sequential Graph (DSG),
i.e., a lightweight ego subgraph with timestamps induced from historical
interactions. At every scoring moment, we construct DSGs for the target user
and the candidate item respectively. Based on the DSGs, we perform graph
convolutional operations iteratively in a bottom-up manner to obtain the final
representations of the target user and the candidate item. As for the graph
convolution, we design a Time-aware Sequential Encoding Layer that leverages
the interaction time information as well as temporal dependencies to learn
evolutionary user and item dynamics. Besides, we propose a Target-Preference
Dual Attention Layer, composed of a preference-aware attention module and a
target-aware attention module, to automatically search for parts of behaviors
that are relevant to the target and alleviate the noise from unreliable
neighbors. Results on real-world CTR prediction benchmarks demonstrate the
improvements brought by DSGL.

这篇论文提出了一种名为动态序列图学习（DSGL）的新方法，通过利用用户或物品相关的本地子图的协同信息来增强用户或物品的表示，以预测点击率。DSGL 使用底部向上的方法在动态顺序图中进行图卷积操作，设计了一个考虑时间信息和时间依赖性的时态序列编码层，并提出了一个目标 - 偏好双重注意层，以搜索与目标相关的行为部分并减轻来自不可靠邻居的噪声。实验结果表明，DSGL 方法可以提高 CTR 预测的准确性。