Cross-device user matching is a critical problem in numerous domains, including advertising, recommender systems, and cybersecurity. It involves identifying and linking different devices belonging to the same person, utilizing sequence logs. Previous data mining techniques have struggled to address the long-range dependencies and higher-order connections between the logs. Recently, researchers have modeled this problem as a graph problem and proposed a two-tier graph contextual embedding (TGCE) neural network architecture, which outperforms previous methods. In this paper, we propose a novel hierarchical graph neural network architecture (HGNN), which has a more computationally efficient second level design than TGCE. Furthermore, we introduce a cross-attention (Cross-Att) mechanism in our model, which improves performance by 5% compared to the state-of-the-art TGCE method.

翻译：跨设备用户匹配是广告、推荐系统和网络安全等多个领域的关键问题，旨在利用序列日志识别并关联同一用户拥有的不同设备。传统数据挖掘技术难以有效处理日志间的长程依赖关系和高阶连接。近期，研究者将该问题建模为图问题，提出双层图上下文嵌入神经网络架构（TGCE），其性能优于先前方法。本文提出一种新型层级图神经网络架构（HGNN），其第二层设计相较于TGCE具有更高的计算效率。此外，我们在模型中引入交叉注意力机制（Cross-Att），相较于最先进的TGCE方法性能提升5%。