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众多领域中的关键问题，它通过利用序列日志来识别和关联属于同一用户的不同设备。以往的数据挖掘技术难以处理日志之间的长距离依赖和高阶连接关系。近期，研究者将该问题建模为图问题，并提出了双层图上下文嵌入（TGCE）神经网络架构，其性能优于以往方法。本文提出了一种新颖的分层图神经网络架构（HGNN），其第二层设计相比TGCE具有更高的计算效率。此外，我们在模型中引入了交叉注意力（Cross-Att）机制，相较当前最优的TGCE方法性能提升了5%。