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:跨设备用户匹配是许多领域(包括广告、推荐系统和网络安全)中的关键问题。它涉及使用序列日志识别和链接属于同一用户ID的不同设备。以往的数据挖掘技术在解决日志间的长程依赖和高阶连接方面遇到了难题。近期,研究人员将其建模为图形问题,并提出了一种双层图形上下文嵌入(TGCE)神经网络架构,其优于以前的方法。本文提出了一种新颖的分层图形神经网络架构(HGNN),其第二层设计比TGCE更具有计算效率。此外,我们引入了一个交叉注意机制(Cross-Att)到我们的模型中,与目前最先进的TGCE方法相比性能提高了5%。