# Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching

arXiv: https://arxiv.org/abs/2304.03215 · HTML: https://mlsanity.com/paper/2304.03215/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Session-based Recommendation with Graph Neural Networks](https://mlsanity.com/paper/1811.00855/) (2018)
- [GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation](https://mlsanity.com/paper/2007.02747/) (2020)
- [Cross-Device User Matching Based on Massive Browse Logs: The Runner-Up Solution for the 2016 CIKM Cup](https://mlsanity.com/paper/1610.03928/) (2016)
- [Cross Device Matching for Online Advertising with Neural Feature Ensembles : First Place Solution at CIKM Cup 2016](https://mlsanity.com/paper/1610.07119/) (2016)
- [Siamese Cookie Embedding Networks for Cross-Device User Matching](https://mlsanity.com/paper/1803.10450/) (2018)
- [Evaluation of Linear Implicit Quantized State System method for analyzing mission performance of power systems](https://mlsanity.com/paper/2302.09397/) (2023)
- [Suitability of Quantized DEVS LIM Methods for Simulation of Power Systems](https://mlsanity.com/paper/2302.13437/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Late-Fusion Multimodal AI Framework for Privacy-Preserving Deduplication in National Healthcare Data Environments](https://mlsanity.com/paper/2603.04595/) (2026)

## Abstract (arXiv)

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.