TY - GEN
T1 - Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching
AU - Taghibakhshi, Ali
AU - Ma, Mingyuan
AU - Aithal, Ashwath
AU - Yilmaz, Onur
AU - Maron, Haggai
AU - West, Matthew
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Cross-attention
KW - Graph neural network
KW - User matching
UR - http://www.scopus.com/inward/record.url?scp=85172409302&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85172409302&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-39831-5_28
DO - 10.1007/978-3-031-39831-5_28
M3 - Conference contribution
AN - SCOPUS:85172409302
SN - 9783031398308
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 303
EP - 315
BT - Big Data Analytics and Knowledge Discovery - 25th International Conference, DaWaK 2023, Proceedings
A2 - Wrembel, Robert
A2 - Gamper, Johann
A2 - Kotsis, Gabriele
A2 - Khalil, Ismail
A2 - Tjoa, A Min
PB - Springer
T2 - Big Data Analytics and Knowledge Discovery - 25th International Conference, DaWaK 2023, Proceedings
Y2 - 28 August 2023 through 30 August 2023
ER -