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

Ali Taghibakhshi, Mingyuan Ma, Ashwath Aithal, Onur Yilmaz, Haggai Maron, Matthew West

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationBig Data Analytics and Knowledge Discovery - 25th International Conference, DaWaK 2023, Proceedings
EditorsRobert Wrembel, Johann Gamper, Gabriele Kotsis, Ismail Khalil, A Min Tjoa
PublisherSpringer
Pages303-315
Number of pages13
ISBN (Print)9783031398308
DOIs
StatePublished - 2023
EventBig Data Analytics and Knowledge Discovery - 25th International Conference, DaWaK 2023, Proceedings - Penang, Malaysia
Duration: Aug 28 2023Aug 30 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14148 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceBig Data Analytics and Knowledge Discovery - 25th International Conference, DaWaK 2023, Proceedings
Country/TerritoryMalaysia
CityPenang
Period8/28/238/30/23

Keywords

  • Cross-attention
  • Graph neural network
  • User matching

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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