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.
A. Taghibakhshi—This work was done while Ali Taghibakhshi was an intern at NVIDIA.
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References
Chen, T., Wong, R.C.W.: Handling information loss of graph neural networks for session-based recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1172–1180 (2020)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Gharaibeh, A., et al.: Smart cities: a survey on data management, security, and enabling technologies. IEEE Commun. Surv. Tutor. 19(4), 2456–2501 (2017)
Gholizadeh, N.: Iec 61850 standard and its capabilities in protection systems (2016)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
Huang, H., et al.: Two-tier graph contextual embedding for cross-device user matching. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 730–739 (2021)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196. PMLR (2014)
Li, B., Wang, W., Sun, Y., Zhang, L., Ali, M.A., Wang, Y.: Grapher: token-centric entity resolution with graph convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 8172–8179 (2020)
Lian, J., Xie, X.: Cross-device user matching based on massive browse logs: the runner-up solution for the 2016 cikm cup. arXiv preprint arXiv:1610.03928 (2016)
Pan, Z., Cai, F., Chen, W., Chen, H., De Rijke, M.: Star graph neural networks for session-based recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1195–1204 (2020)
Phan, M.C., Sun, A., Tay, Y.: Cross-device user linking: url, session, visiting time, and device-log embedding. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 933–936 (2017)
Phan, M.C., Tay, Y., Pham, T.A.N.: Cross device matching for online advertising with neural feature ensembles: first place solution at cikm cup 2016. arXiv preprint arXiv:1610.07119 (2016)
Qiu, R., Yin, H., Huang, Z., Chen, T.: Gag: global attributed graph neural network for streaming session-based recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 669–678 (2020)
Ramos, J., et al.: Using tf-idf to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242, pp. 29–48. Citeseer (2003)
Sun, F., et al.: Bert4rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441–1450 (2019)
Tanielian, U., Tousch, A.M., Vasile, F.: Siamese cookie embedding networks for cross-device user matching. In: Companion Proceedings of the the Web Conference 2018, pp. 85–86 (2018)
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 346–353 (2019)
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This research was supported by NVIDIA Corporation.
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Taghibakhshi, A., Ma, M., Aithal, A., Yilmaz, O., Maron, H., West, M. (2023). Hierarchical Graph Neural Network with Cross-Attention for Cross-Device User Matching. In: Wrembel, R., Gamper, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2023. Lecture Notes in Computer Science, vol 14148. Springer, Cham. https://doi.org/10.1007/978-3-031-39831-5_28
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