Intent Graphs: A Privacy-First Alternative to Identity-Based Advertising Systems A Session-Centric Graph Intelligence Framework for Post-Cookie Ad Targeting
Keywords:
intent graphs; graph neural networks; privacy-preserving advertising; session-based recommendation; post-cookie targetingAbstract
Digital advertising infrastructure is undergoing fundamental restructuring driven by browser-level tracking restrictions, evolving privacy regulation, and the accelerating decline of persistent identity systems. Advertising platforms that have historically depended on cross-session behavioral tracking now require architectures capable of inferring user intent dynamically without relying on stored identity history. This paper introduces Intent Graphs, a privacy-first behavioral intelligence framework that models user intent from session-level interaction sequences represented as temporal graphs. Behavioral events are encoded as graph nodes, with edges capturing temporal and semantic relationships between interactions. Graph neural network architectures process these structures to estimate latent intent states without requiring persistent identifiers or cross-session data retention. Experimental evaluation using a semi-synthetic dataset comprising 120 million sessions demonstrates that the framework achieves intent classification accuracy of 86.3 percent and delivers click-through rate performance approaching 91.2 percent of cookie-based targeting effectiveness in high-intent environments. End-to-end inference latency remains below 60 milliseconds, satisfying production deployment constraints at hyperscale. The framework aligns with General Data Protection Regulation data minimization principles and supports real-time serving at scale. Results suggest that session-centric behavioral graph intelligence represents a viable architectural alternative to identity-based advertising infrastructure in privacy-constrained operational environments.
Downloads
References
Hamilton, W. L., Ying, Z., and Leskovec, J., "Inductive Representation Learning on Large Graphs," Advances in Neural Information Processing Systems, Vol. 30, pp. 1024–1034, 2017.
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y., "Graph Attention Networks," International Conference on Learning Representations, pp. 1–12, 2018.
Kipf, T. N. and Welling, M., "Semi-Supervised Classification with Graph Convolutional Networks," International Conference on Learning Representations, pp. 1–14, 2017.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I., "Attention Is All You Need," Advances in Neural Information Processing Systems, Vol. 30, pp. 5998–6008, 2017.
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., and Tan, T., "Session-Based Recommendation with Graph Neural Networks," Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, pp. 346–353, 2019.
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and Aguera y Arcas, B., "Communication-Efficient Learning of Deep Networks from Decentralized Data", Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, pp. 1273–1282, 2017.
Lewis, R. A., and Rao, J. M., "The Unfavorable Economics of Measuring the Returns to Advertising", Quarterly Journal of Economics, Vol. 130, No. 4, pp. 1941–1973, 2015.
Kang, W. C. and McAuley, J., "Self-Attentive Sequential Recommendation," Proceedings of the 18th IEEE International Conference on Data Mining, pp. 197–206, 2018.
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., and Jiang, P., "BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer," Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441–1450, 2019.
Guo, H., Tang, R., Ye, Y., Li, Z., and He, X., "DeepFM: A Factorization-Machine Based Neural Network for CTR Prediction," Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 1725–1731, 2017.
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., and Ma, J., "Neural Attentive Session-Based Recommendation," Proceedings of the 26th ACM International Conference on Information and Knowledge Management, pp. 1419–1428, 2017.
Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., and Bronstein, M., "Temporal Graph Networks for Deep Learning on Dynamic Graphs," arXiv:2006.10637, pp. 1–24, 2020.
Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., Yan, Y., Jin, J., Li, H., and Gai, K., "Deep Interest Network for Click-Through Rate Prediction," Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1059–1068, 2018.
Chen, Q., Zhao, H., Li, W., Huang, P., and Ou, W., "Behavior Sequence Transformer for E-Commerce Recommendation in Alibaba." Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data at ACM KDD, pp. 1–4, 2019.
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y., "Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation," Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1724–1734, 2014.
European Parliament, "Regulation (EU) 2016/679: General Data Protection Regulation," Official Journal of the European Union, Vol. L 119, pp. 1–88, 2016.
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J., "Graph Convolutional Neural Networks for Web-Scale Recommender Systems," Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 974–983, 2018.
Tang, J. and Wang, K., "Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding", Proceedings of the 11th ACM International Conference on Web Search and Data Mining, pp. 565–573, 2018.
Wang, R., Fu, B., Fu, G., and Wang, M., "Deep and Cross Network for Ad Click Predictions", Proceedings of the ADKDD'17 Workshop at ACM KDD, pp. 1–7, 2017.
Konecny, J., McMahan, H. B., Ramage, D., and Richtarik, P., "Federated Optimization: Distributed Machine Learning for Mobile Devices," arXiv:1610.02527, pp. 1–16, 2016.
Pearl, J., Causality: Models, Reasoning, and Inference, Cambridge University Press, Cambridge, UK, 2009.
Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning, MIT Press, Cambridge, MA, 2016.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
