Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIn
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915343945957376 |
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| author | He, Shihai Choi, Julie Li, Tianqi Ding, Zhiwei Du, Peng Bannur, Priya Liang, Franco Borisyuk, Fedor Jaikumar, Padmini Xue, Xiaobing Gupta, Viral |
| author_facet | He, Shihai Choi, Julie Li, Tianqi Ding, Zhiwei Du, Peng Bannur, Priya Liang, Franco Borisyuk, Fedor Jaikumar, Padmini Xue, Xiaobing Gupta, Viral |
| contents | Notification recommendation systems are critical to driving user engagement on professional platforms like LinkedIn. Designing such systems involves integrating heterogeneous signals across domains, capturing temporal dynamics, and optimizing for multiple, often competing, objectives. Graph Neural Networks (GNNs) provide a powerful framework for modeling complex interactions in such environments. In this paper, we present a cross-domain GNN-based system deployed at LinkedIn that unifies user, content, and activity signals into a single, large-scale graph. By training on this cross-domain structure, our model significantly outperforms single-domain baselines on key tasks, including click-through rate (CTR) prediction and professional engagement. We introduce architectural innovations including temporal modeling and multi-task learning, which further enhance performance. Deployed in LinkedIn's notification system, our approach led to a 0.10% lift in weekly active users and a 0.62% improvement in CTR. We detail our graph construction process, model design, training pipeline, and both offline and online evaluations. Our work demonstrates the scalability and effectiveness of cross-domain GNNs in real-world, high-impact applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12700 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIn He, Shihai Choi, Julie Li, Tianqi Ding, Zhiwei Du, Peng Bannur, Priya Liang, Franco Borisyuk, Fedor Jaikumar, Padmini Xue, Xiaobing Gupta, Viral Machine Learning 68R10 Notification recommendation systems are critical to driving user engagement on professional platforms like LinkedIn. Designing such systems involves integrating heterogeneous signals across domains, capturing temporal dynamics, and optimizing for multiple, often competing, objectives. Graph Neural Networks (GNNs) provide a powerful framework for modeling complex interactions in such environments. In this paper, we present a cross-domain GNN-based system deployed at LinkedIn that unifies user, content, and activity signals into a single, large-scale graph. By training on this cross-domain structure, our model significantly outperforms single-domain baselines on key tasks, including click-through rate (CTR) prediction and professional engagement. We introduce architectural innovations including temporal modeling and multi-task learning, which further enhance performance. Deployed in LinkedIn's notification system, our approach led to a 0.10% lift in weekly active users and a 0.62% improvement in CTR. We detail our graph construction process, model design, training pipeline, and both offline and online evaluations. Our work demonstrates the scalability and effectiveness of cross-domain GNNs in real-world, high-impact applications. |
| title | Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIn |
| topic | Machine Learning 68R10 |
| url | https://arxiv.org/abs/2506.12700 |