Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIn

TL;DR

This paper presents a large-scale, cross-domain graph neural network system deployed at LinkedIn to improve notification recommendations, significantly enhancing user engagement metrics through architectural innovations and real-world deployment.

Contribution

The paper introduces a novel cross-domain GNN architecture with temporal and multi-task capabilities, specifically designed for large-scale, real-world notification systems at LinkedIn.

Findings

Achieved a 0.10% lift in weekly active users.

Improved CTR by 0.62%.

Demonstrated scalability and effectiveness in production environment.

Abstract

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.