用跨域图神经网络提升领英通知推荐效果，显著增加用户活跃和点击率。
Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIn
- 构建跨用户、内容、行为的统一大图，融合多源异构信号
- 在真实系统中实现0.62%点击率提升和0.10%周活增长
- 适合大规模推荐系统研发人员参考落地
通知推荐系统对专业平台如领英的用户参与度至关重要。设计此类系统需整合跨域异构信号、捕捉时间动态，并平衡多个常冲突的目标。图神经网络（GNN）为建模复杂交互提供了强大框架。本文介绍一个部署于领英的跨域GNN推荐系统，将用户、内容与行为信号统一为大规模图结构。通过在该跨域结构上训练，模型在点击率（CTR）预测与职业互动等关键任务上显著优于单域基线。我们引入时间建模与多任务学习等架构创新，进一步提升性能。该方案已上线领英通知系统，带来0.10%的周活跃用户提升和0.62%的点击率改进。本文详述图构建、模型设计、训练流程及离线/在线评估。结果表明，跨域GNN在真实高影响力场景中具备可扩展性与有效性。
原文摘要 · Abstract (English)
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.
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