![[2511.20564] E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systems](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2511.20564] E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systems
Summary
The paper presents E2E-GRec, a novel end-to-end framework that integrates Graph Neural Networks (GNNs) with recommender systems, addressing limitations of traditional two-stage approaches.
Why It Matters
This research is significant as it proposes a unified training approach that enhances the efficiency and effectiveness of GNNs in recommendation tasks, potentially leading to improved user engagement and satisfaction in various applications.
Key Takeaways
- E2E-GRec integrates GNN training directly with recommender systems to improve performance.
- The framework employs efficient subgraph sampling for scalability.
- A Graph Feature Auto-Encoder (GFAE) aids in learning meaningful embeddings.
- Dynamic loss balancing enhances multi-task training stability.
- Extensive evaluations show significant improvements over traditional methods.
Computer Science > Machine Learning arXiv:2511.20564 (cs) [Submitted on 25 Nov 2025 (v1), last revised 22 Feb 2026 (this version, v2)] Title:E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systems Authors:Rui Xue, Shichao Zhu, Liang Qin, Tianfu Wu View a PDF of the paper titled E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systems, by Rui Xue and 3 other authors View PDF HTML (experimental) Abstract:Graph Neural Networks (GNNs) have emerged as powerful tools for modeling graph-structured data and have been widely used in recommender systems, such as for capturing complex user-item and item-item relations. However, most industrial deployments adopt a two-stage pipeline: GNNs are first pre-trained offline to generate node embeddings, which are then used as static features for downstream recommender systems. This decoupled paradigm leads to two key limitations: (1) high computational overhead, since large-scale GNN inference must be repeatedly executed to refresh embeddings; and (2) lack of joint optimization, as the gradient from the recommender system cannot directly influence the GNN learning process, causing the GNN to be suboptimally informative for the recommendation task. In this paper, we propose E2E-GRec, a novel end-to-end training framework that unifies GNN training with the recommender system. Our framework is characterized by three key components: (i) efficient subgraph sampli...
