SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908454987235328 |
|---|---|
| author | Zhang, Weizhi Yang, Liangwei Song, Zihe Zou, Henrry Peng Xu, Ke Zhu, Yuanjie Yu, Philip S. |
| author_facet | Zhang, Weizhi Yang, Liangwei Song, Zihe Zou, Henrry Peng Xu, Ke Zhu, Yuanjie Yu, Philip S. |
| contents | Recommender systems (RecSys) are essential for online platforms, providing personalized suggestions to users within a vast sea of information. Self-supervised graph learning seeks to harness high-order collaborative filtering signals through unsupervised augmentation on the user-item bipartite graph, primarily leveraging a multi-task learning framework that includes both supervised recommendation loss and self-supervised contrastive loss. However, this separate design introduces additional graph convolution processes and creates inconsistencies in gradient directions due to disparate losses, resulting in prolonged training times and sub-optimal performance. In this study, we introduce a unified framework of Supervised Graph Contrastive Learning for recommendation (SGCL) to address these issues. SGCL uniquely combines the training of recommendation and unsupervised contrastive losses into a cohesive supervised contrastive learning loss, aligning both tasks within a single optimization direction for exceptionally fast training. Extensive experiments on three real-world datasets show that SGCL outperforms state-of-the-art methods, achieving superior accuracy and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13336 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation Zhang, Weizhi Yang, Liangwei Song, Zihe Zou, Henrry Peng Xu, Ke Zhu, Yuanjie Yu, Philip S. Information Retrieval Recommender systems (RecSys) are essential for online platforms, providing personalized suggestions to users within a vast sea of information. Self-supervised graph learning seeks to harness high-order collaborative filtering signals through unsupervised augmentation on the user-item bipartite graph, primarily leveraging a multi-task learning framework that includes both supervised recommendation loss and self-supervised contrastive loss. However, this separate design introduces additional graph convolution processes and creates inconsistencies in gradient directions due to disparate losses, resulting in prolonged training times and sub-optimal performance. In this study, we introduce a unified framework of Supervised Graph Contrastive Learning for recommendation (SGCL) to address these issues. SGCL uniquely combines the training of recommendation and unsupervised contrastive losses into a cohesive supervised contrastive learning loss, aligning both tasks within a single optimization direction for exceptionally fast training. Extensive experiments on three real-world datasets show that SGCL outperforms state-of-the-art methods, achieving superior accuracy and efficiency. |
| title | SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2507.13336 |