Self-Supervised Social Recommendation Algorithm Fusing Residual Networks
Social recommendation based on graph neural networks learns the embedded relationships between users and items through the information of social graphs and interaction graphs to get the final recommendation results. However, the existing algorithms mainly utilize the static social graph structure, w...
Saved in:
| Main Author: | WANG Yujie, YANG Zhe |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2024-12-01
|
| Series: | Jisuanji kexue yu tansuo |
| Subjects: | |
| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2401006.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Self-Supervised Enhancement Method for Multi-Behavior Session-Based Recommendation
by: Zhen Zhang, et al.
Published: (2024-01-01) -
Improving healthy food recommender systems through heterogeneous hypergraph learning
by: Jing Wang, et al.
Published: (2024-12-01) -
Hypergraph User Embeddings and Session Contrastive Learning for POI Recommendation
by: Yan Zhang, et al.
Published: (2025-01-01) -
Position-Awareness and Hypergraph Contrastive Learning for Multi-Behavior Sequence Recommendation
by: Sitong Yan, et al.
Published: (2024-01-01) -
SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative Filtering
by: Heng Lu, et al.
Published: (2024-12-01)