"ForwardRec": Aggregate, Optimize, and Forward: A Forward-Forward Algorithm Framework for Graph-Based Recommendation
ForwardRec, a novel graph recommendation framework based on the Forward-Forward (FF) algorithm built on the SELFRec, introduces two core components. Forward Learning: ForwardRec tackles semantic and frequency issues by disentangling each layer independently; Hierarchical Rejection Sampling (HRS): To provide precise and comprehensive semantics for optimization, HRS conducts a layer-specific hierarchical negative sampling.
numba==0.53.1
numpy==1.20.3
scipy==1.6.2
tensorflow==1.14.0
torch>=1.7.0
- Configure the xx.conf file in the directory named conf. (xx is the name of the model you want to run)
- Run main.py and choose the model you want to run.
| Model | Paper | Type | Code |
|---|---|---|---|
| SASRec | Kang et al. Self-Attentive Sequential Recommendation, ICDM'18. | Sequential | PyTorch |
| CL4SRec | Xie et al. Contrastive Learning for Sequential Recommendation, ICDE'22. | Sequential | PyTorch |
| BERT4Rec | Sun et al. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer, CIKM'19. | Sequential | PyTorch |
If you find this repo helpful to your research, please cite our paper and the base framework from Yu.
@article{chen2026tri,
title={Towards a Tri-View Diffusion Framework for Recommendation},
author={Chen, Ximing and Lei, Pui Ieng and Sheng, Yijun and Liu, Yanyan and Gong, Zhiguo},
journal={The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
year={2026},
publisher={ACM}
}
@article{yu2023self,
title={Self-supervised learning for recommender systems: A survey},
author={Yu, Junliang and Yin, Hongzhi and Xia, Xin and Chen, Tong and Li, Jundong and Huang, Zi},
journal={IEEE Transactions on Knowledge and Data Engineering},
year={2023},
publisher={IEEE}
}