--- title: NewsReX emoji: 📰 colorFrom: blue colorTo: purple sdk: static pinned: false --- # NewsReX - News Recommendation Model Zoo A modular, multi-framework benchmark for neural news recommendation research. All models are implemented in **JAX/Flax**, **PyTorch**, and **Keras 3**, trained and evaluated under identical conditions. ## Implemented Models | Model | Paper | Venue | JAX | PyTorch | Keras | |-------|-------|-------|-----|---------|-------| | NRMS | [Wu et al.](https://aclanthology.org/D19-1671/) | EMNLP 2019 | ✅ | ✅ | ✅ | | NAML | [Wu et al.](https://www.ijcai.org/proceedings/2019/536) | IJCAI 2019 | ✅ | ✅ | ✅ | | LSTUR | [An et al.](https://aclanthology.org/P19-1033/) | ACL 2019 | ✅ | ✅ | ✅ | | MINER | [Li et al.](https://aclanthology.org/2022.findings-acl.29/) | ACL Findings 2022 | ✅ | ✅ | ✅ | | PP-Rec | [Qi et al.](https://aclanthology.org/2021.acl-long.34/) | ACL 2021 | ✅ | ✅ | ✅ | | CAUM | [Qi et al.](https://dl.acm.org/doi/10.1145/3477495.3532040) | SIGIR 2022 | ✅ | ✅ | ✅ | | CROWN | [Ko et al.](https://dl.acm.org/doi/10.1145/3696410.3714774) | WWW 2025 | ✅ | ✅ | ✅ | | DIGAT | [Mao et al.](https://aclanthology.org/2022.findings-emnlp.227/) | EMNLP Findings 2022 | ✅ | ✅ | ✅ | | GLORY | [Yang et al.](https://dl.acm.org/doi/10.1145/3604915.3608788) | RecSys 2023 | ✅ | ✅ | ✅ | | TCCM | [Qi et al.](https://dl.acm.org/doi/10.1145/3583780.3615186) | CIKM 2023 | ✅ | ✅ | ✅ | ## Quick Start ```bash # Install git clone https://github.com/igor17400/NewsReX.git cd NewsReX uv sync --extra all-cuda # Train NRMS on MIND-small uv run python src/train.py experiment=mind/nrms framework=jax # Load pretrained weights from HuggingFace uv run python src/train.py experiment=mind/nrms framework=jax \ weights=hf://newsrex/NRMS-JAX-MIND-small/model.safetensors ``` ## Repository **GitHub**: [github.com/igor17400/NewsReX](https://github.com/igor17400/NewsReX) ## Citation If you use NewsReX in your research, please cite: ```bibtex @misc{azevedo2025newsrexefficientapproachnews, title={NewsReX: A More Efficient Approach to News Recommendation with Keras 3 and JAX}, author={Igor L. R. Azevedo and Toyotaro Suzumura and Yuichiro Yasui}, year={2025}, eprint={2508.21572}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2508.21572}, } ```