This organization hosts pre-trained weights for 10 neural news recommendation models trained on the MIND-small dataset using the NewsReX framework. All models are trained with 3 random seeds (42, 123, 456) and evaluated on the standard MIND test split.
Benchmark Results (MIND-small, mean ± std over 3 seeds)
JAX Models
| Model | AUC | MRR | NDCG@5 | NDCG@10 | Weights |
|---|
| CROWN | 0.6778±0.0030 | 0.3246±0.0018 | 0.3619±0.0022 | 0.4233±0.0022 | Download |
| DIGAT | 0.6760±0.0021 | 0.3245±0.0021 | 0.3594±0.0035 | 0.4220±0.0027 | Download |
| CAUM | 0.6734±0.0013 | 0.3202±0.0009 | 0.3546±0.0009 | 0.4185±0.0006 | Download |
| TCCM | 0.6734±0.0055 | 0.3208±0.0034 | 0.3574±0.0046 | 0.4194±0.0043 | Download |
| PP-Rec | 0.6676±0.0040 | 0.3182±0.0033 | 0.3544±0.0041 | 0.4164±0.0036 | Download |
| LSTUR | 0.6672±0.0020 | 0.3177±0.0033 | 0.3525±0.0037 | 0.4156±0.0033 | Download |
| NAML | 0.6639±0.0014 | 0.3130±0.0022 | 0.3456±0.0033 | 0.4097±0.0025 | Download |
| GLORY | 0.6624±0.0030 | 0.3152±0.0038 | 0.3483±0.0041 | 0.4119±0.0040 | Download |
| MINER | 0.6579±0.0024 | 0.3117±0.0027 | 0.3444±0.0035 | 0.4080±0.0025 | Download |
| NRMS | 0.6561±0.0006 | 0.3075±0.0008 | 0.3394±0.0003 | 0.4039±0.0007 | Download |
PyTorch Models
| Model | AUC | MRR | NDCG@5 | NDCG@10 | Weights |
|---|
| CROWN | 0.6705±0.0045 | 0.3183±0.0049 | 0.3553±0.0056 | 0.4173±0.0056 | Download |
| CAUM | 0.6656±0.0053 | 0.3176±0.0028 | 0.3504±0.0040 | 0.4149±0.0035 | Download |
| NAML | 0.6654±0.0015 | 0.3105±0.0009 | 0.3464±0.0027 | 0.4097±0.0018 | Download |
| PP-Rec | 0.6631±0.0044 | 0.3130±0.0024 | 0.3487±0.0041 | 0.4111±0.0033 | Download |
| TCCM | 0.6616±0.0019 | 0.3088±0.0022 | 0.3428±0.0031 | 0.4057±0.0024 | Download |
| NRMS | 0.6534±0.0025 | 0.3052±0.0021 | 0.3367±0.0019 | 0.4017±0.0022 | Download |
| LSTUR | — | — | — | — | Download |
| DIGAT | — | — | — | — | Download |
| GLORY | — | — | — | — | Download |
Supported Models
| Model | Paper | Venue |
|---|
| NRMS | Neural News Recommendation with Multi-Head Self-Attention | EMNLP 2019 |
| NAML | Neural News Recommendation with Attentive Multi-View Learning | EMNLP 2019 |
| LSTUR | Neural News Recommendation with Long- and Short-term User Representations | ACL 2019 |
| CROWN | Intent Disentanglement and Feature Self-Supervision for News Recommendation | WWW 2025 |
| PP-Rec | News Recommendation with Personalized User Interest and Popularity Deconfounding | ACL 2021 |
| DIGAT | Dual Interactive Graph Attention Networks for News Recommendation | EMNLP 2022 |
| GLORY | Global-Local News Recommendation via Multi-Channel Graph Modeling | NAACL 2024 |
| MINER | Multi-Interest News Extraction and Recommendation | EMNLP 2022 |
| CAUM | Candidate-Aware User Modeling for News Recommendation | RecSys 2023 |
| TCCM | Topic-Centric Conversational Collaborative Model for News Recommendation | CIKM 2022 |
Quick Start
git clone https://github.com/igor17400/NewsReX.git
cd NewsReX && uv sync --extra jax
# Evaluate a pre-trained model
uv run python src/train.py experiment=mind/nrms framework=jax \
weights=hf://newsrex/NRMS-JAX-MIND-small/model.safetensors
# Train from scratch (3 seeds)
uv run python src/train.py experiment=mind/nrms framework=jax \
multi_seed.enabled=true
Repository Structure
newsrex/{MODEL}-{FRAMEWORK}-MIND-small/
├── model.safetensors <- best seed (default download)
├── test_results.json
├── training_run_summary.json
├── seed_42/model.safetensors
├── seed_123/model.safetensors
├── seed_456/model.safetensors
└── README.md
Citation
@misc{azevedo2025newsrex,
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},
}
