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<!doctype html>
<html lang="en">
<head>
  <meta charset="utf-8" />
  <meta name="viewport" content="width=device-width, initial-scale=1" />
  <title>NewsReX — Pre-trained News Recommendation Models</title>
  <link rel="stylesheet" href="style.css" />
</head>
<body>
  <div class="container">
    <header>
      <h1>NewsReX</h1>
      <p class="subtitle">Pre-trained News Recommendation Models</p>
      <div class="badges">
        <a href="https://arxiv.org/abs/2508.21572"><img src="https://img.shields.io/badge/arXiv-2508.21572-b31b1b.svg" alt="arXiv"></a>
        <a href="https://github.com/igor17400/NewsReX"><img src="https://img.shields.io/badge/GitHub-NewsReX-blue.svg" alt="GitHub"></a>
        <a href="https://www.python.org/downloads/release/python-3120/"><img src="https://img.shields.io/badge/python-3.12+-blue.svg" alt="Python 3.12+"></a>
      </div>
    </header>

    <section>
      <p>This organization hosts pre-trained weights for <strong>10 neural news recommendation models</strong> trained on the <a href="https://msnews.github.io/">MIND-small</a> dataset using the <a href="https://github.com/igor17400/NewsReX">NewsReX</a> framework. All models are trained with 3 random seeds (42, 123, 456) and evaluated on the standard MIND test split.</p>
    </section>

    <section>
      <h2>Benchmark Results (MIND-small, mean &pm; std over 3 seeds)</h2>

      <h3>JAX Models</h3>
      <div class="table-wrapper">
        <table>
          <thead>
            <tr><th>Model</th><th>AUC</th><th>MRR</th><th>NDCG@5</th><th>NDCG@10</th><th>Weights</th></tr>
          </thead>
          <tbody>
            <tr><td><strong>CROWN</strong></td><td>0.6778&pm;0.0030</td><td>0.3246&pm;0.0018</td><td>0.3619&pm;0.0022</td><td>0.4233&pm;0.0022</td><td><a href="https://huggingface.co/newsrex/CROWN-JAX-MIND-small">Download</a></td></tr>
            <tr><td><strong>DIGAT</strong></td><td>0.6760&pm;0.0021</td><td>0.3245&pm;0.0021</td><td>0.3594&pm;0.0035</td><td>0.4220&pm;0.0027</td><td><a href="https://huggingface.co/newsrex/DIGAT-JAX-MIND-small">Download</a></td></tr>
            <tr><td><strong>CAUM</strong></td><td>0.6734&pm;0.0013</td><td>0.3202&pm;0.0009</td><td>0.3546&pm;0.0009</td><td>0.4185&pm;0.0006</td><td><a href="https://huggingface.co/newsrex/CAUM-JAX-MIND-small">Download</a></td></tr>
            <tr><td><strong>TCCM</strong></td><td>0.6734&pm;0.0055</td><td>0.3208&pm;0.0034</td><td>0.3574&pm;0.0046</td><td>0.4194&pm;0.0043</td><td><a href="https://huggingface.co/newsrex/TCCM-JAX-MIND-small">Download</a></td></tr>
            <tr><td><strong>PP-Rec</strong></td><td>0.6676&pm;0.0040</td><td>0.3182&pm;0.0033</td><td>0.3544&pm;0.0041</td><td>0.4164&pm;0.0036</td><td><a href="https://huggingface.co/newsrex/PPREC-JAX-MIND-small">Download</a></td></tr>
            <tr><td><strong>LSTUR</strong></td><td>0.6672&pm;0.0020</td><td>0.3177&pm;0.0033</td><td>0.3525&pm;0.0037</td><td>0.4156&pm;0.0033</td><td><a href="https://huggingface.co/newsrex/LSTUR-JAX-MIND-small">Download</a></td></tr>
            <tr><td><strong>NAML</strong></td><td>0.6639&pm;0.0014</td><td>0.3130&pm;0.0022</td><td>0.3456&pm;0.0033</td><td>0.4097&pm;0.0025</td><td><a href="https://huggingface.co/newsrex/NAML-JAX-MIND-small">Download</a></td></tr>
            <tr><td><strong>GLORY</strong></td><td>0.6624&pm;0.0030</td><td>0.3152&pm;0.0038</td><td>0.3483&pm;0.0041</td><td>0.4119&pm;0.0040</td><td><a href="https://huggingface.co/newsrex/GLORY-JAX-MIND-small">Download</a></td></tr>
            <tr><td><strong>MINER</strong></td><td>0.6579&pm;0.0024</td><td>0.3117&pm;0.0027</td><td>0.3444&pm;0.0035</td><td>0.4080&pm;0.0025</td><td><a href="https://huggingface.co/newsrex/MINER-JAX-MIND-small">Download</a></td></tr>
            <tr><td><strong>NRMS</strong></td><td>0.6561&pm;0.0006</td><td>0.3075&pm;0.0008</td><td>0.3394&pm;0.0003</td><td>0.4039&pm;0.0007</td><td><a href="https://huggingface.co/newsrex/NRMS-JAX-MIND-small">Download</a></td></tr>
          </tbody>
        </table>
      </div>

      <h3>PyTorch Models</h3>
      <div class="table-wrapper">
        <table>
          <thead>
            <tr><th>Model</th><th>AUC</th><th>MRR</th><th>NDCG@5</th><th>NDCG@10</th><th>Weights</th></tr>
          </thead>
          <tbody>
            <tr><td><strong>CROWN</strong></td><td>0.6705&pm;0.0045</td><td>0.3183&pm;0.0049</td><td>0.3553&pm;0.0056</td><td>0.4173&pm;0.0056</td><td><a href="https://huggingface.co/newsrex/CROWN-PYTORCH-MIND-small">Download</a></td></tr>
            <tr><td><strong>CAUM</strong></td><td>0.6656&pm;0.0053</td><td>0.3176&pm;0.0028</td><td>0.3504&pm;0.0040</td><td>0.4149&pm;0.0035</td><td><a href="https://huggingface.co/newsrex/CAUM-PYTORCH-MIND-small">Download</a></td></tr>
            <tr><td><strong>NAML</strong></td><td>0.6654&pm;0.0015</td><td>0.3105&pm;0.0009</td><td>0.3464&pm;0.0027</td><td>0.4097&pm;0.0018</td><td><a href="https://huggingface.co/newsrex/NAML-PYTORCH-MIND-small">Download</a></td></tr>
            <tr><td><strong>PP-Rec</strong></td><td>0.6631&pm;0.0044</td><td>0.3130&pm;0.0024</td><td>0.3487&pm;0.0041</td><td>0.4111&pm;0.0033</td><td><a href="https://huggingface.co/newsrex/PPREC-PYTORCH-MIND-small">Download</a></td></tr>
            <tr><td><strong>TCCM</strong></td><td>0.6616&pm;0.0019</td><td>0.3088&pm;0.0022</td><td>0.3428&pm;0.0031</td><td>0.4057&pm;0.0024</td><td><a href="https://huggingface.co/newsrex/TCCM-PYTORCH-MIND-small">Download</a></td></tr>
            <tr><td><strong>NRMS</strong></td><td>0.6534&pm;0.0025</td><td>0.3052&pm;0.0021</td><td>0.3367&pm;0.0019</td><td>0.4017&pm;0.0022</td><td><a href="https://huggingface.co/newsrex/NRMS-PYTORCH-MIND-small">Download</a></td></tr>
            <tr><td><strong>LSTUR</strong></td><td>&mdash;</td><td>&mdash;</td><td>&mdash;</td><td>&mdash;</td><td><a href="https://huggingface.co/newsrex/LSTUR-PYTORCH-MIND-small">Download</a></td></tr>
            <tr><td><strong>DIGAT</strong></td><td>&mdash;</td><td>&mdash;</td><td>&mdash;</td><td>&mdash;</td><td><a href="https://huggingface.co/newsrex/DIGAT-PYTORCH-MIND-small">Download</a></td></tr>
            <tr><td><strong>GLORY</strong></td><td>&mdash;</td><td>&mdash;</td><td>&mdash;</td><td>&mdash;</td><td><a href="https://huggingface.co/newsrex/GLORY-PYTORCH-MIND-small">Download</a></td></tr>
          </tbody>
        </table>
      </div>
    </section>

    <section>
      <h2>Supported Models</h2>
      <div class="table-wrapper">
        <table>
          <thead>
            <tr><th>Model</th><th>Paper</th><th>Venue</th></tr>
          </thead>
          <tbody>
            <tr><td>NRMS</td><td>Neural News Recommendation with Multi-Head Self-Attention</td><td>EMNLP 2019</td></tr>
            <tr><td>NAML</td><td>Neural News Recommendation with Attentive Multi-View Learning</td><td>EMNLP 2019</td></tr>
            <tr><td>LSTUR</td><td>Neural News Recommendation with Long- and Short-term User Representations</td><td>ACL 2019</td></tr>
            <tr><td>CROWN</td><td>Intent Disentanglement and Feature Self-Supervision for News Recommendation</td><td>WWW 2025</td></tr>
            <tr><td>PP-Rec</td><td>News Recommendation with Personalized User Interest and Popularity Deconfounding</td><td>ACL 2021</td></tr>
            <tr><td>DIGAT</td><td>Dual Interactive Graph Attention Networks for News Recommendation</td><td>EMNLP 2022</td></tr>
            <tr><td>GLORY</td><td>Global-Local News Recommendation via Multi-Channel Graph Modeling</td><td>NAACL 2024</td></tr>
            <tr><td>MINER</td><td>Multi-Interest News Extraction and Recommendation</td><td>EMNLP 2022</td></tr>
            <tr><td>CAUM</td><td>Candidate-Aware User Modeling for News Recommendation</td><td>RecSys 2023</td></tr>
            <tr><td>TCCM</td><td>Topic-Centric Conversational Collaborative Model for News Recommendation</td><td>CIKM 2022</td></tr>
          </tbody>
        </table>
      </div>
    </section>

    <section>
      <h2>Quick Start</h2>
      <pre><code>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</code></pre>
    </section>

    <section>
      <h2>Repository Structure</h2>
      <pre><code>newsrex/{MODEL}-{FRAMEWORK}-MIND-small/
├── model.safetensors              &lt;- 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</code></pre>
    </section>

    <section>
      <h2>Citation</h2>
      <pre><code>@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},
}</code></pre>
    </section>

    <footer>
      <p><strong>Authors:</strong> Igor L.R. Azevedo (U. Tokyo) &middot; Toyotaro Suzumura (U. Tokyo) &middot; Yuichiro Yasui (Nikkei Inc.)</p>
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