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<p>This study investigates a nudging-based news recommender designed to broaden exposure to political articles while preserving user satisfaction. We built a hybrid transformer-based recommender system that combines content-based embeddings (via a custom transformer architecture) with click-behavior signals to refine user profiles. A dedicated profile extension module augments each profile with semantically related concepts, subtly steering recommendations toward political news according to user-existing interests. In an online experiment with 168 participants, our nudging system significantly outperformed a popularity-based baseline (satisfaction: +12.84%, political clicks: +15.35%) and an interest-based baseline (satisfaction: +6.96%, political clicks: +6.44%). Notably, participants with the lowest initial political interest exhibited the largest engagement gains (clicks: +64.54% over popularity, +22.75% over interest) without compromising user satisfaction.</p>

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Hybrid transformer-based recommender system for political news

  • 期刊论文

  • 发表日期：

    2025年6月2日

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  • Stefaan Vercoutere,
  • Toon De Pessemier,
  • Luc Martens

摘要

This study investigates a nudging-based news recommender designed to broaden exposure to political articles while preserving user satisfaction. We built a hybrid transformer-based recommender system that combines content-based embeddings (via a custom transformer architecture) with click-behavior signals to refine user profiles. A dedicated profile extension module augments each profile with semantically related concepts, subtly steering recommendations toward political news according to user-existing interests. In an online experiment with 168 participants, our nudging system significantly outperformed a popularity-based baseline (satisfaction: +12.84%, political clicks: +15.35%) and an interest-based baseline (satisfaction: +6.96%, political clicks: +6.44%). Notably, participants with the lowest initial political interest exhibited the largest engagement gains (clicks: +64.54% over popularity, +22.75% over interest) without compromising user satisfaction.