{"ID":6536291,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10910","arxiv_id":"2607.10910","title":"ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation","abstract":"We present ZoRRO (Zero-Weight Personalized Recommender System), a zero-weight, training-free framework for personalized news recommendation designed for scalable real-world deployment. ZoRRO outperforms strong neural baselines in offline ranking evaluations and achieves click-through rate performance in online A/B testing that is nearly on par with a state-of-the-art deep learning model, while operating more than 600 times faster. Our experiments reveal gaps between offline and online performance and demonstrate that models with similar click-through rate outcomes can produce markedly different recommendation distributions, thereby influencing the overall news flow. These findings position ZoRRO as a practical and efficient solution for large-scale news recommendation and highlight the importance of evaluating recommender systems using metrics beyond accuracy alone.","short_abstract":"We present ZoRRO (Zero-Weight Personalized Recommender System), a zero-weight, training-free framework for personalized news recommendation designed for scalable real-world deployment. ZoRRO outperforms strong neural baselines in offline ranking evaluations and achieves click-through rate performance in online A/B test...","url_abs":"https://arxiv.org/abs/2607.10910","url_pdf":"https://arxiv.org/pdf/2607.10910v1","authors":"[\"Johannes Kruse\",\"Ryotaro Shimizu\",\"Kasper Lindskow\",\"Jon Tofteskov\",\"Michael Riis Andersen\",\"Julian McAuley\",\"Jes Frellsen\"]","published":"2026-07-12T20:30:24Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.LG\"]","methods":"[]","has_code":false}