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arXiv · 2607.10910

ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation

Johannes Kruse·Ryotaro Shimizu·Kasper Lindskow·Jon Tofteskov·Michael Riis Andersen·Julian McAuley·Jes Frellsen
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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.

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Johannes Kruse, Ryotaro Shimizu, Kasper Lindskow, Jon Tofteskov, Michael Riis Andersen, Julian McAuley, Jes Frellsen. 2026-07-12. ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation. https://doi.org/10.1145/3805712.3809909

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