ZoRRO 不用训练就能做新闻推荐，速度比深度学习模型快 600 多倍，点击率还差不多，特别适合大规模部署。
ZoRRO 是一种零权重、无需训练的个人化新闻推荐框架，专门面向规模化部署设计。在离线排名评估中，ZoRRO 优于多种强神经基线模型。在线 A/B 测试中，其点击率接近最先进的深度学习模型，但速度快超过 600 倍。实验还揭示了离线与在线性能之间的差距，以及点击率相近的模型可能产生截然不同的推荐分布，进而影响整体新闻流。这些结果将 ZoRRO 定位为大规模新闻推荐的实用高效方案。
ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation
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.