零参数推荐框架ZoRRO，600倍提速下接近顶尖模型效果。
ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation
- 零权重训练，直接部署无需调参
- 线上测试点击率接近先进模型，速度超600倍
- 揭示离线与线上性能差异，适合大规模新闻推荐
我们提出ZoRRO（零权重个性化推荐系统），一种无需训练、零参数的个性化新闻推荐框架，专为可扩展的现实部署设计。ZoRRO在离线排序评估中优于强基准神经模型，在线上A/B测试中点击率表现几乎与最先进的深度学习模型相当，同时运行速度超过600倍。实验揭示了离线与线上性能间的差距，并表明点击率相近的模型可能产生显著不同的推荐分布，从而影响整体新闻流。这些发现使ZoRRO成为大规模新闻推荐的实用高效方案，并强调评估推荐系统需超越准确率指标。
原文摘要 · Abstract (English)
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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