Zenith通过优化特征处理实现百亿级直播推荐的高效精准排序
Zenith: Scaling up Ranking Models for Billion-scale Livestreaming Recommendation
- 用令牌融合与增强模块处理高维特征，提升模型扩展性
- 在TikTok Live实测中，点击率AUC提升1.05%，观看时长增8.11%
- 适合大规模实时推荐系统，尤其擅长处理高维稀疏特征
准确捕捉特征交互对推荐系统至关重要，近期趋势表明扩大模型容量是提升预测性能的关键。尽管已有研究探索多种架构以捕捉多粒度特征交互，但对高效特征处理及在不增加推理延迟的前提下扩展模型容量的关注仍不足。本文提出Zenith，一种可扩展且高效的排名架构，能以极低运行开销学习复杂特征交互。该架构通过令牌融合与令牌增强模块处理少量高维主令牌，因提升了令牌异质性，展现出优于现有顶尖方法的优越扩展规律。其实际效果已在全球用户达数十亿的领先直播平台TikTok Live中验证：A/B测试显示，在线CTR AUC提升+1.05%/-1.10%，Logloss改善，质量观看会话/用户提升+9.93%，质量观看时长/用户提升+8.11%。
原文摘要 · Abstract (English)
Accurately capturing feature interactions is essential in recommender systems, and recent trends show that scaling up model capacity could be a key driver for next-level predictive performance. While prior work has explored various model architectures to capture multi-granularity feature interactions, relatively little attention has been paid to efficient feature handling and scaling model capacity without incurring excessive inference latency. In this paper, we address this by presenting Zenith, a scalable and efficient ranking architecture that learns complex feature interactions with minimal runtime overhead. Zenith is designed to handle a few high-dimensional Prime Tokens with Token Fusion and Token Boost modules, which exhibits superior scaling laws compared to other state-of-the-art ranking methods, thanks to its improved token heterogeneity. Its real-world effectiveness is demonstrated by deploying the architecture to TikTok Live, a leading online livestreaming platform that attracts billions of users globally. Our A/B test shows that Zenith achieves +1.05%/-1.10% in online CTR AUC and Logloss, and realizes +9.93% gains in Quality Watch Session / User and +8.11% in Quality Watch Duration / User.
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