通过本地门控与排序框架，提升弱网下视频播放流畅度。
Enhancing Playback Performance in Video Recommender Systems with an On-Device Gating and Ranking Framework
- 在设备端实时识别可能卡顿的视频，动态替换为缓存优选内容。
- 部署于全球数亿用户平台，显著改善播放成功率与用户留存率。
- 适合关注用户体验与系统鲁棒性的推荐系统研发者。
近年来，视频推荐系统受到广泛关注。现有主流系统主要优化用户与内容之间的匹配，但用户在浏览视频时频繁遭遇加载缓慢或卡顿问题，尤其在弱网络环境下，严重影响浏览体验，甚至导致用户流失，即便内容推荐质量优异。这一问题严重却常被忽视。为此，我们提出一种基于设备端的门控与排序框架（GRF），与服务端推荐系统协同工作。具体而言，利用门控模型实时识别可能产生播放问题的视频，并通过排序模型从本地缓存池中选出最优替代视频。该方案已全面部署于全球数亿用户的短视频平台快手（Kwai）。实验表明，该方法显著提升了视频播放性能，有效改善了整体用户体验与用户留存率。
原文摘要 · Abstract (English)
Video recommender systems (RSs) have gained increasing attention in recent years. Existing mainstream RSs focus on optimizing the matching function between users and items. However, we noticed that users frequently encounter playback issues such as slow loading or stuttering while browsing the videos, especially in weak network conditions, which will lead to a subpar browsing experience, and may cause users to leave, even when the video content and recommendations are superior. It is quite a serious issue, yet easily overlooked. To tackle this issue, we propose an on-device Gating and Ranking Framework (GRF) that cooperates with server-side RS. Specifically, we utilize a gate model to identify videos that may have playback issues in real-time, and then we employ a ranking model to select the optimal result from a locally-cached pool to replace the stuttering videos. Our solution has been fully deployed on Kwai, a large-scale short video platform with hundreds of millions of users globally. Moreover, it significantly enhances video playback performance and improves overall user experience and retention rates.
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