Google 提出 SDF 框架，分解推荐系统过时问题
原标题：Decomposing Staleness in Recommender Systems: A Dual-Filter Framework for Supersession and Decay
AI 摘要
Google 在 CIKM 2026 上介绍了 SDF（Supersession-Decay Filtering）系统，用于解决推荐系统中的内容过时问题。该系统部署于 Google Discover，通过关系过时模型和预测流量比模型分别处理内容被取代和自然衰减，并在排序前过滤过时内容。上线两年后，用户过时反馈减少了 54.9%，同时提升了用户参与度并降低了服务成本。
正文节选
Decomposing Staleness in Recommender Systems: A Dual-Filter Framework for Supersession and DecayConference: Proceedings of the 35th ACM International Conference on Information and Knowledge Management; November 7–11, 2026; Rome, Italy.Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM ’26), November 7–11, 2026, Rome, ItalyDOI: 10.1145/3799682.3840082ISBN: 979-8-4007-2539-5/2026/11CCS: Information systems Recommender systemsCCS: Information systems