Google 在 Google Discover 上部署了 SDF 系统，专门解决推荐内容变旧的问题。它用两个模型识别陈旧内容，让用户投诉下降了54.9%。
Google Discover等大型内容平台面临陈旧推荐问题，主要源于内容替代和相关性衰减两种机制。Google 推出 SDF 系统，用于解决推荐内容的陈旧性问题。SDF 通过关系陈旧性模型和预测流量比率（PTR）模型两个过滤器来识别陈旧内容。在为期两年的生产部署中，用户提交的陈旧性报告相比基线下降了54.9%。
Decomposing Staleness in Recommender Systems: A Dual-Filter Framework for Supersession and Decay
Stale recommendations are a pervasive challenge and a leading source of user complaints on large-scale content platforms. Items lose relevance through two primary mechanisms: supersession, where emerging updates render prior coverage stale, and relevance decay, where an item's informational value naturally diminishes over its lifecycle. Traditional countermeasures serve as crude proxies: age cutoffs poorly reflect actual relevance loss, while engagement heuristics rely on lagging signals, broadly exposing users to stale content before the system adapts. We present SDF (Supersession-Decay Filtering), a staleness filtering system fully deployed in Google Discover, a personalized recommendation feed with hundreds of millions of daily and billions of monthly active users. SDF targets both mechanisms with complementary filters, each powered by a learned model: a relational staleness model that detects supersession between item pairs, and a predicted traffic ratio (PTR) model that forecasts relevance decay from the item's content, trained on lifetime visit traffic. Applied via disjunction upstream of the ranking stage, SDF prunes stale candidates, measurably reducing downstream serving costs. Online experiments demonstrate that these filters significantly reduce the prevalence of stale content while improving user engagement. Over a two-year production deployment, user-filed staleness reports (in-product user feedback) declined by 54.9% relative to the pre-deployment baseline, establishing SDF as a robust and scalable paradigm for resolving content staleness at industrial scale.