发表机构
Google LLC（谷歌公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对推荐系统的内容陈旧问题，提出双过滤器框架SDF，通过替代模型和PTR模型分别处理替代与衰减机制，在Google Discover部署后使用户陈旧性报告降54.9%，同时降低服务成本并提升用户参与度。
AI 中文摘要
陈旧推荐是大型内容平台普遍存在的挑战，也是用户投诉的主要来源。项目通过两种主要机制失去相关性：替代，即新的更新使先前的覆盖内容变得陈旧；以及相关性衰减，即项目的信息价值在其生命周期内自然降低。传统对策是粗略的替代指标：年龄截止值无法准确反映实际相关性损失，而参与度启发式方法依赖滞后信号，在系统适应前就广泛向用户暴露陈旧内容。我们提出SDF（替代-衰减过滤，Supersession-Decay Filtering），这是一个完全部署在Google Discover中的陈旧性过滤系统，该平台是拥有数亿日活用户和数十亿月活用户的个性化推荐信息流。SDF通过互补过滤器针对两种机制，每个过滤器都由学习模型驱动：一个检测项目对之间替代关系的关系陈旧性模型，以及一个基于项目内容预测相关性衰减的预测流量比（PTR）模型，该模型基于生命周期访问流量训练。SDF在排序阶段上游通过逻辑或操作应用，修剪陈旧候选，显著降低下游服务成本。在线实验表明，这些过滤器大幅降低了陈旧内容的流行度，同时提升了用户参与度。经过两年的生产部署，用户提交的陈旧性报告（产品内用户反馈）较部署前基线下降了54.9%，证明SDF是解决工业级内容陈旧性的鲁棒且可扩展的范式。
英文摘要
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.
CommentsCIKM Applied Research Track 2026
