从用户序列到缩放定律：Meta广告排序的多阶段架构
从用户序列到缩放定律：Meta广告排序的多阶段架构
英文原标题: From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking
Meta推荐平台每日处理数十亿用户交互，生成捕捉跨产品、广告和内容偏好的时序信号。2024年文章展示了建模用户行为顺序与时间的方法，而非依赖静态稀疏特征。
Original Article
Every day, Meta’s recommendation platforms handle billions of user interactions, generating rich temporal signals that capture individual preferences and intent across products, ads, and content. In our 2024 post on sequence learning for ads recommendations, we showed how modeling the order and timing of user actions (rather than relying on static, manually engineered sparse features) […] Read More… The post From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking appeared first on Engineering at Meta.