AI 中文总结
研究如何解决在线广告中长交互历史与快速服务约束的冲突，采用离线变换器异步编码历史并缓存，运行时模型结合缓存与最新事件，经预训练和微调，提升了广告排名指标和收入，且不增加延迟。
AI 中文摘要
长交互历史是点击率（CTR）预测中最具信息性的输入之一，但在在线广告中，它们与严格的服务约束相冲突：广告必须在几百毫秒内评分才能进入拍卖，这排除了在请求时运行大型序列编码器。我们描述了一个生产广告系统如何通过将历史编码与实时推理解耦来解决此冲突。一个高容量的离线变换器将用户的完整跨表面交互历史异步编码为一个紧凑表示，缓存在特征存储中，而一个轻量级运行时模型在服务时将此缓存表示与用户最近事件和请求上下文相结合。离线编码器在大规模交互日志上进行自回归预训练，有双重目标——反馈预测和下一项预测，然后对两阶段架构进行微调以用于目标广告表面的CTR预测。离线时，这种拆分设计恢复了全历史运行时变换器72 - 80%的质量，而缓存表示对陈旧性具有足够鲁棒性以允许低成本刷新策略。在生产A/B测试中，该系统在搜索广告中使主要排名指标提高了2.77%，在Yandex广告网络上提高了2.1%，收入分别增加了2.26%和0.43%，且不增加服务延迟。
英文摘要
Long interaction histories are among the most informative inputs for click-through rate (CTR) prediction, yet in online advertising they collide with a hard serving constraint: ads must be scored within a few hundred milliseconds to enter the auction, which rules out running a large sequence encoder at request time. We describe how a production advertising system resolves this conflict by decoupling history encoding from real-time inference. A high-capacity offline transformer asynchronously encodes the user's full cross-surface interaction history into a compact representation cached in a feature store, while a lightweight runtime model combines this cached representation with the user's most recent events and the request context at serving time. The offline encoder is pre-trained autoregressively on large-scale interaction logs with a dual objective - feedback prediction and next-item prediction - and the two-stage architecture is then fine-tuned for CTR prediction on the target advertising surface. Offline, the split design recovers 72-80% of the quality of a full-history runtime transformer that would be too expensive to deploy, and the cached representation is robust enough to staleness to permit inexpensive refresh policies. In production A/B experiments, the system improves the primary ranking metric by +2.77% in search advertising and +2.1% on the Yandex Advertising Network, with revenue gains of +2.26% and +0.43% respectively - without increasing serving latency.
