Short-video recommenders such as Douyin must exploit extremely long user behavior histories without breaking latency or cost budgets. We present an end-to-end industrial recommender system that scales long-sequence recommendation modeling to 10K-length histories in production. First, we introduce Stacked Target-to-History Cross Attention (STCA), which replaces history self-attention with stacked cross-attention from the target to the history, reducing complexity from quadratic to linear in sequence length and enabling efficient end-to-end training over long user behavior sequences. Second, we propose Request Level Batching (RLB), a user-centric batching scheme that aggregates multiple targets for the same user/request to share the user-side encoding, substantially lowering sequence-related storage, communication, and compute without changing the learning objective. Third, we design a length-extrapolative training strategy -- train on shorter windows, infer on much longer ones -- so the model generalizes to 10K-scale histories without additional training cost. Across offline and online experiments, we observe predictable, monotonic gains as we scale history length and model capacity, mirroring the scaling law behavior observed in large language models. Deployed at full traffic on Douyin, our system delivers significant improvements on key engagement metrics while meeting production latency, demonstrating a practical path to scaling end-to-end ultra-long sequence recommendation to the 10K regime.

翻译：短推荐器（如抖音）必须在不突破延迟或成本预算的前提下，利用极长的用户行为历史。我们提出一套端到端工业级推荐系统，将长序列推荐建模扩展至生产环境中的万级历史长度。首先，引入堆叠目标-历史交叉注意力（STCA），以目标到历史的堆叠交叉注意力替代历史自注意力，将序列长度的复杂度从二次降至线性，从而实现对长用户行为序列的高效端到端训练。其次，提出请求级批处理（RLB），一种以用户为中心的批处理方案，通过聚合同一用户/请求的多个目标共享用户端编码，在不改变学习目标的前提下，大幅降低序列相关的存储、通信和计算开销。第三，设计长度外推训练策略——在较短窗口上训练，在更长窗口上推理——使模型在不增加训练成本的情况下泛化至万级历史长度。离线与在线实验表明，随着历史长度和模型容量的扩展，我们观察到可预测的单调增益，这与大语言模型中的缩放定律行为一致。该系统已在抖音全流量部署，在满足生产延迟的同时，显著提升了关键参与度指标，为端到端超长序列推荐扩展至万级规模提供了一条实践路径。