用实时排名分数构建动态兴趣序列，提升推荐系统响应速度与精准度。
POEM: Partial-Order Enhanced Real-Time Sequential Modeling for Recommendation
- 基于实时排名得分生成动态偏序序列，捕捉用户瞬时偏好变化。
- 多目标融合模块将异构信号压缩为紧凑表示，提升模型一致性。
- 结合硬负样本与成对损失训练，适合工业级实时推荐场景。
实时推荐系统面临用户兴趣动态漂移和上下文条件变化的挑战。传统序列推荐模型仅依赖静态历史点击序列，无法捕捉即时偏好变化，也忽略了工业推荐系统多阶段排序流程中隐藏的结构化信号。为此，我们提出POEM（部分序增强建模），一种基于推荐级联内在部分序关系的新实时序列建模框架。POEM利用上游排序模块生成的实时多任务排名分数（包括预测点击率和预测观看时长）作为监督信号，构建动态部分序序列，支持细粒度实时兴趣建模，并确保系统排序目标与用户行为模式的一致性优化。核心贡献包括：(1) 偏序引导的序列构建范式，通过基于实时排名分数的动态分组与采样，重构每次请求下的用户兴趣；(2) 多目标分数融合模块，将异构排序信号统一为带归一化秩感知加权的五元组紧凑表示；(3) 分层样本学习策略，采用系统偏好高排名项及用户正反馈（如长时观看视频）作为正例，配以图挖掘的难负样本和基于边距的成对损失进行鲁棒训练。在快手线上流量全量部署下，POEM在KS单页和KS轻页分别实现人均观看时长提升0.249%和0.213%。
原文摘要 · Abstract (English)
Real-time recommendation systems suffer from the dynamic drift of user interests and varying contextual conditions. Conventional sequential recommendation models only exploit static historical click sequences, which fail to capture instant preference changes and overlook structured signals hidden within the multi-stage ranking pipeline of industrial recommendation systems. To tackle these limitations, we propose POEM (Partial-Order Enhanced Modeling), a new real-time sequential modeling framework built upon intrinsic partial-order relations from the recommendation cascade. POEM takes real-time multi-task ranking scores (including predicted CTR and predicted watch duration) generated by upstream ranking modules as supervision to construct dynamic partial-order sequences, supporting fine-grained real-time interest modeling and consistent optimization between system ranking targets and user behavioral patterns. We summarize our core contributions as three aspects: (1) a partial-order guided sequence construction paradigm, which enriches vanilla chronological sequences via dynamic grouping and sampling conditioned on real-time ranking scores to reassess user interests per request; (2) a multi-objective score fusion module that unifies heterogeneous ranking signals into a compact quintuple representation with normalized rank-aware weighting; (3) a hierarchical sample learning strategy, which adopts system-favored high-ranked items and user positive feedback (e.g., long-duration watched videos) as positive instances, paired with graph-mined hard negatives and a margin-based pairwise loss for robust training. Fully deployed on Kuaishou online traffic, POEM achieves significant online gains: average per-user watch time lifts by 0.249% on the KS Single Page and 0.213% on the KS Lite Page.
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