发表机构
Meituan; SJTU; BUAA（美团; 上海交通大学; 北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有CTR预测模型将历史与请求视为异质信号的缺陷，提出以统一上下文为中心的UniCon架构，在美团搜索广告任务中显著提升了离线AUC及在线RPM、CTR、营收指标。
AI 中文摘要
统一建模已成为工业界点击率（CTR）预测的主要方向。现有方法通常在令牌级别统一序列和非序列信号，在共享主干中对其交互进行建模，并增加模型容量以提升扩展性能。然而，这种划分源于遗留的特征工程实践，与底层决策过程不匹配。用户行为本质上是一系列同质上下文单元；在输入组织层面，历史行为与当前请求的区别仅在于其结果是已观测到的还是待预测的。将它们视为异质信号会模糊用户决策上下文内的结构依赖关系，限制扩展效率和预测质量，在电商货架、瀑布流等上下文丰富场景中这一限制尤为显著。为解决该问题，我们提出UniCon，一种以统一上下文为中心的建模架构，将请求上下文作为基本建模单元，把历史行为和预测目标组织为同质上下文单元。上下文内注意力捕捉同一上下文内项目间的局部耦合（局部性），上下文间注意力建模跨上下文决策状态的动态演化（动态性）。这种组织弥合了历史与目标间的结构差距，支持统一CTR模型更高效的扩展。上下文单元级序列压缩进一步降低部署开销。在美团搜索广告任务中，UniCon相较于强大的生产基线，将离线AUC提升0.0139，且在线指标实现具有统计显著性的提升：RPM提升3.09%，CTR提升2.07%，营收提升2.95%。
英文摘要
Unified modeling has become a major direction for industrial click-through rate (CTR) prediction. Existing approaches typically unify sequential and non-sequential signals at the token level, model their interactions in a shared backbone, and increase model capacity to improve scaling behavior. However, this division originates from legacy feature-engineering practice and is misaligned with the underlying decision process. User behavior is inherently a sequence of homogeneous context units; at the level of input organization, historical behavior and the current request differ only in whether their outcomes are observed or remain to be predicted. Treating them as heterogeneous signals obscures structural dependencies within the user's decision context, limiting both scaling efficiency and prediction quality. This limitation is particularly pronounced in context-rich scenarios such as e-commerce shelves and waterfall feeds. To address this, we propose UniCon, a unified context-centric modeling architecture that treats the request context as the basic modeling unit and organizes history and prediction targets as homogeneous context units. Intra-context attention captures local coupling among items within a context (Locality), while inter-context attention models the dynamic evolution of decision states across contexts (Dynamics). This organization bridges the structural gap between history and target and supports more effective scaling of unified CTR models. Context-unit-level sequence compression further reduces deployment overhead. On Meituan search advertising, UniCon improves offline AUC by 0.0139 over a strong production baseline and achieves statistically significant online lifts of 3.09% in RPM, 2.07% in CTR, and 2.95% in revenue.
Comments10 pages, 5 figures, 2 tables
