UniCon: A Unified Context-Centric Modeling Paradigm for CTR Prediction

2026-09-02

AI Summary

为解决CTR预测中历史与当前请求信号异质性导致的效率和质量限制，UniCon采用统一上下文为中心的方法，通过上下文内和上下文间注意力机制提高模型性能。

Abstract

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.

Problem

• Unified Modeling
• CTR Prediction
• Context-Centric
• Structural Dependencies
• Decision Process

Innovation

• Unified Context-Centric Modeling
• Intra-context Attention
• Inter-context Attention
• Sequence Compression