提出特征传输机制，让推荐模型更高效地传递用户意图。
From Feature Interaction to Feature Transport - A Unified Block for Scalable Recommendation Models
- 用上下文感知场控制非序列特征对表示演化的主动调节
- 在广告推荐竞赛中达0.83809的测试AUC，优于基线0.83798
- 支持深度和宽度扩展，适合大规模推荐系统
统一推荐模型旨在联合建模非序列多字段特征与序列用户行为，但现有以交互为中心的设计主要关注每层内异构标记的混合。我们认为，可扩展的统一推荐还需控制意图信息在堆叠模块间的传递、过滤与保留。受基于流的表示动态启发，我们引入特征传输概念，将深层统一推荐视为离散上下文条件化的表示演化过程。我们提出CRAFT（Contextual Residual Adaptive Feature Transport）块，将非序列特征归纳为可靠性感知的上下文域，并据此生成残差位移与记忆保持信号，用于意图与序列表示。如此，非序列上下文成为表示演化的主动控制器而非被动交互对象。在TAAC2026广告推荐竞赛中，CRAFT取得0.838090的测试AUC，超越此前榜单最佳分数0.83798。扩展实验表明，将CRAFT堆叠至六层可提升测试AUC至0.838148，增加隐藏维度则达0.838106。结果验证了特征传输范式的有效性、可扩展性与泛化潜力。源码：https://github.com/AshleyLuo001/CRAFT
原文摘要 · Abstract (English)
Unified recommendation models aim to jointly model non-sequential multi-field features and sequential user behaviors, but existing interaction-centric designs mainly focus on mixing heterogeneous tokens within each layer. We argue that scalable unified recommendation also requires controlling how intent information is carried, filtered, and preserved across stacked blocks. Inspired by flow-based representation dynamics, we introduce feature transport, a view that treats deep unified recommendation as a discrete context-conditioned representation evolution process. We propose CRAFT, a Contextual Residual Adaptive Feature Transport block, which summarizes non-sequential features into a reliability-aware contextual field and uses it to generate residual displacement and memory-preserving signals for intent and sequence representations. In this way, non-sequential context acts as an active controller of representation evolution rather than a passive object of interaction. In the TAAC2026 advertising recommendation competition, CRAFT achieves a test AUC of 0.838090, surpassing the previous leaderboard-best score of 0.83798. Scaling experiments further show that CRAFT benefits from both depth and width expansion: stacking CRAFT to six blocks improves test AUC to 0.838148, while increasing the hidden dimension reaches 0.838106. These results demonstrate the effectiveness, scalability, and generalization potential of the feature transport paradigm. Source code: https://github.com/AshleyLuo001/CRAFT
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