发表机构
Tianjin University（天津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CRAFT模块，将非序列特征作为主动控制器实现可扩展推荐，在TAAC2026竞赛中AUC优于此前最佳，且具备深度与宽度扩展性。
AI 中文摘要
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聚焦于在每一层内混合异构令牌。本文提出，可扩展的统一推荐还需要控制意图信息在堆叠模块间的传递、过滤与保留方式。受基于流的表示动态性启发，本文引入特征迁移（feature transport）视角，将深度统一推荐视为离散的上下文条件化表示演化过程。本文提出CRAFT（Contextual Residual Adaptive Feature Transport，上下文残差自适应特征迁移模块），该模块将非序列特征汇总为感知可靠性的上下文域，并利用该上下文域为意图表示与序列表示生成残差位移及记忆保留信号。由此，非序列上下文作为表示演化的主动控制器，而非被动的交互对象。在TAAC2026广告推荐竞赛中，CRAFT取得了0.838090的测试AUC值，超越了排行榜此前最佳的0.83798分。扩展性实验进一步表明，CRAFT受益于深度与宽度的扩展：将CRAFT堆叠至6个模块可将测试AUC提升至0.838148，而增加隐藏维度则达到0.838106。这些结果证明了特征迁移范式的有效性、可扩展性及泛化潜力。源代码：this https URL
英文摘要
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
Comments13 pages, 8 figures, accept to KDDCUP2026 Workshop
