From Feature Interaction to Feature Transport - A Unified Block for Scalable Recommendation Models

2026-08-31

AI Summary

本文针对统一推荐模型中特征交互问题，提出CRAFT模块，通过上下文条件下的表示演化过程控制意图信息的传递与保存，提升模型效果和扩展性。

Abstract

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

Problem

• unified recommendation models
• feature interaction
• intent information
• scalability
• representation evolution

Innovation

• Feature Transport
• CRAFT
• Representation Evolution
• Scalable Recommendation
• Context-Aware