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

arXiv:2609.01655 · cs.IR, cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.IR, cs.AI

Submitted: 2026-08-31

Updated: 2026-08-31

Comments: 13 pages, 8 figures, accept to KDDCUP2026 Workshop

Code: https://github.com/AshleyLuo001/CRAFT

Project page: https://algo.qq.com

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Distillation — 33× in this paper · explained in 2 episodes
• AUC — 31× in this paper · explained in 2 episodes
• Tokenization — 17× in this paper · explained in 2 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Embeddings — 9× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Robustness — 4× in this paper · explained in 5 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Late Fusion — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Teacher-Student Distillation — 1× in this paper · explained in 2 episodes

Sources

• Wukong: Towards a Scaling Law for Large-Scale Recommendation
• TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds
• Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
• On the Embedding Collapse when Scaling up Recommendation Models
• Session-based Recommendations with Recurrent Neural Networks
• HyFormer: Revisiting the Roles of Sequence Modeling and Feature Interaction in CTR Prediction

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