cross-field attention 1industrial deployment 1recommender systems 1sequence compression 1transformer models 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.IR2026
CCFormer: Efficient Cross-Field Interaction and Hierarchical Sequence Compression for Industrial Recommendation at Tencent
Yunlong Wang, Huizhe Zhang, Haonan Hu +5
The paper introduces CCFormer, an efficient Transformer architecture that combines cross-field attention with hierarchical sequence compression to improve industrial recommendation…
cs.IR2026
Bridging the Structural Gap: Adapting Autoregressive Generation for Recommendation
Junchao Zeng, Junzhang Zhu, Junyang Chen +4
Generative Recommendation (GR) has emerged as a new paradigm for sequential recommendation, in which a representative line of work encodes items into hierarchical semantic IDs via…
cs.IR2026
HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent
Yunsheng Pang, Zijian Liu, Yudong Li +10
Slate recommendation, which presents users with a ranked item list in a single display, is ubiquitous across mainstream online platforms. While recent generative recommendation met…