27 citations · 42 across the 6 of their papers we have counts for
6 papers
TGR: Advancing Industrial Recommendation from Generative-Paradigm Ranking toward Unified Generation and Reasoning
TGR Team, Lei Cheng, Haonan Hu +11
Industrial recommender systems typically rely on cascaded retrieval, pre-ranking, ranking, and reranking stages, whose separately optimized models limit scaling, fragment decision…
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…
CCFormer: Efficient Cross-Field Interaction and Hierarchical Sequence Compression for Industrial Recommendation at Tencent
Yunlong Wang, Huizhe Zhang, Haonan Hu +5
Recent studies in industrial recommendation systems have demonstrated that sequential recommendation models built upon self-attention can benefit from predictable scaling laws by i…
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…
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Guanghu Yuan, Fajie Yuan, Yudong Li +9
Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets of…
One Person, One Model, One World: Learning Continual User Representation without Forgetting
Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou +3
Learning user representations is a vital technique toward effective user modeling and personalized recommender systems. Existing approaches often derive an individual set of model…