1 citations · 1 across the 4 of their papers we have counts for
8 papers
TokenMixer-Large: Scaling Up Large Ranking Models in Industrial Recommenders
Yuchen Jiang, Jie Zhu, Xintian Han +18
While scaling laws for recommendation models have gained significant traction, existing architectures such as Wukong, HiFormer and DHEN, often struggle with sub-optimal designs and…
MSN: A Memory-based Sparse Activation Scaling Framework for Large-scale Industrial Recommendation
Shikang Wu, Hui Lu, Jinqiu Jin +9
Scaling deep learning recommendation models is an effective way to improve model expressiveness. Existing approaches often incur substantial computational overhead, making them dif…
HyFormer: Revisiting the Roles of Sequence Modeling and Feature Interaction in CTR Prediction
Yunwen Huang, Shiyong Hong, Xijun Xiao +7
Industrial large-scale recommendation models (LRMs) face the challenge of jointly modeling long-range user behavior sequences and heterogeneous non-sequential features under strict…
LongRetriever: Towards Ultra-Long Sequence based Candidate Retrieval for Recommendation
Qin Ren, Zheng Chai, Xijun Xiao +2
Precisely modeling user ultra-long sequences is critical for industrial recommender systems. Current approaches predominantly focus on leveraging ultra-long sequences in the rankin…
RankMixer: Scaling Up Ranking Models in Industrial Recommenders
Jie Zhu, Zhifang Fan, Xiaoxie Zhu +18
Recent progress on large language models (LLMs) has spurred interest in scaling up recommendation systems, yet two practical obstacles remain. First, training and serving cost on i…
LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders
Zheng Chai, Qin Ren, Xijun Xiao +14
Modeling ultra-long user behavior sequences is critical for capturing both long- and short-term preferences in industrial recommender systems. Existing solutions typically rely on…