2 citations · 3 across the 7 of their papers we have counts for
7 papers
OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval
Ziwei Li, Shuyao Li, Xufeng Cai +12
In modern recommendation systems, retrieval serves as a primary stage responsible for filtering billions of candidate items down to thousands prior to refined ranking. To make this…
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…
Pyramid Mixer: Multi-dimensional Multi-period Interest Modeling for Sequential Recommendation
Zhen Gong, Zhifang Fan, Hui Lu +7
Sequential recommendation, a critical task in recommendation systems, predicts the next user action based on the understanding of the user's historical behaviors. Conventional stud…
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…
Large Memory Network for Recommendation
Hui Lu, Zheng Chai, Yuchao Zheng +5
Modeling user behavior sequences in recommender systems is essential for understanding user preferences over time, enabling personalized and accurate recommendations for improving…
Adaptive Domain Scaling for Personalized Sequential Modeling in Recommenders
Zheng Chai, Hui Lu, Di Chen +3
Users generally exhibit complex behavioral patterns and diverse intentions in multiple business scenarios of super applications like Douyin, presenting great challenges to current…