5 citations · 5 across the 5 of their papers we have counts for
5 papers
WHALE: A Scalable Unified Model for Recommendation with Wukong-HSTU Architecture
Renqin Cai, Dawei Sun, Yuanjun Yao +8
As scalability becomes increasingly important in recommendation modeling, recent architectures have advanced the modeling of two broad sources of ranking signals along separate pat…
Request-Only Optimization for Recommendation Systems
Liang Guo, Wei Li, Lucy Liao +25
Deep Learning Recommendation Models (DLRMs) represent one of the largest machine learning applications on the planet. Industry-scale DLRMs are trained with petabytes of recommendat…
Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment
Dai Li, Kevin Course, Wei Li +13
Scaling laws have been established for recommender systems, yet efficiently deploying foundation model (FM) across multiple recommendation surfaces remains a major unsolved challen…
Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
Jiaqi Zhai, Lucy Liao, Xing Liu +9
Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a dail…
Breaking the Curse of Quality Saturation with User-Centric Ranking
Zhuokai Zhao, Yang Yang, Wenyu Wang +5
A key puzzle in search, ads, and recommendation is that the ranking model can only utilize a small portion of the vastly available user interaction data. As a result, increasing da…