5 citations · 5 across the 7 of their papers we have counts for
8 papers
AlignPoison: Conditional Targeted Poisoning of Generative Recommenders via Decoding-Aligned Prefixes
Zhuoxuan Zhang, Jinhao Duan, Xinyu Zhao +7
Generative recommendation models based on autoregressive decoding have been adopted as alternatives to classical ranking-based systems. While their modeling flexibility has been wi…
Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System
Ming Li, Dai Li, Xuying Ning +10
Auto-research agents have shown the potential to automate hypothesis generation, experiment execution, and iterative refinement. However, scaling this paradigm to industry-scale re…
Bumblebee: Interleaved Mixed-Layer Building Blocks for Large-Scale Recommendation Systems
David Bauer, Cancan Zhang, Wenshun Liu +13
Recommendation systems have undergone significant transformations in the past years. The transition from traditional feature interaction modules to generative next-action predictio…
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…
Target-Aware Early Stage Ranking
Juhee Hong, Meng Liu, Shengzhi Wang +18
Early Stage Ranking (ESR) in large-scale recommendation systems is dominated by ''user--item decoupling'' Two Tower architectures, which scale efficiently but cannot capture fine-g…
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…