4 papers · 1 filter
Lightweight Ranking Heads: Accelerating Multi-Task Experimentation in Production Recommender Systems
Sanjay Surendranath Girija, Aniruddh Nath, Li Wei +6
Modern production-scale recommender systems rely on complex, multi-task ranking models. Introducing new prediction tasks into these massive systems often causes bottlenecks - it ri…
Code-to-Harness: Distilling Black-Box Optimizers from Self-Play
Yi Wu, Zheng Ren, Zhiyu Hu +8
Can an agent learn a numerical search strategy through executable practice and then transfer that strategy as text? We study low-budget black-box optimization, where unaided langua…
Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents
Haochen Wang, Yi Wu, Daryl Chang +2
Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hyperparameter search space and, more criti…
Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction
Yi Wu, Daryl Chang, Jennifer She +3
We present the Learned Ranking Function (LRF), a system that takes short-term user-item behavior predictions as input and outputs a slate of recommendations that directly optimizes…