5 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…
ML-DCN: Masked Low-Rank Deep Crossing Network Towards Scalable Ads Click-through Rate Prediction at Pinterest
Jiacheng Li, Yixiong Meng, Yi wu +7
Deep learning recommendation systems rely on feature interaction modules to model complex user-item relationships across sparse categorical and dense features. In large-scale ad ra…
Learning to Alleviate Familiarity Bias in Video Recommendation
Zheng Ren, Yi Wu, Jianan Lu +4
Modern video recommendation systems aim to optimize user engagement and platform objectives, yet often face structural exposure imbalances caused by behavioral biases. In this work…
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…