4 papers
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…
Token Factory: Efficiently Integrating Diverse Signals into Large Recommendation Models
Xilun Chen, Shao-Chuan Wang, Baykal Cakici +6
Large Recommendation Models (LRMs) have demonstrated promising capabilities in industry-scale recommendation tasks. However, holistically integrating traditional signals into these…
TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems
Qingyun Liu, Bo Yan, Yang Liu +16
User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints due to fixed-dimensional vectors. As an emerging…
Zero-shot Cross-domain Knowledge Distillation: A Case study on YouTube Music
Srivaths Ranganathan, Nikhil Khani, Shawn Andrews +8
Knowledge Distillation (KD) has been widely used to improve the quality of latency sensitive models serving live traffic. However, applying KD in production recommender systems wit…