2 papers
cs.DC2025
Near-Zero-Overhead Freshness for Recommendation Systems via Inference-Side Model Updates
Wenjun Yu, Sitian Chen, Cheng Chen +1
Deep Learning Recommendation Models (DLRMs) underpin personalized services but face a critical freshness-accuracy tradeoff due to massive parameter synchronization overheads. Produ…
cs.IR2024
Data Efficiency for Large Recommendation Models
Kshitij Jain, Jingru Xie, Kevin Regan +9
Large recommendation models (LRMs) are fundamental to the multi-billion dollar online advertising industry, processing massive datasets of hundreds of billions of examples before t…