2 citations · 8 across the 11 of their papers we have counts for
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cs.IR2026
Versioned Late Materialization for Ultra-Long Sequence Training in Recommendation Systems at Scale
Liang Guo, Ge Song, Litao Deng +9
Modern Deep Learning Recommendation Models (DLRMs) follow scaling laws with sequence length, driving the frontier toward ultra-long User Interaction History (UIH). However, the ind…
cs.IR2026
Bending the Scaling Law Curve in Large-Scale Recommendation Systems
Qin Ding, Kevin Course, Linjian Ma +19
Learning from user interaction history through sequential models has become a cornerstone of large-scale recommender systems. Recent advances in large language models have revealed…
cs.IR2024★ 2 cited
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
Tianxin Wei, Bowen Jin, Ruirui Li +8
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration. Ou…