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cs.LG2025
DynamiX: Dynamic Resource eXploration for Personalized Ad-Recommendations
Sohini Roychowdhury, Adam Holeman, Mohammad Amin +3
For online ad-recommendation systems, processing complete user-ad-engagement histories is both computationally intensive and noise-prone. We introduce Dynamix, a scalable, personal…
cs.LG2025
SIDE: Semantic ID Embedding for effective learning from sequences
Dinesh Ramasamy, Shakti Kumar, Chris Cadonic +4
Sequence-based recommendations models are driving the state-of-the-art for industrial ad-recommendation systems. Such systems typically deal with user histories or sequence lengths…