3 papers
cs.IR2026
LLaTTE: Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendation
Lee Xiong, Zhirong Chen, Rahul Mayuranath +17
We present LLaTTE (LLM-Style Latent Transformers for Temporal Events), a scalable transformer architecture for production ads recommendation. Through systematic experiments, we dem…
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…
cs.IR2025
Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID
Carolina Zheng, Minhui Huang, Dmitrii Pedchenko +15
The exponential growth of online content has posed significant challenges to ID-based models in industrial recommendation systems, ranging from extremely high cardinality and dynam…