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cs.IR2026
Native Multimodal Representation Learning for Click-Through Rate Prediction in E-Commerce Scenarios
Chao Yi, Feifan Yang, Jiawei Feng +4
Multimodal representations have been widely adopted in industrial e-commerce recommendation systems. Due to their strong semantic understanding and generalization capabilities, the…
cs.IR2026
EST: Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling
Mingyang Liu, Yong Bai, Zhangming Chan +5
Efficiently scaling industrial Click-Through Rate (CTR) prediction has recently attracted significant research attention. Existing approaches typically employ early aggregation of…