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cs.IR2026
Self-Balancing Gradient Allocation for Heterogeneity-Aware Feature Generation in Click-Through Rate Prediction
Moyu Zhang, Yun Chen, Yujun Jin +3
Generative pre-training via discrete diffusion provides dense reconstruction supervision across all feature fields simultaneously, mitigating representation collapse from data spar…
cs.IR2025
Infer As You Train: A Symmetric Paradigm of Masked Generative for Click-Through Rate Prediction
Moyu Zhang, Yujun Jin, Yun Chen +3
Generative models are increasingly being explored in click-through rate (CTR) prediction field to overcome the limitations of the conventional discriminative paradigm, which rely o…
cs.IR2025
DGenCTR: Towards a Universal Generative Paradigm for Click-Through Rate Prediction via Discrete Diffusion
Moyu Zhang, Yun Chen, Yujun Jin +2
Recent advances in generative models have inspired the field of recommender systems to explore generative approaches, but most existing research focuses on sequence generation, a p…