13 papers
MATT-CTR: Unleashing a Model-Agnostic Test-Time Paradigm for CTR Prediction with Confidence-Guided Inference Paths
Moyu Zhang, Yun Chen, Yujun Jin +3
Recently, a growing body of research has focused on either optimizing CTR model architectures to better model feature interactions or refining training objectives to aid parameter…
Selective Test-Time Compute Scaling for Click-Through Rate Prediction via Uncertainty-Triggered Feature Path Exploration
Moyu Zhang, Yun Chen, Yujun Jin +3
Scaling test-time compute has proven highly effective for language models, yet this opportunity remains largely unexplored for industrial Click-Through Rate (CTR) prediction. CTR m…
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…
AgenticRS-EnsNAS: Ensemble-Decoupled Self-Evolving Architecture Search
Yun Chen, Moyu Zhang, Jinxin Hu +2
Neural Architecture Search (NAS) deployment in industrial production systems faces a fundamental validation bottleneck: verifying a single candidate architecture pi requires evalua…
Deferred is Better: A Framework for Multi-Granularity Deferred Interaction of Heterogeneous Features
Yi Xu, Moyu Zhang, Chaofan Fan +3
Click-through rate (CTR) prediction models estimates the probability of a user-item click by modeling interactions across a vast feature space. A fundamental yet often overlooked c…
Bridging Sequential and Contextual Features with a Dual-View of Fine-grained Core-Behaviors and Global Interest-Distribution
Yi Xu, Chaofan Fan, Moyu Zhang +6
Click-through rate (CTR) prediction tasks typically estimate the probability of a user clicking on a candidate item by modeling both user behavior sequence features and the item's…