5 papers
UniCon: A Unified Context-Centric Modeling Paradigm for CTR Prediction
Jiajun Cui, Zhengqi Xu, Fan Zhang +6
Unified modeling has become a major direction for industrial click-through rate (CTR) prediction. Existing approaches typically unify sequential and non-sequential signals at the t…
Beyond Retrieval: Learning Compact User Representations for Scalable LLM Personalization
Heng Cao, Fan Zhang, Jian Yao +8
Personalizing large language models requires adapting model behavior to individual users while preserving robustness and deployment-scale efficiency. Existing approaches typically…
HoMer: Addressing Heterogeneities by Modeling Sequential and Set-wise Contexts for CTR Prediction
Shuwei Chen, Jiajun Cui, Zhengqi Xu +4
Click-through rate (CTR) prediction, which models behavior sequence and non-sequential features (e.g., user/item profiles or cross features) to infer user interest, underpins indus…
EGA-V1: Unifying Online Advertising with End-to-End Learning
Junyan Qiu, Ze Wang, Fan Zhang +7
Modern industrial advertising systems commonly employ Multi-stage Cascading Architectures (MCA) to balance computational efficiency with ranking accuracy. However, this approach pr…
ShuffleGate: Scalable Feature Optimization for Recommender Systems via Batch-wise Sensitivity Learning
Yihong Huang, Chen Chu, Fan Zhang +4
Feature optimization -- specifically Feature Selection (FS) and Dimension Selection (DS) -- is critical for the efficiency and generalization of large-scale recommender systems. Wh…