3 papers
cs.IR2026
DeRes: Decoupling Residual Stability and Adaptivity for Scalable CTR Prediction
Wenzhuo Cheng, Shipeng Nie, Qixin Guo +3
Transformer-based CTR models face a growing bottleneck at the residual connection: under Pre-Norm, early user-interest signals are diluted layer by layer; the identity skip cannot…
cs.IR2026
CapsID: Soft-Routed Variable-Length Semantic IDs for Generative Recommendation
Wenzhuo Cheng, Menghang Gong, Qixin Guo +4
Generative recommendation maps each item to a sequence of Semantic IDs (SIDs) and recasts retrieval as autoregressive token generation. In this paradigm the main bottleneck is the…
cs.IR2026
MI-DPG: Decomposable Parameter Generation Network Based on Mutual Information for Multi-Scenario Recommendation
Wenzhuo Cheng, Ke Ding, Xin Dong +3
Conversion rate (CVR) prediction models play a vital role in recommendation and advertising systems. Recent research on multi-scenario recommendation shows that learning a unified…