3 papers
cs.LG2026
Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation
Guoming Li, Shangyu Zhang, Junwei Pan +7
Scaling recommendation models is a central challenge in recommender systems. Recently, RankMixer has emerged as an effective solution, operating on a unified token representation a…
cs.IR2025
Dual Test-time Training for Out-of-distribution Recommender System
Xihong Yang, Yiqi Wang, Jin Chen +5
Deep learning has been widely applied in recommender systems, which has achieved revolutionary progress recently. However, most existing learning-based methods assume that the user…
cs.IR2024
Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction
Qi Liu, Xuyang Hou, Haoran Jin +6
Extracting users' interests from their lifelong behavior sequence is crucial for predicting Click-Through Rate (CTR). Most current methods employ a two-stage process for efficiency…