6 papers
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…
RankUp: Towards High-rank Representations for Large Scale Advertising Recommender Systems
Jin Chen, Shangyu Zhang, Bin Hu +16
The scaling laws for recommender systems have been increasingly validated, where MetaFormer-based architectures consistently benefit from increased model depth, hidden dimensionali…
TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds
Yifeng Zhou, Yuehong Hu, Zhixiang Feng +9
Recommender systems have historically developed along two largely independent paradigms: feature interaction models for modeling correlations among multi-field categorical features…
From Feature Interaction to Feature Generation: A Generative Paradigm of CTR Prediction Models
Mingjia Yin, Junwei Pan, Hao Wang +5
Click-Through Rate (CTR) prediction, a core task in recommendation systems, aims to estimate the probability of users clicking on items. Existing models predominantly follow a disc…
Practice on Long Behavior Sequence Modeling in Tencent Advertising
Xian Hu, Ming Yue, Zhixiang Feng +24
Long-sequence modeling has become an indispensable frontier in recommendation systems for capturing users' long-term preferences. However, user behaviors within advertising domains…
Large Foundation Model for Ads Recommendation
Shangyu Zhang, Shijie Quan, Zhongren Wang +30
Online advertising relies on accurate recommendation models, with recent advances using pre-trained large-scale foundation models (LFMs) to capture users' general interests across…