4 citations · 6 across the 7 of their papers we have counts for
4 papers · 1 filter
Compress, Cross and Scale: Multi-Level Compression Cross Networks for Efficient Scaling in Recommender Systems
Heng Yu, Xiangjun Zhou, Jie Xia +4
Modeling high-order feature interactions efficiently is a central challenge in click-through rate and conversion rate prediction. Modern industrial recommender systems are predomin…
Multi-Epoch Learning for Deep Click-Through Rate Prediction Models
Zhaocheng Liu, Zhongxiang Fan, Jian Liang +2
The one-epoch overfitting phenomenon has been widely observed in industrial Click-Through Rate (CTR) applications, where the model performance experiences a significant degradation…
Improving Multi-Interest Network with Stable Learning
Zhaocheng Liu, Yingtao Luo, Di Zeng +4
Modeling users' dynamic preferences from historical behaviors lies at the core of modern recommender systems. Due to the diverse nature of user interests, recent advances propose t…
LPFS: Learnable Polarizing Feature Selection for Click-Through Rate Prediction
Yi Guo, Zhaocheng Liu, Jianchao Tan +6
In industry, feature selection is a standard but necessary step to search for an optimal set of informative feature fields for efficient and effective training of deep Click-Throug…