17 citations · 59 across the 8 of their papers we have counts for
5 papers · 1 filter
PEARL: Unbiased Percentile Estimation via Contrastive Learning for Industrial-Scale Livestream Recommendation
Blake Gella, Wei Wu, Yuhao Yin +6
Recommender systems trained on user interaction data are susceptible to behavioral intensity imbalance--a systematic distortion arising from heterogeneous engagement patterns acros…
Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction
Zhiqiang Wang, Qingyun She, PengTao Zhang +1
Normalization has become one of the most fundamental components in many deep neural networks for machine learning tasks while deep neural network has also been widely used in CTR e…
GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction
Tongwen Huang, Qingyun She, Zhiqiang Wang +1
Advertising and feed ranking are essential to many Internet companies such as Facebook. Among many real-world advertising and feed ranking systems, click through rate (CTR) predict…
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
Tongwen Huang, Zhiqi Zhang, Junlin Zhang
Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising and feed ranking systems, click through rat…
FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine
Junlin Zhang, Tongwen Huang, Zhiqi Zhang
Click through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems. Recent years have witnessed the success of both the deep learning bas…