19 citations · 59 across the 6 of their papers we have counts for
6 papers
ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding
Zhiqiang Wang, Qingyun She, PengTao Zhang +1
Click-through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems and it's important for ranking models to effectively capture complex h…
Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction
Qingyun She, Zhiqiang Wang, Junlin Zhang
Click-through rate (CTR) prediction plays important role in personalized advertising and recommender systems. Though many models have been proposed such as FM, FFM and DeepFM in re…
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
Zhiqiang Wang, Qingyun She, Junlin Zhang
Click-Through Rate(CTR) estimation has become one of the most fundamental tasks in many real-world applications and it's important for ranking models to effectively capture complex…
BoostingBERT:Integrating Multi-Class Boosting into BERT for NLP Tasks
Tongwen Huang, Qingyun She, Junlin Zhang
As a pre-trained Transformer model, BERT (Bidirectional Encoder Representations from Transformers) has achieved ground-breaking performance on multiple NLP tasks. On the other hand…
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…