49 citations · 108 across the 7 of their papers we have counts for
7 papers
Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model
Wentao Ouyang, Xiuwu Zhang, Chaofeng Guo +8
In real-world advertising systems, conversions have different types in nature and ads can be shown in different display scenarios, both of which highly impact the actual conversion…
Contrastive Learning for Conversion Rate Prediction
Wentao Ouyang, Rui Dong, Xiuwu Zhang +4
Conversion rate (CVR) prediction plays an important role in advertising systems. Recently, supervised deep neural network-based models have shown promising performance in CVR predi…
Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction
Wentao Ouyang, Xiuwu Zhang, Shukui Ren +5
Click-through rate (CTR) prediction is one of the most central tasks in online advertising systems. Recent deep learning-based models that exploit feature embedding and high-order…
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
Wentao Ouyang, Xiuwu Zhang, Lei Zhao +5
Click-through rate (CTR) prediction is a critical task in online advertising systems. Existing works mainly address the single-domain CTR prediction problem and model aspects such…
Click-Through Rate Prediction with the User Memory Network
Wentao Ouyang, Xiuwu Zhang, Shukui Ren +3
Click-through rate (CTR) prediction is a critical task in online advertising systems. Models like Deep Neural Networks (DNNs) are simple but stateless. They consider each target ad…
Representation Learning-Assisted Click-Through Rate Prediction
Wentao Ouyang, Xiuwu Zhang, Shukui Ren +3
Click-through rate (CTR) prediction is a critical task in online advertising systems. Most existing methods mainly model the feature-CTR relationship and suffer from the data spars…