1 citations · 2 across the 6 of their papers we have counts for
6 papers
Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao
Xu Chen, Zida Cheng, Yuangang Pan +6
Existing click-through rate (CTR) prediction works have studied the role of feature interaction through a variety of techniques. Each interaction technique exhibits its own strengt…
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation
Xu Chen, Zida Cheng, Jiangchao Yao +5
Cross-domain CTR (CDCTR) prediction is an important research topic that studies how to leverage meaningful data from a related domain to help CTR prediction in target domain. Most…
Cross-domain Augmentation Networks for Click-Through Rate Prediction
Xu Chen, Zida Cheng, Shuai Xiao +2
Data sparsity is an important issue for click-through rate (CTR) prediction, particularly when user-item interactions is too sparse to learn a reliable model. Recently, many works…
Category-Oriented Representation Learning for Image to Multi-Modal Retrieval
Zida Cheng, Chen Ju, Shuai Xiao +5
The rise of multi-modal search requests from users has highlighted the importance of multi-modal retrieval (i.e. image-to-text or text-to-image retrieval), yet the more complex tas…
Spatio-Temporal Graph Complementary Scattering Networks
Zida Cheng, Siheng Chen, Ya Zhang
Spatio-temporal graph signal analysis has a significant impact on a wide range of applications, including hand/body pose action recognition. To achieve effective analysis, spatio-t…
Semi-supervised 3D Hand-Object Pose Estimation via Pose Dictionary Learning
Zida Cheng, Siheng Chen, Ya Zhang
3D hand-object pose estimation is an important issue to understand the interaction between human and environment. Current hand-object pose estimation methods require detailed 3D la…