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cs.IR2023
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…
cs.IR2023★ 1 cited
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…
cs.IR2023
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…