24 citations · 53 across the 5 of their papers we have counts for
5 papers
Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation
Jiajie Zhu, Yan Wang, Feng Zhu +1
The conventional single-target Cross-Domain Recommendation (CDR) aims to improve the recommendation performance on a sparser target domain by transferring the knowledge from a sour…
DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation
Feng Zhu, Mingjie Zhong, Xinxing Yang +8
In recommendation scenarios, there are two long-standing challenges, i.e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-…
A Unified Framework for Cross-Domain and Cross-System Recommendations
Feng Zhu, Yan Wang, Jun Zhou +3
Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have been proposed to improve the recommendation accuracy in a target dataset (domain/system) with the help…
Cross-Domain Recommendation: Challenges, Progress, and Prospects
Feng Zhu, Yan Wang, Chaochao Chen +3
To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information f…
A Deep Framework for Cross-Domain and Cross-System Recommendations
Feng Zhu, Yan Wang, Chaochao Chen +3
Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender system…