71 citations · 86 across the 4 of their papers we have counts for
6 papers
Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection
Yongchun Zhu, Dongbo Xi, Bowen Song +4
With the explosive growth of the e-commerce industry, detecting online transaction fraud in real-world applications has become increasingly important to the development of e-commer…
Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
Dongbo Xi, Zhen Chen, Peng Yan +4
In most real-world large-scale online applications (e.g., e-commerce or finance), customer acquisition is usually a multi-step conversion process of audiences. For example, an impr…
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users
Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang +5
Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich info…
Graph Factorization Machines for Cross-Domain Recommendation
Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu +3
Recently, graph neural networks (GNNs) have been successfully applied to recommender systems. In recommender systems, the user's feedback behavior on an item is usually the result…
Transfer Learning Toolkit: Primers and Benchmarks
Fuzhen Zhuang, Keyu Duan, Tongjia Guo +4
The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function. It…
A Comprehensive Survey on Transfer Learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan +5
Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this wa…