227 citations · 461 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification
Dongbo Xi, Fuzhen Zhuang, Yanchi Liu +4
Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Int…
Neural Hierarchical Factorization Machines for User's Event Sequence Analysis
Dongbo Xi, Fuzhen Zhuang, Bowen Song +6
Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance. However, existing popular…
Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification
Dongbo Xi, Fuzhen Zhuang, Yanchi Liu +3
Human mobility data accumulated from Point-of-Interest (POI) check-ins provides great opportunity for user behavior understanding. However, data quality issues (e.g., geolocation i…
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection
Dongbo Xi, Bowen Song, Fuzhen Zhuang +5
With the explosive growth of e-commerce, online transaction fraud has become one of the biggest challenges for e-commerce platforms. The historical behaviors of users provide rich…
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…