Intelligent financial fraud detection practices in post-pandemic era
The great losses caused by financial fraud have attracted continuous attention from
academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus …
academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus …
Collaborative filtering recommender systems taxonomy
H Papadakis, A Papagrigoriou, C Panagiotakis… - … and Information Systems, 2022 - Springer
In the era of internet access, recommender systems try to alleviate the difficulty that
consumers face while trying to find items (eg, services, products, or information) that better …
consumers face while trying to find items (eg, services, products, or information) that better …
Learning to warm up cold item embeddings for cold-start recommendation with meta scaling and shifting networks
Recently, embedding techniques have achieved impressive success in recommender
systems. However, the embedding techniques are data demanding and suffer from the cold …
systems. However, the embedding techniques are data demanding and suffer from the cold …
Multi-view multi-behavior contrastive learning in recommendation
Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to
improve the target behavior's performance. We argue that MBR models should:(1) model the …
improve the target behavior's performance. We argue that MBR models should:(1) model the …
Modeling the sequential dependence among audience multi-step conversions with multi-task learning in targeted display advertising
In most real-world large-scale online applications (eg, e-commerce or finance), customer
acquisition is usually a multi-step conversion process of audiences. For example, an …
acquisition is usually a multi-step conversion process of audiences. For example, an …
Intention-aware heterogeneous graph attention networks for fraud transactions detection
Fraud transactions have been the major threats to the healthy development of e-commerce
platforms, which not only damage the user experience but also disrupt the orderly operation …
platforms, which not only damage the user experience but also disrupt the orderly operation …
Learning to retrieve user behaviors for click-through rate estimation
Click-through rate (CTR) estimation plays a crucial role in modern online personalization
services. It is essential to capture users' drifting interests by modeling sequential user …
services. It is essential to capture users' drifting interests by modeling sequential user …
Sequence as genes: An user Behavior modeling framework for fraud transaction detection in E-commerce
Z Wang, Q Wu, B Zheng, J Wang, K Huang… - Proceedings of the 29th …, 2023 - dl.acm.org
With the explosive growth of e-commerce, detecting fraudulent transactions in real-world
scenarios is becoming increasingly important for e-commerce platforms. Recently, several …
scenarios is becoming increasingly important for e-commerce platforms. Recently, several …
Modeling the field value variations and field interactions simultaneously for fraud detection
With the explosive growth of e-payment industry, online transaction fraud has become one of
the biggest challenges for the business. The historical behavior information of users …
the biggest challenges for the business. The historical behavior information of users …
MT-BICN: Multi-task Balanced Information Cascade Network for Recommendation
H Wu, Y Gao - … Conference on Knowledge Science, Engineering and …, 2023 - Springer
Multi-task learning (MTL) is a promising research direction in recommender systems, whose
prediction accuracy greatly depends on the quality of the modeling of the relationships …
prediction accuracy greatly depends on the quality of the modeling of the relationships …