96 citations · 391 across the 25 of their papers we have counts for
10 papers · 1 filter
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
Ke Tu, Peng Cui, Daixin Wang +4
Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relati…
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing
Hui Li, Xing Fu, Ruofan Wu +8
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as compu…
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…
Bandit Samplers for Training Graph Neural Networks
Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang +4
Several sampling algorithms with variance reduction have been proposed for accelerating the training of Graph Convolution Networks (GCNs). However, due to the intractable computati…
NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay
Jianbin Lin, Zhiqiang Zhang, Jun Zhou +5
Ant Credit Pay is a consumer credit service in Ant Financial Service Group. Similar to credit card, loan default is one of the major risks of this credit product. Hence, effective…
TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial
Shaosheng Cao, Xinxing Yang, Cen Chen +3
With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business. To tack…
Can Graph Neural Networks Help Logic Reasoning?
Yuyu Zhang, Xinshi Chen, Yuan Yang +4
Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training da…
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
Chao Qu, Shie Mannor, Huan Xu +3
We consider the networked multi-agent reinforcement learning (MARL) problem in a fully decentralized setting, where agents learn to coordinate to achieve the joint success. This pr…
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen, Shuang Li, Hui Li +3
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither…
Reinforcement Learning for Uplift Modeling
Chenchen Li, Xiang Yan, Xiaotie Deng +6
Uplift modeling aims to directly model the incremental impact of a treatment on an individual response. In this work, we address the problem from a new angle and reformulate it as…