96 citations · 391 across the 25 of their papers we have counts for
25 papers
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…
Question Directed Graph Attention Network for Numerical Reasoning over Text
Kunlong Chen, Weidi Xu, Xingyi Cheng +6
Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language un…
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…
SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
Xingyi Cheng, Weidi Xu, Kunlong Chen +5
Chinese Spelling Check (CSC) is a task to detect and correct spelling errors in Chinese natural language. Existing methods have made attempts to incorporate the similarity knowledg…
Practical Privacy Preserving POI Recommendation
Chaochao Chen, Jun Zhou, Bingzhe Wu +4
Point-of-Interest (POI) recommendation has been extensively studied and successfully applied in industry recently. However, most existing approaches build centralized models on the…
RNE: A Scalable Network Embedding for Billion-scale Recommendation
Jianbin Lin, Daixin Wang, Lu Guan +5
Nowadays designing a real recommendation system has been a critical problem for both academic and industry. However, due to the huge number of users and items, the diversity and dy…
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…
AGL: a Scalable System for Industrial-purpose Graph Machine Learning
Dalong Zhang, Xin Huang, Ziqi Liu +8
Machine learning over graphs have been emerging as powerful learning tools for graph data. However, it is challenging for industrial communities to leverage the techniques, such as…
Local Contextual Attention with Hierarchical Structure for Dialogue Act Recognition
Zhigang Dai, Jinhua Fu, Qile Zhu +3
Dialogue act recognition is a fundamental task for an intelligent dialogue system. Previous work models the whole dialog to predict dialog acts, which may bring the noise from unre…
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance
Cen Chen, Chen Liang, Jianbin Lin +7
The insurance industry has been creating innovative products around the emerging online shopping activities. Such e-commerce insurance is designed to protect buyers from potential…
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models
Yankun Ren, Jianbin Lin, Siliang Tang +4
Today text classification models have been widely used. However, these classifiers are found to be easily fooled by adversarial examples. Fortunately, standard attacking methods ge…
Long Short-Term Sample Distillation
Liang Jiang, Zujie Wen, Zhongping Liang +7
In the past decade, there has been substantial progress at training increasingly deep neural networks. Recent advances within the teacher--student training paradigm have establishe…
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing
Ziqi Liu, Dong Wang, Qianyu Yu +8
Mobile payment such as Alipay has been widely used in our daily lives. To further promote the mobile payment activities, it is important to run marketing campaigns under a limited…
Uncovering Insurance Fraud Conspiracy with Network Learning
Chen Liang, Ziqi Liu, Bin Liu +4
Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over…
Efficient Probabilistic Logic Reasoning with Graph Neural Networks
Yuyu Zhang, Xinshi Chen, Yuan Yang +4
Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in ML…
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…
Cost-Effective Incentive Allocation via Structured Counterfactual Inference
Romain Lopez, Chenchen Li, Xiang Yan +4
We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers…
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…
Double Neural Counterfactual Regret Minimization
Hui Li, Kailiang Hu, Zhibang Ge +3
Counterfactual Regret Minimization (CRF) is a fundamental and effective technique for solving Imperfect Information Games (IIG). However, the original CRF algorithm only works for…
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…