364 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Alibaba Group (China)CN5 papers
- Tsinghua UniversityCN4 papers
- Zhejiang UniversityCN3 papers
- Ant Group (China)CN2 papers
- Shanghai Jiao Tong UniversityCN2 papers
- Chinese Academy of Social SciencesCN1 paper
- Financial Research (Hungary)HU1 paper
- Financial Services AuthorityGB1 paper
- Georgia Institute of TechnologyUS1 paper
- IBM Research (China)CN1 paper
- Indiana University BloomingtonUS1 paper
- Johns Hopkins UniversityUS1 paper
22 papers
RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregation
Junyu Chen, Long Shi, Badong Chen
Graph Neural Networks (GNNs) have exhibited remarkable efficacy in learning from multi-view graph data. In the framework of multi-view graph neural networks, a critical challenge l…
Deep Unified Representation for Heterogeneous Recommendation
Chengqiang Lu, Mingyang Yin, Shuheng Shen +3
Recommendation system has been a widely studied task both in academia and industry. Previous works mainly focus on homogeneous recommendation and little progress has been made for…
LinkLouvain: Link-Aware A/B Testing and Its Application on Online Marketing Campaign
Tianchi Cai, Daxi Cheng, Chen Liang +6
A lot of online marketing campaigns aim to promote user interaction. The average treatment effect (ATE) of campaign strategies need to be monitored throughout the campaign. A/B tes…
Secure Collaborative Training and Inference for XGBoost
Andrew Law, Chester Leung, Rishabh Poddar +6
In recent years, gradient boosted decision tree learning has proven to be an effective method of training robust models. Moreover, collaborative learning among multiple parties has…
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
Yufei Feng, Fuyu Lv, Binbin Hu +5
Click-through rate (CTR) prediction is a critical task for many industrial systems, such as display advertising and recommender systems. Recently, modeling user behavior sequences…
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
Yufei Feng, Binbin Hu, Fuyu Lv +3
Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts m…
Open-Retrieval Conversational Question Answering
Chen Qu, Liu Yang, Cen Chen +3
Conversational search is one of the ultimate goals of information retrieval. Recent research approaches conversational search by simplified settings of response ranking and convers…
A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning
Shijun Wang, Baocheng Zhu, Lintao Ma +1
In this paper, we consider optimizing a smooth, convex, lower semicontinuous function in Riemannian space with constraints. To solve the problem, we first convert it to a dual prob…
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…
Unpack Local Model Interpretation for GBDT
Wenjing Fang, Jun Zhou, Xiaolong Li +1
A gradient boosting decision tree (GBDT), which aggregates a collection of single weak learners (i.e. decision trees), is widely used for data mining tasks. Because GBDT inherits t…
A Semi-supervised Graph Attentive Network for Financial Fraud Detection
Daixin Wang, Jianbin Lin, Peng Cui +7
With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutio…
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…
Occlum: Secure and Efficient Multitasking Inside a Single Enclave of Intel SGX
Youren Shen, Hongliang Tian, Yu Chen +4
Intel Software Guard Extensions (SGX) enables user-level code to create private memory regions called enclaves, whose code and data are protected by the CPU from software and hardw…
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…
Infinite Curriculum Learning for Efficiently Detecting Gastric Ulcers in WCE Images
Xiaolu Zhang, Shiwan Zhao, Lingxi Xie
The Wireless Capsule Endoscopy (WCE) is becoming a popular way of screening gastrointestinal system diseases and cancer. However, the time-consuming process in inspecting WCE data…
FinBrain: When Finance Meets AI 2.0
Xiaolin Zheng, Mengying Zhu, Qibing Li +2
Artificial intelligence (AI) is the core technology of technological revolution and industrial transformation. As one of the new intelligent needs in the AI 2.0 era, financial inte…
Distributed Collaborative Hashing and Its Applications in Ant Financial
Chaochao Chen, Ziqi Liu, Peilin Zhao +3
Collaborative filtering, especially latent factor model, has been popularly used in personalized recommendation. Latent factor model aims to learn user and item latent factors from…
Diffusion Based Network Embedding
Yong Shi, Minglong Lei, Peng Zhang +1
In network embedding, random walks play a fundamental role in preserving network structures. However, random walk based embedding methods have two limitations. First, random walk m…
GeniePath: Graph Neural Networks with Adaptive Receptive Paths
Ziqi Liu, Chaochao Chen, Longfei Li +4
We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data. In GeniePath, we propose an adapti…
Robust Online Multi-Task Learning with Correlative and Personalized Structures
Peng Yang, Peilin Zhao, Xin Gao
Multi-Task Learning (MTL) can enhance a classifier's generalization performance by learning multiple related tasks simultaneously. Conventional MTL works under the offline or batch…
Partitioned Sampling of Public Opinions Based on Their Social Dynamics
Weiran Huang, Liang Li, Wei Chen
Public opinion polling is usually done by random sampling from the entire population, treating individual opinions as independent. In the real world, individuals' opinions are ofte…