Jia Wu

FraudNE: a Joint Embedding Approach for Fraud Detection

2018

Detecting fraudsters is a meaningful problem for both users and e-commerce platform. Existing graph-based approaches mainly adopt shallow models, which cannot capture the highly non-linear relationship between vertexes in a bipartite graph composed of users …

Deep Reinforcement Learning with Model-Based Acceleration for Hyperparameter Optimization

2019

Hyperparameter optimization is a key part of AutoML. In recent years, there have been successful hyperparameter optimization algorithms. However, these methods still face several challenges, such as high cost of evaluating large models or large …

Deep Semantic Network Representation

2020

Network representation aims to learn low-dimensional vector representations of network nodes while preserving the inherent properties of the network. For all its popularity, majority of the existing methods focus on exploitation of diverse information, including …

Dual-branch Density Ratio Estimation for Signed Network Embedding

2022 · Proceedings of the ACM Web Conference 2022

Signed network embedding (SNE) has received considerable attention in recent years. A mainstream idea of SNE is to learn node representations by estimating the ratio of sampling densities. Though achieving promising performance, these methods based …

Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations

2021 · arXiv (Cornell University)

Event extraction is a fundamental task for natural language processing. Finding the roles of event arguments like event participants is essential for event extraction. However, doing so for real-life event descriptions is challenging because an …

Exploring Sparsity in Graph Transformers

2023 · arXiv (Cornell University)

Graph Transformers (GTs) have achieved impressive results on various graph-related tasks. However, the huge computational cost of GTs hinders their deployment and application, especially in resource-constrained environments. Therefore, in this paper, we explore the feasibility …

Artificial intelligence auxiliary diagnosis and treatment system for breast cancer in developing countries

2024 · Journal of X-Ray Science and Technology

BACKGROUND: In many developing countries, a significant number of breast cancer patients are unable to receive timely treatment due to a large population base, high patient numbers, and limited medical resources. OBJECTIVE: This paper proposes …

Social Recommendation with an Essential Preference Space

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Social recommendation, which aims to exploit social information to improve the quality of a recommender system, has attracted an increasing amount of attention in recent years. A large portion of existing social recommendation models are …

A Deep Framework for Cross-Domain and Cross-System Recommendations

2018

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e.g., ratings, from the source domain …