Tianlong Gu

Neighborhood Attentional Memory Networks for Recommendation Systems

2021 · Scientific Programming

Deep learning systems have been phenomenally successful in the fields of computer vision, speech recognition, and natural language processing. Recently, researchers have adopted deep learning techniques to tackle collaborative filtering with implicit feedback. However, the …

Learning from Mistakes: Self-correct Adversarial Training for Chinese Unnatural Text Correction

2024 · arXiv (Cornell University)

Unnatural text correction aims to automatically detect and correct spelling errors or adversarial perturbation errors in sentences. Existing methods typically rely on fine-tuning or adversarial training to correct errors, which have achieved significant success. However, …

Towards Fair Graph Neural Networks via Graph Counterfactual Without Sensitive Attributes

2025

Graph-structured data is ubiquitous in today's connected world, driving extensive research in graph analysis. Graph Neural Networks (GNNs) have shown great success in this field, leading to growing interest in developing fair GNNs for critical …

Improving Recommendation Fairness via Graph Structure and Representation Augmentation

2025

Graph Convolutional Networks (GCNs) have become increasingly popular in recommendation systems. However, recent studies have shown that GCN-based models will cause sensitive information to disseminate widely in the graph structure, amplifying data bias and raising …