166 citations · 342 across the 5 of their papers we have counts for
5 papers
Improving Code Search with Hard Negative Sampling Based on Fine-tuning
Hande Dong, Jiayi Lin, Yanlin Wang +3
Pre-trained code models have emerged as the state-of-the-art paradigm for code search tasks. The paradigm involves pre-training the model on search-irrelevant tasks such as masked…
AutoDebias: Learning to Debias for Recommendation
Jiawei Chen, Hande Dong, Yang Qiu +5
Recommender systems rely on user behavior data like ratings and clicks to build personalization model. However, the collected data is observational rather than experimental, causin…
On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
Hande Dong, Jiawei Chen, Fuli Feng +4
The original design of Graph Convolution Network (GCN) couples feature transformation and neighborhood aggregation for node representation learning. Recently, some work shows that…
Bias and Debias in Recommender System: A Survey and Future Directions
Jiawei Chen, Hande Dong, Xiang Wang +3
While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit user beh…
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
Hande Dong, Zhaolin Ding, Xiangnan He +2
Today, there are two major understandings for graph convolutional networks, i.e., in the spectral and spatial domain. But both lack transparency. In this work, we introduce a new u…