39 citations · 105 across the 9 of their papers we have counts for
9 papers
TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit
Yihan Cao, Xu Chen, Lun Du +7
Person-job fit is an essential part of online recruitment platforms in serving various downstream applications like Job Search and Candidate Recommendation. Recently, pretrained la…
Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries
Xinyi He, Mengyu Zhou, Xinrun Xu +9
Tabular data analysis is crucial in various fields, and large language models show promise in this area. However, current research mostly focuses on rudimentary tasks like Text2SQL…
Professional Network Matters: Connections Empower Person-Job Fit
Hao Chen, Lun Du, Yuxuan Lu +6
Online recruitment platforms typically employ Person-Job Fit models in the core service that automatically match suitable job seekers with appropriate job positions. While existing…
On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering
Jiayan Guo, Lun Du, Xu Chen +5
Collaborative filtering (CF) is an important research direction in recommender systems that aims to make recommendations given the information on user-item interactions. Graph CF h…
Homophily-oriented Heterogeneous Graph Rewiring
Jiayan Guo, Lun Du, Wendong Bi +6
With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG) have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown great potenti…
Robust Mid-Pass Filtering Graph Convolutional Networks
Jincheng Huang, Lun Du, Xu Chen +3
Graph convolutional networks (GCNs) are currently the most promising paradigm for dealing with graph-structure data, while recent studies have also shown that GCNs are vulnerable t…
Source Free Unsupervised Graph Domain Adaptation
Haitao Mao, Lun Du, Yujia Zheng +5
Graph Neural Networks (GNNs) have achieved great success on a variety of tasks with graph-structural data, among which node classification is an essential one. Unsupervised Graph D…
Neuron with Steady Response Leads to Better Generalization
Qiang Fu, Lun Du, Haitao Mao +4
Regularization can mitigate the generalization gap between training and inference by introducing inductive bias. Existing works have already proposed various inductive biases from…
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective
Lun Du, Xu Chen, Fei Gao +4
Network Embedding aims to learn a function mapping the nodes to Euclidean space contribute to multiple learning analysis tasks on networks. However, the noisy information behind th…