101 citations · 423 across the 19 of their papers we have counts for
7 papers · 1 filter
Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation
Zhongfen Deng, Hao Peng, Congying Xia +3
Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing. However, most existing methods either use…
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
Hu Liu, Jing Lu, Xiwei Zhao +8
Click-through rate (CTR) prediction is one of the fundamental tasks for e-commerce search engines. As search becomes more personalized, it is necessary to capture the user interest…
KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning
Ye Liu, Yao Wan, Lifang He +2
Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text genera…
Adversarial Directed Graph Embedding
Shijie Zhu, Jianxin Li, Hao Peng +2
Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly…
A Survey on Text Classification: From Shallow to Deep Learning
Qian Li, Hao Peng, Jianxin Li +5
Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented su…
Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
Shen Wang, Jibing Gong, Jinlong Wang +4
Massive open online courses are becoming a modish way for education, which provides a large-scale and open-access learning opportunity for students to grasp the knowledge. To attra…
Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College
Chen Li, Xutan Peng, Hao Peng +4
Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups. For better model performance, previous studies learn to tr…