1 code implementation • 15 Oct 2022 • Jiaqi Sun, Lin Zhang, Shenglin Zhao, Yujiu Yang
Graph neural networks (GNNs) hold the promise of learning efficient representations of graph-structured data, and one of its most important applications is semi-supervised node classification.
no code implementations • 28 Sep 2022 • Xinni Zhang, Yankai Chen, Cuiyun Gao, Qing Liao, Shenglin Zhao, Irwin King
Incorporating knowledge graphs (KGs) as side information in recommendation has recently attracted considerable attention.
no code implementations • 10 Oct 2020 • Xixian Chen, Haiqin Yang, Shenglin Zhao, Michael R. Lyu, Irwin King
Estimating covariance matrix from massive high-dimensional and distributed data is significant for various real-world applications.
no code implementations • 10 Oct 2020 • Xixian Chen, Haiqin Yang, Shenglin Zhao, Michael R. Lyu, Irwin King
Data-dependent hashing methods have demonstrated good performance in various machine learning applications to learn a low-dimensional representation from the original data.
no code implementations • 27 May 2019 • Jiani Zhang, Xingjian Shi, Shenglin Zhao, Irwin King
We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario.
no code implementations • CIKM 2017 • Jiajun Cheng, Shenglin Zhao, Jiani Zhang, Irwin King, Xin Zhang, Hui Wang
However, the prior work only attends to the sentiment information and ignores the aspect-related information in the text, which may cause mismatching between the sentiment words and the aspects when an unrelated sentiment word is semantically meaningful for the given aspect.
no code implementations • 3 Jul 2016 • Shenglin Zhao, Irwin King, Michael R. Lyu
Then, we present a comprehensive review in three aspects: influential factors for POI recommendation, methodologies employed for POI recommendation, and different tasks in POI recommendation.