79 citations · 164 across the 7 of their papers we have counts for
7 papers
Improving Your Graph Neural Networks: A High-Frequency Booster
Jiaqi Sun, Lin Zhang, Shenglin Zhao +1
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 cla…
Knowledge-aware Neural Networks with Personalized Feature Referencing for Cold-start Recommendation
Xinni Zhang, Yankai Chen, Cuiyun Gao +3
Incorporating knowledge graphs (KGs) as side information in recommendation has recently attracted considerable attention. Despite the success in general recommendation scenarios, p…
Effective Data-aware Covariance Estimator from Compressed Data
Xixian Chen, Haiqin Yang, Shenglin Zhao +2
Estimating covariance matrix from massive high-dimensional and distributed data is significant for various real-world applications. In this paper, we propose a data-aware weighted…
Making Online Sketching Hashing Even Faster
Xixian Chen, Haiqin Yang, Shenglin Zhao +2
Data-dependent hashing methods have demonstrated good performance in various machine learning applications to learn a low-dimensional representation from the original data. However…
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
Jiani Zhang, Xingjian Shi, Shenglin Zhao +1
We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, e…
A Survey of Point-of-interest Recommendation in Location-based Social Networks
Shenglin Zhao, Irwin King, Michael R. Lyu
Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs). Due to the importance of PO…
GT-SEER: Geo-Temporal SEquential Embedding Rank for Point-of-interest Recommendation
Shenglin Zhao, Tong Zhao, Irwin King +1
Point-of-interest (POI) recommendation is an important application in location-based social networks (LBSNs), which learns the user preference and mobility pattern from check-in se…