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Found 7 papers, 1 papers with code
Improving Your Graph Neural Networks: A High-Frequency Booster
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.
Knowledge-aware Neural Networks with Personalized Feature Referencing for Cold-start Recommendation
Incorporating knowledge graphs (KGs) as side information in recommendation has recently attracted considerable attention.
Effective Data-aware Covariance Estimator from Compressed Data
Estimating covariance matrix from massive high-dimensional and distributed data is significant for various real-world applications.
Making Online Sketching Hashing Even Faster
Data-dependent hashing methods have demonstrated good performance in various machine learning applications to learn a low-dimensional representation from the original data.
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
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.
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network
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.
A Survey of Point-of-interest Recommendation in Location-based Social Networks
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.
