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Explaining Dynamic Graph Neural Networks via Relevance Back-propagation
Graph Neural Networks (GNNs) have shown remarkable effectiveness in capturing abundant information in graph-structured data.
Multiple Interest and Fine Granularity Network for User Modeling
User modeling plays a fundamental role in industrial recommender systems, either in the matching stage and the ranking stage, in terms of both the customer experience and business revenue.
Deep Kernel Learning via Random Fourier Features
On small datasets (less than 1000 samples), for which deep learning is generally not suitable due to overfitting, our method achieves superior performance compared to advanced kernel methods.
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