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ClusterSeq: Enhancing Sequential Recommender Systems with Clustering based Meta-Learning
In practical scenarios, the effectiveness of sequential recommendation systems is hindered by the user cold-start problem, which arises due to limited interactions for accurately determining user preferences.
KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images
To follow this vision, we developed KS-GNNExplainer, the first instance-level graph neural network explainer that leverages current instance-level approaches in an effective manner to provide more informative and reliable explainable outputs, which are crucial for applied AI in the health domain.
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