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. Previous studies have attempted to address this issue by combining meta-learning with user and item-side information. However, these approaches face inherent challenges in modeling user preference dynamics, particularly for "minor users" who exhibit distinct preferences compared to more common or "major users." To overcome these limitations, we present a novel approach called ClusterSeq, a Meta-Learning Clustering-Based Sequential Recommender System. ClusterSeq leverages dynamic information in the user sequence to enhance item prediction accuracy, even in the absence of side information. This model preserves the preferences of minor users without being overshadowed by major users, and it capitalizes on the collective knowledge of users within the same cluster. Extensive experiments conducted on various benchmark datasets validate the effectiveness of ClusterSeq. Empirical results consistently demonstrate that ClusterSeq outperforms several state-of-the-art meta-learning recommenders. Notably, compared to existing meta-learning methods, our proposed approach achieves a substantial improvement of 16-39% in Mean Reciprocal Rank (MRR).

KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images

Instance-level graph neural network explainers have proven beneficial for explaining such networks on histopathology images. However, there has been few methods that provide model explanations, which are common patterns among samples within the same class. We envision that graph-based histopathological image analysis can benefit significantly from such explanations. On the other hand, current model-level explainers are based on graph generation methods that are not applicable in this domain because of no corresponding image for their generated graphs in real world. Therefore, such explanations are communicable to the experts. 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. Our experiments on various datasets, and based on both quantitative and qualitative measures, demonstrate that the proposed explainer is capable of being a global pattern extractor, which is a fundamental limitation of current instance-level approaches in this domain.