Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity
and cold-start problems present in Single-Domain Sequential Recommendation (SDSR) …

Accurately predicting individual-level infection state is of great value since its essential role
in reducing the damage of the epidemic. However, there exists an inescapable risk of …

Diabetic Retinopathy (DR) is a major cause of blindness worldwide. Early detection and
treatment are crucial to prevent vision loss, making accurate and timely diagnosis critical …

This paper proposes a novel 3D point cloud segmentation network called EDGCNet.
Structurally, the network combines the encoder–decoder structure and graph convolution to …

With the rapid development of Large Language Models (LLMs), various explorations have
arisen to utilize LLMs capability of context understanding on recommender systems. While …

Graph Neural Networks (GNNs) have received considerable attention on graph-structured
data learning for a wide variety of tasks. The well-designed propagation mechanism, which …

Existing sequential POI recommendation methods overlook a fact that each city exhibits
distinct characteristics and totally ignore the city signature. In this study, we claim that city …

Providing potential next point-of-interest (POI) suggestions for users has become a
prominent task in location-based social networks, which receives more and more attention …

In recent years, self-attentive sequential learning models have surpassed conventional
collaborative filtering techniques in next-item recommendation tasks. However, Euclidean …

Graph Neural Networks (GNNs) have demonstrated effectiveness in collaborative filtering
tasks due to their ability to extract powerful structural features. However, combining the …