Recommender systems, in real-world circumstances, tend to limit user exposure to certain
topics and to overexpose them to others to maximize performance. However, repeated …

Prior solutions on session-based recommendation (SBR) are mainly limited by two major
issues:(1) the sequence and transition relationships of items need further integration;(2) the …

Session-based recommendation uses the click history of anonymous users to recommend
the next possible click item for a given point in time. GNN-based models have returned …

Major Depressive Disorder (MDD) is a common mental illness resulting in immune disorders
and even thoughts of suicidal behavior. Neuroimaging techniques serve as a quantitative …

Session-based recommendation uses short interaction sequences of anonymous users to
predict the next item most likely to be clicked, and many methods have been proposed …

Session-based recommendation (SBR) predicts the next item clicked by anonymous users
based on the given sessions. Nowadays, numerous SBR models merge global information …

Session-based recommendation uses past clicks and interaction sequences from
anonymous users to predict the next item most likely to be clicked. Predicting the user's …

A session-based recommendation system (SRS) tries to predict the next possible choice of
anonymous users. In recent years, graph neural network (GNN) models have been …

The recommendation system (RS) on the strength of Graph Neural Networks (GNN)
perceives a user-item interaction graph after collecting all items the user has interacted with …

In a high variety of application domains, the amount of data generated daily has grown more
and more over time to the point that its use now exceeds the computational capacity of …