Cold-start problem is one of the long-standing challenges in recommender systems,
focusing on accurately modeling new or interaction-limited users or items to provide better …

Purpose This paper addresses the limitations of current graph neural network-based
recommendation systems, which often neglect the integration of side information and the …

The field of face recognition (FR) has undergone significant advancements with the rise of
deep learning. Recently, the success of unsupervised learning and graph neural networks …

Cross-domain recommendation (CDR) extends conventional recommender systems by
leveraging user-item interactions from dense domains to mitigate data sparsity and the cold …

Cross-Domain Recommendation has been popularly studied to resolve data sparsity
problem via leveraging knowledge transfer across different domains. In this paper, we focus …

Recent generative methods have revolutionized the way of human motion synthesis, such
as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and …

Cross-domain sequential recommendation (CDSR) is proposed to alleviate the data sparsity
issue while capturing users' sequential preferences. However, most existing methods do not …