2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2023
Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks
Abdalgader Abubaker, Takanori Maehara, Madhav Nimishakavi +1
Recently, pretraining methods for the Graph Neural Networks (GNNs) have been successful at learning effective representations from unlabeled graph data. However, most of these meth…
cs.IR2023★ 2 cited
Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?
Davide Buffelli, Ashish Gupta, Agnieszka Strzalka +1
Recommender systems have become fundamental building blocks of modern online products and services, and have a substantial impact on user experience. In the past few years, deep le…