Heterogeneous Evolution Network Embedding with Temporal Extension for Intelligent Tutoring Systems
Graph embedding (GE) aims to acquire low-dimensional node representations while
maintaining the graph's structural and semantic attributes. Intelligent tutoring systems (ITS) …
maintaining the graph's structural and semantic attributes. Intelligent tutoring systems (ITS) …
Introduction to the special section on graph technologies for user modeling and recommendation, part 2
As a powerful data structure that represents the relationships among data objects, graph-
structure data is ubiquitous in real-world applications. Learning on graph-structure data has …
structure data is ubiquitous in real-world applications. Learning on graph-structure data has …
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning
Recent advancements in graph neural networks (GNNs) and heterogeneous GNNs
(HGNNs) have advanced node embeddings and relationship learning for various tasks …
(HGNNs) have advanced node embeddings and relationship learning for various tasks …
Feature-enhanced embedding learning for heterogeneous collaborative filtering
Heterogeneous information network (HIN) has recently been receiving increasing attention
in recommender systems due to its practicability in depicting data heterogeneity. The rich …
in recommender systems due to its practicability in depicting data heterogeneity. The rich …