Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

There is an increasing attention on next-item recommendation systems to infer the dynamic
user preferences with sequential user interactions. While the semantics of an item can …

Abstract Recently, Recurrent Neural Network (RNN) solutions for recommender systems
(RS) are becoming increasingly popular. The insight is that, there exist some intrinsic …

Location Based Social Networks (LBSNs) have been widely used as a primary data source
to study the impact of mobility and social relationships on each other. Traditional …

In e-learning recommender systems, interpersonal information between learners is very
scarce, which makes it difficult to apply collaborative filtering (CF) techniques to achieve …

In recommender systems, cold-start issues are situations where no previous events, eg,
ratings, are known for certain users or items. In this paper, we focus on the item cold-start …

Collaborative filtering (CF) algorithms have been widely used to build recommender
systems since they have distinguishing capability of sharing collective wisdoms and …

Location-Based Social Networks (LBSNs) have been widely used as a primary data source
for studying the impact of mobility and social relationships on each other. Traditional …

Hypergraphs provide a natural way of representing group relations, whose complexity
motivates an extensive array of prior work to adopt some form of abstraction and …