In recent years there has been an enormous increase in learning resources available online
through massive open online courses and learning management systems. In this context …

In many e-commerce platforms user communities share product information in the form of
reviews and ratings to help other consumers to make their choices. This study develops a …

Graph neural networks (GNNs), with their promising potential to learn effective graph
representation, have been widely used for recommender systems, in which the given graph …

With the widespread use of social networks, social recommendation algorithms that add
social relationships between users to recommender systems have been widely applied …

Over the past decade, recommendation systems have been one of the most sought after by
various researchers. Basket analysis of online systems' customers and recommending …

In the traditional content-based recommendation method, we usually use the movies users
watched before or rated to represent their profile. However, there are many movies that …

Graph-based recommendation approaches use a graph model to represent the
relationships between users and items, and exploit the graph structure to make …

With the rapid development of network technology and entertainment creation, the types of
movies have become more and more diverse, which makes users wonder how to choose …

Traditional collaborative filtering techniques suffer from the data sparsity problem in practice.
That is, only a small proportion of all items in the recommender system occur in a user's …

Most collaborative filtering recommendation algorithms rely too much on the user's historical
rating data. However, selection bias is common in explicit feedback data, which makes the …