Recent popularity surrounds large AI language models due to their impressive natural
language capabilities. They contribute significantly to language-related tasks, including …

Knowledge graphs (KG) have been proven to be a powerful source of side information to
enhance the performance of recommendation algorithms. Their graph-based structure …

Graph contrastive learning has emerged as a powerful technique for dealing with graph
noise and mining latent information in networks, that has been widely applied in GNN-based …

Bundle recommendation approaches offer users a set of related items on a particular topic.
The current state-of-the-art (SOTA) method utilizes contrastive learning to learn …

Recently, graph neural networks (GNNs)-based recommender systems have encountered
great success in recommendation. As the number of GNNs approaches rises, some works …

This tutorial provides an interdisciplinary overview about the topics of fairness, non-
discrimination, transparency, privacy, and security in the context of recommender systems …

Traditional collaborative filtering (CF)-based recommendation systems are often challenged
by data sparsity. The recent research has recognized the potential of integrating new …

The recent integration of Graph Neural Networks (GNNs) into recommendation has led to a
novel family of Collaborative Filtering (CF) approaches, namely Graph Collaborative …

This tutorial provides an interdisciplinary overview of fairness, non-discrimination,
transparency, privacy, and security in the context of recommender systems. According to …

In the era of daily information overload, personalized retrieval applications represent a
crucial solution to provide suggestions to users. The research and industrial community …