Recommender systems can strongly influence which information we see online, eg, on
social media, and thus impact our beliefs, decisions, and actions. At the same time, these …

The success of graph neural network-based models (GNNs) has significantly advanced
recommender systems by effectively modeling users and items as a bipartite, undirected …

Recommender systems have become important tools in the daily life of human beings since
they are powerful to address information overload, and discover relevant and useful items …

In recent years, there has been an increasing recognition that when machine learning (ML)
algorithms are used to automate decisions, they may mistreat individuals or groups, with …

In domains such as fashion, music, food, and micro-video recommendation, items'
representation can be suitably enhanced through multimodal side information (extracted …

Recent developments in recommendation have harnessed the collaborative power of graph
neural networks (GNNs) in learning users' preferences from user-item networks. Despite …

Multimodal-aware recommender systems (MRSs) exploit multimodal content (eg, product
images or descriptions) as items' side information to improve recommendation accuracy …

Although beyond-accuracy metrics have gained attention in the last decade, the accuracy of
recommendations is still considered the gold standard to evaluate Recommender Systems …

Diffusion-based recommender systems have recently proven to outperform traditional
generative recommendation approaches, such as variational autoencoders and generative …

In recent years, graph neural networks (GNNs) have become a popular tool to improve the
accuracy and performance of recommender systems. Modern recommender systems are not …