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 …

To date, graph collaborative filtering (CF) strategies have been shown to outperform pure CF
models in generating accurate recommendations. Nevertheless, recent works have raised …

Generally, items with missing modalities are dropped in multimodal recommendation.
However, with this work, we question this procedure, highlighting that it would further …

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

Recent studies tend to employ Contrastive Learning (CL) methods to facilitate model training
by extracting self-supervised signals to mitigate data sparsity. However, existing CL-based …

In multimodal-aware recommendation, the extraction of meaningful multimodal features is at
the basis of high-quality recommendations. Generally, each recommendation framework …

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

In specific domains like fashion, music, and movie recommendation, the multi-faceted
features characterizing products and services may influence each customer on online …

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

Recommender systems (RSs) provide customers with a personalized navigation experience
within the vast catalogs of products and services offered on popular online platforms …