  Graph neural networks (GNNs) have gained prominence in recommendation systems
in recent years. By representing the user-item matrix as a bipartite and
undirected graph, GNNs have demonstrated their potential to capture short- and
long-distance user-item interactions, thereby learning more accurate preference
patterns than traditional recommendation approaches. In contrast to previous
tutorials on the same topic, this tutorial aims to present and examine three
key aspects that characterize GNNs for recommendation: (i) the reproducibility
of state-of-the-art approaches, (ii) the potential impact of graph topological
characteristics on the performance of these models, and (iii) strategies for
learning node representations when training features from scratch or utilizing
pre-trained embeddings as additional item information (e.g., multimodal
features). The goal is to provide three novel theoretical and practical
perspectives on the field, currently subject to debate in graph learning but
long been overlooked in the context of recommendation systems.
