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.

翻译：近年来，图神经网络（GNN）在推荐系统中日益受到重视。通过将用户-项目矩阵表示为二分无向图，GNN展现了捕捉用户-项目短程与长程交互的能力，从而比传统推荐方法更准确地学习偏好模式。与以往同类主题教程不同，本教程旨在阐述并探讨表征推荐系统中GNN的三个关键维度：（i）先进方法的可复现性，（ii）图拓扑特征对模型性能的潜在影响，以及（iii）在从头训练特征或利用预训练嵌入（如多模态特征）作为附加项目信息时的节点表示学习策略。目标是为该领域提供三个新颖的理论与实践视角——这些视角目前在图学习领域备受争议，却在推荐系统背景下长期被忽视。