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，图神经网络在推荐系统中日益受到重视。通过将用户-物品矩阵表示为二分且无向的图，图神经网络展现出捕捉短程与长程用户-物品交互能力的潜力，从而相比传统推荐方法可学习到更精确的偏好模式。与以往围绕同一主题的教程不同，本教程旨在展示并探讨表征面向推荐的图神经网络的三个关键方面：（i）现有最优方法的可复现性，（ii）图拓扑特征对这些模型性能的潜在影响，以及（iii）在从零开始训练特征或利用预训练嵌入（如多模态特征）作为额外物品信息时学习节点表示的策略。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，但在推荐系统语境中长期被忽视。