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：图神经网络（GNNs）近年来在推荐系统中备受关注。通过将用户-物品矩阵表示为二分无向图，GNNs展现出捕捉用户与物品间短距离与长距离交互的潜力，从而比传统推荐方法学习到更精准的偏好模式。与以往同类主题教程不同，本教程旨在呈现并审视图神经网络在推荐系统中的三个关键特性：(i) 现有最先进方法的可复现性，(ii) 图拓扑特征对这些模型性能的潜在影响，以及(iii) 在从零训练特征或利用预训练嵌入作为额外物品信息（如多模态特征）时学习节点表征的策略。目标是为该领域提供三个新颖的理论与实践视角——这些视角当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。