By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches using Graph Neural Networks (GNNs), have demonstrated great results in terms of recommendation accuracy. However, accuracy may not always be the most important criterion for evaluating recommender systems' performance, since beyond-accuracy aspects such as recommendation diversity, serendipity, and fairness can strongly influence user engagement and satisfaction. This review paper focuses on addressing these dimensions in GNN-based recommender systems, going beyond the conventional accuracy-centric perspective. We begin by reviewing recent developments in approaches that improve not only the accuracy-diversity trade-off but also promote serendipity and fairness in GNN-based recommender systems. We discuss different stages of model development including data preprocessing, graph construction, embedding initialization, propagation layers, embedding fusion, score computation, and training methodologies. Furthermore, we present a look into the practical difficulties encountered in assuring diversity, serendipity, and fairness, while retaining high accuracy. Finally, we discuss potential future research directions for developing more robust GNN-based recommender systems that go beyond the unidimensional perspective of focusing solely on accuracy. This review aims to provide researchers and practitioners with an in-depth understanding of the multifaceted issues that arise when designing GNN-based recommender systems, setting our work apart by offering a comprehensive exploration of beyond-accuracy dimensions.

翻译：通过向用户提供个性化推荐，推荐系统已成为众多在线平台的核心组成部分。协同过滤，尤其是基于图神经网络（GNN）的图方法，在推荐准确性方面取得了显著成果。然而，准确性并非衡量推荐系统性能的唯一关键标准，因为推荐多样性、意外发现和公平性等超越准确性的维度会显著影响用户参与度和满意度。本综述聚焦于在基于GNN的推荐系统中探讨这些维度，突破传统以准确性为中心的研究视角。我们首先回顾了近期在改进准确性-多样性权衡的同时，提升基于GNN推荐系统意外发现与公平性的方法。随后，讨论了模型开发的不同阶段，包括数据预处理、图构建、嵌入初始化、传播层、嵌入融合、得分计算及训练方法论。此外，我们还深入分析了在保持高准确率的同时确保多样性、意外发现和公平性所面临的实际困难。最后，探讨了未来可能的研究方向，旨在构建更稳健的、超越单一准确性维度的基于GNN的推荐系统。本综述旨在为研究人员和实践者提供关于设计基于GNN推荐系统时出现的多维度问题的深入理解，通过全面探索超越准确性的维度，使我们的工作具有独特价值。