In recent years, graphs have gained prominence across various domains, especially in recommendation systems. Within the realm of music recommendation, graphs play a crucial role in enhancing genre-based recommendations by integrating Mel-Frequency Cepstral Coefficients (MFCC) with advanced graph embeddings. This study explores the efficacy of Graph Convolutional Networks (GCN), GraphSAGE, and Graph Transformer (GT) models in learning embeddings that effectively capture intricate relationships between music items and genres represented within graph structures. Through comprehensive empirical evaluations on diverse real-world music datasets, our findings consistently demonstrate that these graph-based approaches outperform traditional methods that rely solely on MFCC features or collaborative filtering techniques. Specifically, the graph-enhanced models achieve notably higher accuracy in predicting genre-specific preferences and offering relevant music suggestions to users. These results underscore the effectiveness of utilizing graph embeddings to enrich feature representations and exploit latent associations within music data, thereby illustrating their potential to advance the capabilities of personalized and context-aware music recommendation systems. Keywords: graphs, recommendation systems, neural networks, MFCC

翻译：近年来，图结构在各个领域日益受到重视，尤其在推荐系统中表现突出。在音乐推荐领域，通过将梅尔频率倒谱系数（MFCC）与先进的图嵌入技术相结合，图在增强基于流派的推荐方面发挥着关键作用。本研究探讨了图卷积网络（GCN）、GraphSAGE 和图 Transformer（GT）模型在学习嵌入表示方面的效能，这些嵌入能有效捕捉图结构中音乐条目与流派之间复杂的关联关系。通过对多个真实世界音乐数据集进行全面的实证评估，我们的研究结果一致表明：这些基于图的方法优于仅依赖 MFCC 特征或协同过滤技术的传统方法。具体而言，图增强模型在预测用户特定流派偏好及提供相关音乐建议方面实现了显著更高的准确率。这些结果印证了利用图嵌入来丰富特征表示并挖掘音乐数据中潜在关联的有效性，从而展现了其在提升个性化与情境感知音乐推荐系统能力方面的潜力。关键词：图，推荐系统，神经网络，MFCC