用图模型提升音乐推荐精准度，融合音频特征与用户偏好
Graphs are everywhere -- Psst! In Music Recommendation too
- 将音频特征MFCC与用户行为构建图结构，用GCN等模型学习嵌入
- 在多个真实数据集上，图模型比纯MFCC或协同过滤准确率更高
- 适合对个性化音乐推荐和图神经网络感兴趣的开发者
近年来，图结构在推荐系统中日益重要。本文研究图卷积网络（GCN）、GraphSAGE和图注意力网络（GT）在音乐推荐中的表现，通过融合梅尔频率倒谱系数（MFCC）与用户-音乐交互关系构建图结构，学习更具表达力的嵌入表示。在多个真实世界音乐数据集上的实证评估表明，这些图模型在预测用户对特定音乐类型的偏好、生成相关推荐方面显著优于仅依赖MFCC或协同过滤的传统方法，验证了图嵌入在捕捉音乐数据隐含关联、增强特征表示方面的有效性，为构建更智能、个性化的音乐推荐系统提供了新路径。
原文摘要 · Abstract (English)
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
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