融合多模态与社交关系，让音乐推荐更懂情感和社群
Socially Aware Music Recommendation: A Multi-Modal Graph Neural Networks for Collaborative Music Consumption and Community-Based Engagement
- 用图神经网络整合歌词、音频、视觉与用户社交关系
- 在多个数据集上显著优于现有推荐方法
- 适合关注情感化与社交化推荐的开发者
本研究提出一种新型多模态图神经网络（MM-GNN）框架，用于社会感知的音乐推荐，旨在提升个性化并促进社区参与。该模型采用无融合的深度互学习策略，对歌词、音频和视觉信息的模态特异性表示进行对齐，同时对缺失模态具有鲁棒性。构建异构图结构以捕捉用户-歌曲交互与用户-用户社交关系，实现个体偏好与社会影响的融合。此外，基于声学与文本信号的情绪嵌入有助于实现情感一致的推荐。在基准数据集上的实验表明，MM-GNN 在多项性能指标上显著优于现有最先进方法。消融实验证实了各组件的关键作用，验证了该框架在提供准确且具社会上下文的音乐推荐方面的有效性。
原文摘要 · Abstract (English)
This study presents a novel Multi-Modal Graph Neural Network (MM-GNN) framework for socially aware music recommendation, designed to enhance personalization and foster community-based engagement. The proposed model introduces a fusion-free deep mutual learning strategy that aligns modality-specific representations from lyrics, audio, and visual data while maintaining robustness against missing modalities. A heterogeneous graph structure is constructed to capture both user-song interactions and user-user social relationships, enabling the integration of individual preferences with social influence. Furthermore, emotion-aware embeddings derived from acoustic and textual signals contribute to emotionally aligned recommendations. Experimental evaluations on benchmark datasets demonstrate that MM-GNN significantly outperforms existing state-of-the-art methods across various performance metrics. Ablation studies further validate the critical impact of each model component, confirming the effectiveness of the framework in delivering accurate and socially contextualized music recommendations.
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