Music Recommendation

Music recommendation systems aim to personalize music listening experiences by suggesting songs or artists users might enjoy. Current research focuses on improving recommendation accuracy and diversity, addressing challenges like the cold-start problem (recommending new music) and mitigating biases (e.g., popularity bias favoring mainstream artists). This involves exploring various model architectures, including graph neural networks, deep Bayesian networks, and multimodal models that integrate audio and text data, often leveraging large language models and contrastive learning techniques. The field's advancements have significant implications for user experience on music streaming platforms and contribute to a broader understanding of music perception, preference, and cultural influence.