Evolution and Fairness Challenges of Content-Based and Multimodal Music Recommendation Systems

The contradiction between massive music libraries and users' limited attention spans has become prominent, making recommendation systems a core bridge connecting content and consumers. The technological advancement of music recommendation systems, which is evolving from traditional collaborative filtering to multimodal deep learning, is the main topic of this study. It also examines the difficulties in algorithmic fairness that result from these technological advancements. This study divides and contrasts previous research using fundamental technologies and technical stages. Research indicates that technology has made progress in using state-space models (SSMs) for continuous-time modeling; the improvement in technical accuracy is accompanied by increased popularity bias, gender imbalance, and two-sided market unfairness, and existing offline evaluation metrics are insufficient to reflect long-term user satisfaction. This paper provides a theoretical basis and reference for developing a next-generation music recommendation system that balances accuracy, diversity, and social responsibility.