Tuning in to Personalized Music: A Spotify API-Based Hybrid Recommendation System Integrating Content-Based and Popularity-Based Approaches

This research paper offers a comprehensive approach for constructing a successful music recommendation system that integrates popularity- and content-based approaches. The suggested method intends to improve music discovery for users in an era of expansive music libraries and personalized user experiences. It makes use of the Spotify API to compile thorough track data and employs data preparation methods to guarantee data quality. The recommendation engine is the center of the research. Song recommendations are personalized by a content-based algorithm that bases them on aural features. In addition, a hybrid model balances user preferences and trending music by weighting track popularity by release date. A user-friendly GUI is included to make it easier for users to interact, allowing users to input their favorite music and get recommendations right away. Although the code serves as the basis for a strong recommendation system, later versions can include performance criteria for evaluation, such as accuracy, precision, and recall. This study contributes to the constantly developing field of music technology and user pleasure by providing a foundation for the improvement of music recommendation systems.