Expert Answer
Anonymous
To design a music recommendation system that tracks user listening behavior, I would build a hybrid model combining collaborative filtering and content-based filtering. The system would log key features from a user's listening history, such as song play frequency, time of day, session duration, skipped songs, and liked or saved tracks. Additionally, it would extract audio and metadata features from each song—such as genre, tempo, mood, and artist similarity—using services like Spotify's audio features API or signal processing techniques. Collaborative filtering would uncover hidden patterns by comparing users with similar listening habits, while content-based filtering would ensure recommendations align with a user's personal preferences. By integrating both models, the system can deliver personalized and diverse music suggestions, adapting over time as user behavior evolves.
