How would you design a recommendation system that tracks a user's music listening behavior and uses that data to suggest new songs or artists that they might enjoy? What features or characteristics would you use to model the user's listening history and how would you incorporate those into your recommendation algorithm?

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Anonymous · Accepted Answer

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.

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