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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?

Data Scientist

Square

Google

Spotify

TikTok

Uber

Meta

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?
How do you envision a system that recommends music tailored to a user's listening patterns, and what attributes of their history would you analyze?
What architecture would you propose for a recommendation engine that adapts to a user's musical preferences, and how would you integrate their listening traits?
Can you draft a design for a system that makes music suggestions derived from user behavior, and what user history aspects would you utilize?
How would you craft a recommendation service that reflects a user's auditory preferences, and what historical listening details would you leverage?
What framework would you suggest for a recommendation tool that considers a user's song choices, and how would these choices inform your algorithm?
What's your concept for a recommendation algorithm based on user's listening activity, and which listening history features would you model?
How would you construct a recommendation feature that aligns with user's music choices, and what listening behaviors would you factor in?
What's your blueprint for a music recommendation system based on user listening habits, and which features of their listening history would you consider for suggestions?

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Interview question asked to Data Scientists interviewing at Kraken, Google, Marqeta and others: 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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