Music Recommendation System
Music streaming services like Spotify periodically provide their millions of users with curated music recommendations to keep them wanting to come back for more. It is important that these recommendations truly resonate with their users, while also introducing them to novelty that keeps their curiosity alive.
This example creates a music recommendation using mathematical optimization. The solution comprises of a set of artists that likable and diverse, and caters to a user of a music streaming platform. Users’ preferences are learned using collaborative filtering (via matrix factorization) and the artist selection is optimized using an Integer Program. This example illustrates how a prediction model combines with a prescriptive model to create a recommendation that is fine-tuned to a user’s likes.
This modeling tutorial is at the introductory level, where we assume that you know Python and that you have a background on a discipline that uses quantitative methods.
You may find it helpful to refer to the documentation of the Gurobi Python API. This notebook is explained in detail in our webinar on data science and mathematical optimization. You can watch these videos by clicking here.
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For details on licensing or on running the notebooks, see the overview on Modeling Examples
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