Built a recommender system based on user-item pairing in the Million Song Dataset. This recommender system using the alternating least-squares algorithm both in PySpark for a distributed implementation, and with lenskit for a local implementation. In addition, we provide a baseline popularity model which can provide recommendations for cold-start users.
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