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In this presentation I introduce various Machine Learning methods that we utilize for music recommendations and discovery at Spotify. Specifically, I focus on Implicit Matrix Factorization for Collaborative Filtering, how to implement a small scale version using python, numpy, and scipy, as well as how to scale up to 20 Million users and 24 Million songs using Hadoop and Spark.
In this presentation I introduce various Machine Learning methods that we utilize for music recommendations and discovery at Spotify. Specifically, I focus on Implicit Matrix Factorization for Collaborative Filtering, how to implement a small scale version using python, numpy, and scipy, as well as how to scale up to 20 Million users and 24 Million songs using Hadoop and Spark.
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