Ever wonder how Spotify knows what song you want to hear next? This episode unpacks the collaborative filtering algorithm that powers Discover Weekly and Release Radar. Lucas walks through the math behind user-item matrices, matrix factorization, and how Spotify tackles the cold-start problem for new users and new artists. Luna probes the trade-off between personalization and the filter bubble, and the hosts discuss why Spotify's A-B testing showed that a 70/30 blend of familiar and novel tracks maximizes user satisfaction. Specific data: how the 'Taste Profile' matrix handles 100 million active users and 70 million tracks, and why the algorithm still recommends a song you already skipped.

#Spotify #CollaborativeFiltering #RecommendationEngine #MatrixFactorization #DiscoverWeekly #ColdStartProblem #FilterBubble #ABTesting #UserItemMatrix #Personalization #DataScience #MachineLearning #Technology #FexingoBusiness #BusinessPodcast #DataDriven #LucasAndLuna #TheDataSciencePodcast

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