In this episode, Lucas and Luna dive into the collaborative filtering algorithm behind Spotify's Discover Weekly. They break down how matrix factorization learns user preferences from sparse listening data, using the example of a user who jumps from classical to indie rock. They discuss cold-start problems, the trade-off between exploration and exploitation, and why your weekly playlist feels eerily accurate. Along the way, they touch on implicit feedback signals like skip rate and repeat listens, and compare Spotify's approach to those of Pandora and Apple Music. If you've ever wondered how a machine can predict your next favorite song, this episode explains the math and the engineering choices that make it work.

#Spotify #DiscoverWeekly #CollaborativeFiltering #MatrixFactorization #RecommendationSystems #MachineLearning #Personalization #ColdStartProblem #ExplorationVsExploitation #ImplicitFeedback #DataScience #Technology #FexingoBusiness #BusinessPodcast #Podcast #MusicRecommendation #UserBehavior #Algorithms

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