How Data Scientists Build Recommendation Systems That Actually Work
from The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations Hosted by Fexingo
In this episode, Lucas and Luna dive into the practical side of recommendation systems, focusing on how data scientists at companies like Spotify and Netflix move beyond simple collaborative filtering. They explore the evolution from matrix factorization to two-tower neural networks, the critical role of negative sampling, and why offline metrics often don't match real-world success. Specific numbers include a 2017 Netflix prize insight and a 20 percent improvement in user engagement from Spotify's deep learning model. The conversation also touches on the cold start problem and how to handle new users or items. A must-listen for anyone building or evaluating recommender systems in production. #RecommendationSystems #MachineLearning #CollaborativeFiltering #MatrixFactorization #TwoTowerNetworks #NegativeSampling #NetflixPrize #Spotify #ColdStart #DataScience #Technology #FexingoBusiness #BusinessPodcast #DataDriven #Personalization #DeepLearning #OfflineMetrics #UserEngagement Keep every episode free: buymeacoffee.com/fexingo
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