How Data Scientists Use Knowledge Graphs to Power Recommendation Engines
from The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations Hosted by Fexingo
In this episode of The Data Science Podcast, Lucas and Luna explore how knowledge graphs are transforming recommendation engines beyond collaborative filtering. They dive into a real example: how a music streaming service uses knowledge graphs to recommend obscure tracks that collaborative filtering would miss. The hosts break down the graph structure, the role of graph embeddings, and how hybrid approaches combine the best of both worlds. They also discuss the practical challenges of building and maintaining knowledge graphs at scale, from data quality to compute costs. If you've ever wondered why your recommendations sometimes feel surprisingly good, this episode explains the mechanics behind it. Tune in to understand how knowledge graphs are becoming a key tool in the modern data scientist's toolkit. #KnowledgeGraphs #RecommendationEngines #GraphEmbeddings #DataScience #MachineLearning #CollaborativeFiltering #GraphNeuralNetworks #DataEngineering #MusicStreaming #Tech #DataDriven #FexingoBusiness #BusinessPodcast #Analytics #AI #ML #DataMining #RecommenderSystems Keep every episode free: buymeacoffee.com/fexingo
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