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[] Evaluating RecSys parallel to RAGs
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RecSys at Spotify - Building and Evaluating Large-Scale Recommender Systems
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- 1 [] Sanket's preferred coffee
- 2 [] Takeaways
- 3 [] RecSys are RAGs
- 4 [] Evaluating RecSys parallel to RAGs
- 5 [] Music RecSys Optimization
- 6 [] Dealing with cold start problems
- 7 [] Quantity of models in the recommender systems
- 8 [] Radio models
- 9 [] Evaluation system
- 10 [] Infrastructure support
- 11 [] Transfer learning
- 12 [] Vector database features
- 13 [] Listening History Balance
- 14 [26:35 - ] LatticeFlow Ad
- 15 [] The beauty of embeddings
- 16 [] Shift to real-time recommendation
- 17 [] Vector Database Architecture Options
- 18 [] Embeddings drive personalized
- 19 [] Feature Stores vs Vector Databases
- 20 [] Spotify product integration strategy
- 21 [] Staying up to date with new features
- 22 [] Speed vs Relevance metrics
- 23 [] Wrap up
