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This interview delves into the use of machine learning in predicting Netflix watch time. Our guest covers a wide range of topics, including data utilization, feature engineering, supervised and unsupervised approaches, and similarity metrics. They also discuss the potential for combining these approaches to create a versatile recommendation system. Additionally, the interview explores challenges related to model deployment, temporal effects, and considerations for retraining. This conversation shows the complexities of machine learning for watch time prediction.

Chapters (Powered by ChapterMe) -
00:00 - Intro
00:29 - Design goal Netflix watch time prediction
01:36 - Access to user data demographic, movie, show features
04:47 - Training model for watch time prediction
09:42 - Sparse matrix, similarity search, metrics
13:12 - Pearson correlation for watch time prediction
15:02 - Unsupervised methods offer advantages over supervised
18:24 - Linear model embeddings simplify learning
19:03 - Deployment issues and model integration
23:16 - Matrices for similarity over latent factor models
24:17 - Issues with supervised hot feature vectors
25:22 - Supervised feature-based prediction using embedding space
27:43 - Outro

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