Building Recommender system
summary
Covers recommender-system techniques for predicting sparse user-item matrices, showing applications such as conference audience clustering, joke recommendation, and Kaggle competitions. Explains and implements both collaborative filtering and content-based filtering, with the underlying mathematical approaches, and references the Netflix Prize as motivating context.
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chapters (11)
- 0:00Intro
- 0:42The Recommender problem
- 2:26What is the output of a Recommendation Engine?
- 3:30How is it different
- 4:00Traditional Methods
- 6:17Collaborative Fitering
- 8:39Content Based Recommendations
- 10:16Pros/Cons of Content Based Approach
- 10:46Factor Model for Recommendation System
- 14:06When to use What?
- 14:46What Works.. (1/2)
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