A 195-page monograph by a top-1% Netflix Prize contestant. Learn about the famous machine learning competition. Improve your machine learning skills. Learn how to build recommender systems.
What’s inside:
- introduction to predictive modeling,
- a comprehensive summary of the Netflix Prize,
- detailed description of my top-50 Netflix Prize solution predicting movie ratings,
- summary of methods published by others – RMSE’s from different papers listed and grouped in one place,
- detailed analysis of matrix factorizations / regularized SVD,
- how to interpret the factorization results – new, most informative movie genres (see how I use it here and here),
- how to adapt the algorithms developed for the Netflix Prize to calculate good quality personalized recommendations,
- dealing with the cold-start: simple content-based augmentation,
- description of two rating-based recommender systems realized by me (see one of them in action),
- commentary on everything: novel and unique insights, know-how from >9 years of practicing and analysing predictive modeling.
Must-have for:
- people interested in a comprehensive summary of the developments around the Netflix Prize contest,
- for people developing recommender systems based on ratings – the publication can potentially save you hundreds of hours of work, and maybe give a tech edge over the competition.
Predicting Movie Ratings And Recommender Systems - A Monograph
by A. Paterek (PDF) – 195 pages, 2MB
