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Predicting Movie Ratings And Recommender Systems – A Monograph

Predicting Movie Ratings And Recommender Systems – A Monograph

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

Predicting Movie Ratings And Recommender Systems - A Monograph by A. Paterek

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