Developing and Movie Recommendation System in R

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Harvard Stats 135 Final Presentation in R.

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Developing and Movie Recommendation System in R

  1. 1. DEVELOPING A MOVIE RECOMMENDATION SYSTEM IN R FINAL PROJECT - STATS 135 - JODY SCHECHTER
  2. 2. OUTLINE • • • • • • Data Overview Exploratory data analysis Overview of model Steps in R Star Wards Example Demonstration R Tricks
  3. 3. • M o v i e l e n s d a
  4. 4. Positive Correlation between the number of reviews and the average rating, as expected.
  5. 5. • I t e m b a s e d r
  6. 6. • E x p e n s i v e t o
  7. 7. OUTLINE OF STEPS IN R (CONT) • Regularized the scores, reducing variance • Given a movie, found highest similarity scores with other movies • • Also: Created functions that solicit and act on user input Also: Found highest similarity scores for movies in same genre
  8. 8. Average user review largely unchanged by data trimming.
  9. 9. Most movie pairs have fewer than 50 common reviewers. More movies have 10-19 than 0-9 common reviewers.
  10. 10. • e x p a n d . g r i d
  11. 11. • R w o r k s f o r
  12. 12. WORKS CITED Conway, Drew, and John Myles. White. Machine Learning for Hackers. Sebastopol, CA: O'Reilly Media, 2012. Print. Do We Really Need Chocolate Recommendations? 18 Oct. 2013. AC209 Homework Set #4. Harvard University, Cambridge, MA. R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/. "Recommendation System in R." Yhat. N.p., 19 June 2013. Web. 03 Dec. 2013. <http://blog.yhathq.com/posts/recommender-system-inr.html>.

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