Harvard Stats 135 Final Presentation in R.

1. DEVELOPING A MOVIE
RECOMMENDATION
SYSTEM IN R
FINAL PROJECT - STATS 135 - JODY SCHECHTER

2. OUTLINE
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Data Overview
Exploratory data analysis
Overview of model
Steps in R
Star Wards Example
Demonstration
R Tricks

3. •
M
o
v
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l
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n
s
d
a

5. Positive Correlation between the
number of reviews and the average
rating, as expected.

6. •
I
t
e
m
b
a
s
e
d
r

7. •
E
x
p
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v
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t
o

8. OUTLINE OF STEPS IN R (CONT)
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Regularized the scores, reducing variance
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Given a movie, found highest similarity scores with other
movies
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Also: Created functions that solicit and act on user input
Also: Found highest similarity scores for movies in same
genre

9. Average user review largely unchanged by data trimming.

10. Most movie pairs have fewer than
50 common reviewers. More
movies have 10-19 than 0-9 common
reviewers.

13. •
e
x
p
a
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d
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g
r
i
d

14. •
R
w
o
r
k
s
f
o
r

15. 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>.