Collaborative Filtering
in iCAMP
Max Welling
Professor of Computer Science & Statistics
Example I: Movie Recommendation
http://www.netflix.com/RecommendationsHome?lnkctr=mh2rh&lnkce=sntRc
Example II: Book Recommendation
http://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0120884070/ref=sr_1_1?ie=UTF8&s=books&qid=1273092289&sr=1-1
Example III: Internet Search
http://www.google.com/search?hl=en&client=firefox-a&hs=gSR&rls=org.mozilla%3Aen-US%3Aofficial&q=max+welling&aq=f&aqi=g2g-m1&aql=&oq=&gs_rfai=
movies (+/- 17,770)
Back to The Movies: Data
total of +/- 400,000,000 nonzero entries
(99% sparse)
4
users (+/- 240,000)
movies (+/- 17,770)
Demo Matlab
total of +/- 400,000,000 nonzero entries
(99% sparse)
users (+/- 240,000)

movies (+/- 17,770)
K
x
K
users (+/- 240,000)
“K” is the number of factors, or topics.
Conclusion
• We will implement a number of collaborative filtering
algorithms in matlab.
• You will learn: Clustering; Matrix factorization & Principal
Components Analysis; Regression; Classification: naive
Bayes classifier, decision trees, neural networks
• We will work with real world data from netflix, stockportfolio management, and more.
• But most of all: this will be fun!