Collaborative Filteringin iCAMP

Max WellingProfessor 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=

Back to The Movies: Datam

ovie

s (+/

- 17,

770)

users (+/- 240,000)

total of +/- 400,000,000 nonzero entries(99% sparse)

4

Demo Matlabm

ovie

s (+/

- 17,

770)

users (+/- 240,000)

total of +/- 400,000,000 nonzero entries(99% sparse)

users (+/- 240,000)

mov

ies (

+/- 1

7,77

0) x

K

K

“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, stock-portfolio management, and more.

• But most of all: this will be fun!