20-colfiltering

20-colfiltering - Collaborative Filtering Xifeng Y Xif Yan...

Info iconThis preview shows pages 1–8. Sign up to view the full content.

View Full Document Right Arrow Icon
Collaborative Filtering Xifeng Yan Computer Science niversity of California at Santa Barbara University of California at Santa Barbara
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Department of Computer Science Collaborative Filtering Data Mining | University of California at Santa Barbara 2
Background image of page 2
Department of Computer Science Everyday Examples of Collaborative Filtering. .. Common insight : personal tastes are correlated : If Alice and Bob both like X and Alice likes Y then Bob is more ely to like Y likely to like Y especially (perhaps) if Bob knows Alice Most of slides adapted from W. Cohen Tutorial Data Mining | University of California at Santa Barbara 3
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Department of Computer Science Problem Setting Airplane Matrix Room with a View ... Hidalgo comedy action romance ... action Movie Title Movie Type Customer Joe 27,M,70k 11 0 1 Carol 53,F,20k 0 ... Kuma r 25,M,22k 10 0 1 U a 48,M,81k 0 1 ? ? ? What we can learn from this dataset? 1. Find a group of persons sharing the common interest? 2. Find a group of movies having a similar customer base? ecommend a movie to a customer Data Mining | University of California at Santa Barbara 4 3. Recommend a movie to a customer
Background image of page 4
Department of Computer Science Solutions Find a group of persons sharing the common interest? – Frequent Pattern, Clustering Find a group of movies having a similar set of fans? –Frequent Pattern, Clustering Recommend a movie to a customer - Classification Airplane Matrix Room with a View ... Hidalgo comed ya c tion romance ... action Joe 27,M,70k 11 0 1 Carol 53,F,20k 0 ... Kuma r 25,M,22k 10 0 1 Data Mining | University of California at Santa Barbara 5 U a 48,M,81k 01 ?? ?
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Department of Computer Science Collaborative Filtering As Voting v i,j = vote of user i on item j I i = items for which user i has voted Mean vote for i is Predicted vote for “active user” a is weighted sum weights of n similar users normalizer Data Mining | University of California at Santa Barbara 6
Background image of page 6
Department of Computer Science Weighting Methods K-nearest neighbor eighbors( else 0 ) neighbors( if 1 ) , ( a i i a w Pearson correlation coefficient (Resnick ’94,
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 8
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 01/09/2012 for the course CS CS273 taught by Professor Xifengyan during the Spring '11 term at UCSB.

Page1 / 22

20-colfiltering - Collaborative Filtering Xifeng Y Xif Yan...

This preview shows document pages 1 - 8. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online