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CS294 Collaborative Filtering Algorithms

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Collaborative Filtering CS294 Practical Machine Learning Week 14 Pat Flaherty [email_address]
Amazon.com Book Recommendations ,[object Object],[object Object]
Google PageRank ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],http://en.wikipedia.org/wiki/PageRank
Netflix Movie Recommendation http://www.netflixprize.com/ “ The Netflix Prize seeks to substantially improve the accuracy of predictions about how much someone is going to love a movie based on their movie preferences.”
Collaborative filtering ,[object Object],[object Object],[object Object],[object Object],http://en.wikipedia.org/wiki/Collaborative_filtering User-centric Item-centric Collaborative filtering : filter information based on user preference Information filtering : filter information based on content
Data Structures ,[object Object],[object Object],[object Object],[object Object],[object Object],sparse rating / co-occurance matrix user 1
Today we’ll talk about… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Classification Each item y=1,…,M gets its own classifier. Some users will not have recorded a rating for item y - discard those users from the training set when learning the classier for item y.
Nearest Neighbors ,[object Object],[object Object],[object Object],[object Object]
Weighted kNN Estimation ,[object Object],[object Object],[object Object],Majority Vote Weighted Mean
kNN Similarity Metrics ,[object Object],[object Object],[object Object],for each user w qu = correlation between query user and each data set user (u) end for sort weights vector for each item end for
kNN Complexity ,[object Object],[object Object],[object Object],[object Object]
Data set size examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Naïve Bayes Classifier ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],r 1 r M r y
Naïve Bayes Algorithm ,[object Object],[object Object]
Classification Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Today we’ll talk about… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Singular Value Decomposition ,[object Object],[object Object],[object Object],[object Object]
Weighted SVD ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Srebro & Jaakkola Weighted Low-Rank Approximations ICML2003
SVD with Missing Values ,[object Object],[object Object],[object Object],[object Object]
PageRank ,[object Object],[object Object],word id  web page list
PageRank Eigenvector ,[object Object],[object Object],Link analysis, eigenvectors & stability http://ai.stanford.edu/~ang/papers/ijcai01-linkanalysis.pdf
Convergence ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
User preference ,[object Object],[object Object],[object Object],[object Object],[object Object]
PCA & Factor Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Z r     M =3 K =2 r 1 r 3 r 2 z 1 z 2 N
Matrix methods ,[object Object],[object Object],Thomas Hofmann. Learning What People (Don't) Want. In Proceedings of the European Conference on Machine Learning (ECML), 2001.
Dimensionality Reduction Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Today we’ll talk about… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Probabilistic Models ,[object Object],[object Object],[object Object],[object Object],[object Object]
Mixture of Multinomials ,[object Object],[object Object],[object Object],Z r  M N 
Aspect Model ,[object Object],[object Object],[object Object],[object Object],Z r  M N U Y  Thomas Hofmann. Learning What People (Don't) Want. In Proceedings of the European Conference on Machine Learning (ECML), 2001.
Hierarchical Models ,[object Object],[object Object],[object Object],[object Object],Z r  M N  
Cross Validation ,[object Object],[object Object],[object Object],[object Object]
Support Vector Machine ,[object Object],[object Object],http://db.cs.ualberta.ca/webkdd05/proc/paper25-mladenic.pdf
Data Quality ,[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],cost: computation & memory most popular no meta-data cost: computation popular missing data ok cost: computation offline many assumptions missing data ok cost: computation offline less assumptions missing data ok cost: computation offline some assumptions missing data ok can include meta-data
Boosting Methods ,[object Object]
Clustering Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],week 6 clustering lecture

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CS294 Collaborative Filtering Algorithms

  • 1. Collaborative Filtering CS294 Practical Machine Learning Week 14 Pat Flaherty [email_address]
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  • 4. Netflix Movie Recommendation http://www.netflixprize.com/ “ The Netflix Prize seeks to substantially improve the accuracy of predictions about how much someone is going to love a movie based on their movie preferences.”
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