Difference between revisions of "10-601 Matrix Factorization"
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* [http://www.cs.cmu.edu/~wcohen/10-601/cf.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/cf.pdf Slides in PDF]. | * [http://www.cs.cmu.edu/~wcohen/10-601/cf.pptx Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/cf.pdf Slides in PDF]. | ||
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| + | * Poll: [https://piazza.com/class/ij382zqa2572hc] | ||
=== Readings === | === Readings === | ||
Revision as of 09:39, 18 April 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
- Poll: [1]
Readings
Matrix factorization and collaborative filtering is not covered in Murphy or Mitchell. Some external readings are below.
- Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 8 (2009): 30-37.
- There's a nice description of the gradient-based approach to MF, and a scheme for parallelizing it,by Gemulla et al.
Summary
You should know:
- What social recommendations systems are, and how they relate to matrix factorization.
- How to solve MF via gradient descent.
- How matrix factorization is related to PCA and k-means.
