10-601 Matrix Factorization

This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016

Slides

• Slides in Powerpoint, Slides in PDF.

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.