Covers: theory of Non-negative Matrix Factorization
Estimated time needed to finish: 60 minutes
Questions this item addresses:
  • What is the theory behind non-negative matrix factorization?
How to use this item?

Read through slides 1-12 for most relevant content

Author(s) / creator(s) / reference(s)
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Matrix Factorization for Recommender Systems

Total time needed: ~4 hours
This list will go over three matrix factorization methods (unconstrained, SVD, and NMF) used in Recommender Systems.
Potential Use Cases
Recommending movies (Netflix), "watch next" (YouTube), or "items you might like" (Amazon).
Who is This For ?
Click on each of the following annotated items to see details.
VIDEO 1. Introduction to Matrix Factorization
  • Overview of the use of matrix factorization applied to Netflix recommendation
35 minutes
OTHER 2. Lecture Notes on Unconstrained Matrix Factorization Methods from Carnegie Mellon
  • What is the theory behind unconstrained matrix factorization?
60 minutes
ARTICLE 3. SVD for Recommender Systems
  • How to implement SVD for recommender systems?
30 minutes
VIDEO 4. Video Lecture on SVD for Recommender Systems from Stanford
  • What is the theory behind SVD for recommender systems?
15 minutes
OTHER 5. Lecture Notes on Non-negative Matrix Factorization from Stanford
  • What is the theory behind non-negative matrix factorization?
60 minutes
LIBRARY 6. Python Package (NIMFA) to solve NMF
  • How do I solve NMF and other factorization problems in Python
30 minutes

Concepts Covered

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