Covers: implementation of Non-negative Matrix Factorization

- How do I solve NMF and other factorization problems in Python

See "Examples" to solve NMF problems

NIMFA

Hojae Lee**Total time needed: **~4 hours

- Learning Objectives
- 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).
- Target Audience
- INTERMEDIATE

Go through the following **annotated items** *in order*:

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

Previewing stream ** Recommender Systems**

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