Matrix Factorization (SVD): The Math That Won the Netflix Prize (50 Algorithms #44)

Matrix factorization is the workhorse of model-based collaborative filtering. You start with a giant,

This is algorithm #44 of the 50 Algorithms Every Programmer Should Know series (NLP & Recommendation). You will learn:
- The exact problem Matrix Factorization (SVD) solves and the intuition behind it
- How it works step by step, with animated diagrams
- The real Python code from the book repo, walked line by line
- Where it is used in practice, its strengths, and its limits

Chapters
00:00 Intro
01:41 Latent Factors and SVD
04:49 Building It in Python
07:36 Embeddings in Production
08:01 The Cold Start Problem
09:43 When to Deploy It

WATCH NEXT
▶ Next: Content-Based Recommendation: Recommending by What an Item Is, Not Who Liked It (50 Algorithms #45)
https://www.youtube.com/watch?v=SOjR840fA58&list=PLE7n_DIQnYRE
◀ Previously: Collaborative Filtering: How Netflix Knows What You'll Watch (50 Algorithms #43)
https://www.youtube.com/watch?v=4uhAxBml1B0&list=PLE7n_DIQnYRE
📺 Full playlist (51 videos): https://www.youtube.com/playlist?list=PLE7n_DIQnYRE
Code (GitHub): https://github.com/cloudanum/50Algorithms/blob/main/Chapter12/Movies_recommendation.ipynb
Book (Amazon): https://www.amazon.com/Algorithms-Every-Programmer-Should-Know/dp/1803247762
neurals.ca: https://neurals.ca
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Playlist (50 Algorithms): https://www.youtube.com/playlist?list=PLE7n_DIQnYRE

Subscribe to @neurals_ca for the whole series.

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