Matrix Factorization — Singular Value Decomposition (SVD) Explained

Build a Recommender System Pipeline using Latent Factor Recommendations (SVD)

Vatsal
Towards Data Science

Image taken by Vlado Paunovic from Unsplash

This article will outline the intuition and the Python implementation of matrix factorization for recommendation systems. The following is the outline of the article.

Table of Contents

  • Intuition behind Matrix Factorization
  • Singular Value Decomposition (SVD)
    - Mathematics of SVD
    - Example Walkthrough
  • Problem Statement
  • Data
    - Requirements
  • Solution Architecture
  • SVD Recommendation System Implementation
    - Generate User-Item Matrix
    - Calculate SVD
    - Generate Recommendations
  • Challenges
  • Concluding Remarks
  • Resources

Intuition behind Matrix Factorization

A recommendation engine is a subcategory of machine learning which aims to provide a rating for some user or item. Matrix factorization falls under the category of collaborative filtering in recommendation systems. Intuitively, collaborative filtering aims to identify items that a user A would like based on the interactions…

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