Content-Based Similarities

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prerequisites
intermediate Python • basics of NumPy, pandas, scikit-learn, and machine learning
skills learned
define and compute similarities between users and items using content and metadata • define recommender systems based on the definition of when two users or items are close
Alejandro Bellogin
1 week · 4-6 hours per week · BEGINNER
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In this liveProject, you’ll build a movie recommendation system based on the content and metadata of movies in your system. This system is intended to maximize the satisfaction of your movie-watching users. You’ll start with an analysis to determine the content of your movies, then use that data to implement content-based similarities for both products and users. You’ll build and evaluate your recommender system based on these connections, till it’s the best it can be!

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Alejandro Bellogin
Alejandro Bellogín is an Associate Professor at Universidad Autónoma de Madrid. Previously, he held a post-doctoral research grant with the Centrum Wiskunde and Informatica in Amsterdam, The Netherlands. He has supervised around ten Master Theses, one PhD thesis, and more than twenty Bachelor Theses on recommender systems. His publication history includes around 80 publications about different aspects of recommender systems.

prerequisites

The liveProject is for intermediate Python programmers who know the basics of data science. To begin this liveProject, you will need to be familiar with the following:


TOOLS
  • Intermediate Python, min. version 3.6.0
  • Basics of data structures
  • Basics of NumPy, min. version 1.19.0
  • Basics of pandas, min. version 1.1.0
  • Basics of scikit-learn, min. version 0.20.3
  • Basics of Jupyter Notebook
TECHNIQUES
  • Algebra and calculus
  • Basics of machine learning

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book resources
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  • exclusive 50% discount on all purchases
  • Content-Based Similarities project for free