Dimensionality Reduction

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prerequisites
intermediate Python • basics of NumPy, pandas, scikit-learn, and machine learning
skills learned
compute clusters of users and items based on similarities • compute similarities on top of learned latent representations based on user and item interactions • define recommender systems based on similarity clusters
Alejandro Bellogin
1 week · 4-6 hours per week · BEGINNER
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In this liveProject, you’ll build a movie recommendation system that reduces the dimensionality of the spaces involved to enhance its performance so that latent representations are identified, which aim to capture users and items interactions with the system on the same latent vector space. You’ll work to compute clusters on the movies and clients of your site, and utilize latent representations of users and items with a reduced dimensionality to compute similarities. Once you’ve established this data, you’ll build and test your recommendation engine.

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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