Similarities and Recommender Systems

Collaborative Similarities you own this product

This free project is part of the liveProject series Similarities and Recommender Systems
prerequisites
intermediate Python • basics of NumPy, pandas, scikit-learn, and machine learning
skills learned
read, process, and exploit user-item data • define and compute similarities between users and items using interactions • compute similarities between users and items in a recommender system
Alejandro Bellogin
1 week · 4-6 hours per week · BEGINNER
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Look inside

In this liveProject, you’ll build a movie recommendation system based on movies that your users have previously interacted with. This system is designed to boost engagement and keep your users on your site. You’ll develop a competitive array of similarity functions, and create your own recommender system based on these similarities. Finally, you’ll evaluate your system’s effectiveness and tune its parameters.

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

you will learn

In this liveProject, you’ll learn how to build powerful recommendation systems using the most popular tools in the Python data ecosystem.


  • Read, process, and exploit user-item data
  • Define and compute similarities between users and items using interactions
  • Compute similarities between users and items in a recommender system
  • Analyze algorithm output
  • Tune model settings to improve its results

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
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While within the liveProject platform, get help from other participants.
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For each step, compare your deliverable to the solutions by the author and other participants.
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