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Similarities and Recommender Systems

Content-Based Similarities you own this product

This 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
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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liveProject This project is part of the liveProject series Similarities and Recommender Systems liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $17.99 $29.99 you save: $12 (40%)
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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.

book and video resources

When you start your liveProject, you get full access to the following books and videos for 90 days.

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
  • Basics of data structures
  • Basics of NumPy
  • Basics of pandas
  • Basics of scikit-learn
  • 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 content and metadata
  • Define recommender systems based on the definition of when two users or items are close
  • 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.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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