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Similarities and Recommender Systems

Dimensionality Reduction 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
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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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 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.

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
  • 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 and similarities from latent representations
  • 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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