Build Recommendation Systems using Collaborative Filtering
Explore collaborative filtering to build personalized recommendation systems. Learn how to analyze user-item interactions and leverage algorithms like matrix factorization to suggest content, products, or services in e-commerce and media.
At a Glance
Python is a popular programming language that can be used to create recommendation systems. In this guided project, you will learn how to create a recommendation system based on collaborative filtering.
Python is a popular programming language used in data science and analytics, as it provides a wide range of libraries and tools for working with data. You can use Python to create recommendation systems using collaborative filtering, which uses similar users’ preferences to generate recommendations.
In this guided project, you’ll learn how to use Python and the pandas library to create a recommendation system using collaborative filtering.
You’ll begin by downloading a data set from GroupLens, then importing the necessary libraries, storing the data in DataFrames, and preprocessing the DataFrame to prepare the data for analysis. Finally, you’ll use this data to create a movie recommendation system using collaborative filtering,
After completing this guided project, you’ll be ready to apply your new skills to use collaborative filtering to create recommendations using your own data sets and criteria.
A Look at the Project Ahead
After completing this project, you’ll be able to:
- Create a recommendation system based on collaborative filtering
What You’ll Need
For this project, you will need:
- Familiarity with basics of Python and pandas
- A web browser
Everything else is provided to you via the IBM Skills Network Labs environment, where you will have access to the Python environment that we offer as part of the IBM Skills Network Labs environment. This platform works best with current versions of modern browsers.
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