Building Similarity Based Recommendation System

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Coursera Project Network
In this Guided Project, you will:

Understand what is collaborative filtering and how to collect data to build a recommendation system

Understand how to create user item interactions matrix to find which users are most similar to the other users

Build a similarity based recommendation system based on collaborative filtering

Clock2 hour
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

Welcome to this 1-hour project-based course on Building Similarity Based Recommendation System. In this project, you will learn how similarity based collaborative filtering recommendation systems work, how you can collect data for building such systems. You will learn what are some different ways you to compute similarity between users and recommend items based on products interacted by other similar users. You will learn to create user item interactions matrix from the original dataset and also how to recommend items to a new user who does not have any historical interactions with the items. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Data Manipulationcosine similarityRecommender Systems

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Understanding collaborative filtering and dataset

  2. Exploring the dataset

  3. Creating user item interactions matrix

  4. Finding similar users

  5. Creating similarity based recommendation system

  6. Conclusion

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.