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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
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.
Data Manipulation
cosine similarity
Recommender Systems
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