From the course: Building a Recommendation System with Python Machine Learning and AI
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Content-based recommender systems
From the course: Building a Recommendation System with Python Machine Learning and AI
Content-based recommender systems
- [Instructor] The last type of recommender I want to cover is content-based recommendation systems. These type of recommenders are not collaborative filtering systems because user preferences and attitudes do not weigh into the evaluation. Instead content-based recommenders recommend an item based on its features and how similar of those are to features of other items in a dataset. In the demo, we're going to use the nearest neighbor algorithm to build a content-based recommender. The nearest neighbor algorithm is a unsupervised classifier. It's also known as a memory-based system because it memorizes instances and then recommends an item or a single instance based on how quantitatively similar it is to a new incoming instance. To conceptualize how to use nearest neighbor algorithm in this capacity, imagine you're a car dealer. You get a customer that comes in and tugs you that he wants a car that gets 25 miles per gallon and has a 4.7-liter, 425 horsepower engine. You have a dataset…
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