Introduction to Recommender Systems
Lesson 1 of 1
  1. 1
    What are Recommender Systems?
    With the growth of the internet, the number of options people have for everything from watching videos, to buying clothes, to finding a date has increased dramatically. Having potentially thousands…
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  2. 2
    Recommender Systems versus Supervised Learning
    As you learned about recommender systems in the previous exercise, you may be wondering how recommender systems differ from traditional supervised learning techniques you have learned about in pre…
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  3. 3
    Properties of Good Recommender Systems
    When considering the performance of a recommender system, there are a couple of different dimensions we should consider: - Relevance: When a recommender system makes a recommendation, it should …
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  4. 4
    Types of Recommender Systems
    Recommender systems can generally be classified into one of three different groups depending on the algorithmic approach they take to make recommendations: Collaborative Filtering: Collaborative…
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  5. 5
    Ratings Matrix: Representing User Preferences
    The first step in building a recommender system is to have a mathematical representation of data relating to user’s preferences. Often, the representation used is a matrix of numbers called a **rat…
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  6. 6
    Collaborative Filtering
    In the previous exercise, we introduced collaborative filtering. However collaborative filtering can be further classified into two major subclasses: memory-based methods (also called **neighbo…
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  7. 7
    Summary
    In this lesson, we learned about recommender systems. We learned about how they compare to traditional supervised learning and what the properties of good recommender systems. We also discussed the…
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