Introduction to Recommender Systems
Lesson 1 of 1
  1. 1
    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…
  2. 2
    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…
  3. 3
    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 …
  4. 4
    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…
  5. 5
    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…
  6. 6
    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…
  7. 7
    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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