Recommender Systems: Under the Hood of Recommendation Systems

Math    |    Beginner
  • 10 videos | 1h 23m 20s
  • Includes Assessment
  • Earns a Badge
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Users marvel at a system's ability to recommend items they're likely to appreciate. As someone working with machine learning, implementing these recommendation systems (also called recommender systems) can dramatically increase user engagement and goodwill towards your products or brand. Use this course to comprehend the math behind recommendation systems and how to apply latent factor analysis to make recommendations to users. Examine the intuition behind recommender systems before investigating two of the main techniques used to build them: content-based filtering and collaborative filtering. Moving on, explore latent factor analysis by decomposing a ratings matrix into its latent factors using the gradient descent algorithm and implementing this technique to decompose a ratings matrix using the Python programming language. By the end of this course, you'll be able to build a recommendation system model that best suits your products and users.


  • Discover the key concepts covered in this course
    Summarize the use cases of recommendation systems and the different techniques applied to build such models, with emphasis on the content-based filtering approach
    Describe the intuition behind collaborative filtering, its main advantages, and how ratings matrices, the nearest neighbor approach, and latent factor analysis are involved
    Decompose a ratings matrix into its latent factors
    Apply gradient descent to compute the factors of a ratings matrix
  • Compute a penalty for a large number of latent factors when computing the factors of a ratings matrix
    Use numpy and pandas to define a ratings matrix that can be fed into a recommendation system
    Implement the gradient descent algorithm to decompose a ratings matrix
    Compute the predicted ratings given by users for various items by using matrix decomposition
    Summarize the key concepts covered in this course


  • 2m 51s
  • 11m 50s
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    3.  The Collaborative Filtering Technique
    12m 49s
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    4.  How to Work with Matrix Factorization
    9m 54s
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    5.  Using Matrix Factorization with Gradient Descent
    10m 52s
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    6.  Introducing a Regularization Term to Matrices
    6m 53s
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    7.  Preparing the Ratings Matrix
    8m 8s
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    8.  Decomposing a Ratings Matrix
    8m 59s
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    9.  Estimating Ratings Using Gradient Descent
    8m 57s
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    10.  Course Summary
    2m 6s


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