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Building Recommendation Engines with PySpark

Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.

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4 Hours15 Videos56 Exercises9,265 Learners4550 XPBig Data with PySpark Track

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Course Description

This course will show you how to build recommendation engines using Alternating Least Squares in PySpark. Using the popular MovieLens dataset and the Million Songs dataset, this course will take you step by step through the intuition of the Alternating Least Squares algorithm as well as the code to train, test and implement ALS models on various types of customer data.

  1. 1

    Recommendations Are Everywhere


    This chapter will show you how powerful recommendations engines can be, and provide important distinctions between collaborative-filtering engines and content-based engines as well as the different types of implicit and explicit data that recommendation engines can use. You will also learn a very powerful way to uncover hidden features (latent features) that you may not even know exist in customer datasets.

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    Why learn how to build recommendation engines?
    50 xp
    See the power of a recommendation engine
    100 xp
    Power of recommendation engines
    50 xp
    Recommendation engine types and data types
    50 xp
    Collaborative vs content-based filtering
    50 xp
    Collaborative vs content based filtering part II
    50 xp
    Implicit vs explicit data
    100 xp
    Ratings data types
    100 xp
    Uses for recommendation engines
    50 xp
    Alternate uses of recommendation engines.
    50 xp
    Confirm understanding of latent features
    100 xp
  2. 2

    How does ALS work?

    In this chapter you will review basic concepts of matrix multiplication and matrix factorization, and dive into how the Alternating Least Squares algorithm works and what arguments and hyperparameters it uses to return the best recommendations possible. You will also learn important techniques for properly preparing your data for ALS in Spark.

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  3. 4

    What if you don't have customer ratings?

    In most real-life situations, you won't not have "perfect" customer data available to build an ALS model. This chapter will teach you how to use your customer behavior data to "infer" customer ratings and use those inferred ratings to build an ALS recommendation engine. Using the Million Songs Dataset as well as another version of the MovieLens dataset, this chapter will show you how to use the data available to you to build a recommendation engine using ALS and evaluate it's performance.

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In the following tracks

Big Data with PySpark


loreLore DiricknicksolomonNick SolomonadriansotoAdrián Soto
Jamen Long Headshot

Jamen Long

Data Scientist

Jamen is a data scientist with experience building machine learning models to predict and guide customers’ physical and digital shopping journeys. Having started his data science journey with DataCamp years ago, Jamen enjoys continuing to learn new applications of algorithms and data science frameworks.
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