PySpark Tutorial : Why learn how to build recommendation engines?
Want to learn more? Take the full course at https://learn.datacamp.com/courses/recommendation-engines-in-pyspark at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work.
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Hi. Welcome to this course on building recommendation engines using Alternating Least Squares or "ALS" in PySpark.
You're probably already familiar with the output of these types of recommendation engines where a website tells you something along the lines of, "If you like that, then you'll probably like this." You've likely seen these types of recommendations on your favorite retail or media streaming websites. These recommendations are generated through different types of data that you as a user or customer provide either directly or indirectly.
When you purchase something online, or watch a movie, or even read an article, you are often given a chance to rate that item on a scale of 1 to 5 stars, a thumbs up or thumbs down, or some other type of rating scale. Based on your feedback from these types of rating systems, companies can learn a lot about your preferences, and offer you recommendations based on preferences of users that are similar to you.
For example, if your movie streaming service sees that you liked Dark Knight and Iron Man, and did not like Tangled, and it also sees other users that also liked Dark Knight and Iron Man and also did not like Tangled, the ALS algorithm would see that you and these other users have similar tastes. It would then look at the movies that you have not yet seen, and see which ones are the highest rated among those similar users, and offer them as recommendations to you. This is why websites will often say things like, "Because you liked that movie, we think you'll like this movie." Or "Users like you also watched this movie."
These types of rating systems are extremely powerful. In fact, an article published by McKinsey & Company in October of 2013 stated that 35% of what customers buy on Amazon a
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