Learn to build recommendation engines with PySpark's Alternating Least Squares algorithm. The technique powers 35% of Amazon purchases and 75% of Netflix viewing. ALS compares users with similar rating patterns to predict and suggest unseen items, and can also compress images and discover hidden features.
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 provide, either directly or indirectly. When you purchase something online or watch a movie, or even read an article, you're often given a chance to rate that item on a scale of one.
To five stars, a thumbs up or.
Thumbs down or some other type of rating scale. Based on your feedback from these types.
Of rating systems, customers 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 the Dark Knight and Iron man and did not like Tangled, and it also sees other users that also liked the 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 like 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 Co. In October of 2013 stated that 35%.
Of what customers buy on Amazon and.
75% of what they watch on Netflix come from product recommendations based on algorithms such as the one you are going to be learning in this course. That's a powerful use of data, and with this course you will learn how to do this. In addition to this, there are alternate uses of recommendation algorithms that can be extremely useful for purposes as broad as.
Feature space reduction, image compression, mathematical user and product grouping, latent feature discovery, and.
You'Re going to learn some of these in this course. This tutorial is intended for those that have experience with Spark and Python and understand the fundamentals of machine learning if needed. Some good introductory resources are datacamp's Introduction to pyspark course, their intermediate Python for Data Science course, and their supervised machine Learning with Python's SciKit learn course.
