Recommendation Systems with Deep Learning
A practical approach using Google Cloud
Matrix multiplications are still widely used for Recommendation Systems. And they are a great starting point if you want to build a quick and easy solution to deliver great recommendations to your customers. Nothing wrong with that. Keep it simple until you make it complicated.
In this article, we want to make it at least a bit more complicated and dig deeper into how you can build a Recommendation System that uses Deep Learning instead of Matrix Multiplication.
Jump Directly to the Notebook and Code
All the code for this article is ready to use in a Google Colab notebook. If you have questions, please reach out to me via LinkedIn or Twitter.
It all starts with your data
In a typical recommender system, you have two types of input the users and the items. Items can be books, movies, songs, or anything your customers interact with.
