From the course: Spark for Machine Learning & AI
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Understand recommendation systems - Apache Spark Tutorial
From the course: Spark for Machine Learning & AI
Understand recommendation systems
- [Instructor] A common problem in machine learning is making recommendations. Now, there's two general ways of doing this. One is called Collaborative Filtering. Let's imagine you run an online bookstore, and you have a number of customers. And these customers all like reading both the brown book and the red book. Now a new customer comes along and indicates that they really enjoyed reading the red book. What other books can we recommend to them? Well, definitely the brown book, since other people who have read the red book also enjoy reading the brown book. This is an example of collaborative filtering. Another way to make recommendations is based on the properties of the items that you're working with. So, for example, if we have a customer who really enjoys readying Sci-fi, we might want to recommend other science fiction books to them, but not necessarily biographies. Spark MLlib supports Collaborative Filtering, and it works by filling in something known as the user-item matrix.…
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