From the course: Building Recommender Systems with Machine Learning and AI

Unlock this course with a free trial

Join today to access over 23,200 courses taught by industry experts.

Measuring similarity and sparsity

Measuring similarity and sparsity - Python Tutorial

From the course: Building Recommender Systems with Machine Learning and AI

Start my 1-month free trial Buy for my team

Measuring similarity and sparsity

“

- In our next section, we'll cover a neighborhood based collaborative filtering. This is the idea of leveraging the behavior or others to inform what you might enjoy. At a very high level, it means finding other people like you and recommending stuff they liked. Or it might mean finding other things similar to the things that you like. That is, recommending stuff people bought who also bought the stuff that you liked. Either way, the idea is taking cues from people like you, your neighborhood, if you will, and recommending stuff based on the things they like that you haven't seen yet. That's why we call it collaborative filtering. It's recommending stuff based on other people's collaborative behavior. We're going to start off by revisiting the first architecture for top-N recommender systems we covered earlier in the course. The way we initially do collaborative filtering at Amazon was like this. You start with a data store of some sort that includes rating information, be they…

Contents