In this episode,  we cover the two main types of recommendation engines used at companies like Netflix and Spotify.

1) Content based recommendation systems use the genres or tags of each product to find other similar products to recommend to users.
2) Collaborative filtering based recommendation systems use user activity and user ratings on the website to recommend products. 

We go through the pros and cons of each, the challenges, how do companies like Netflix and Spotify scale their recommendation engines for millions of users and more! 

My code in the Github repo which implements these concepts from scratch using MovieLens dataset.  

Links:
1) Youtube talk by Xavier Amatriain from Netflix
2) Youtube talk on "Machine Learning & Big Data for Music Discovery presented by Spotify"
3) Youtube tutorial by Luis Serrano on how Netflix recommends movies

#netflix #spotify #movielens #recommendations #recommendation-engines

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