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Overview of Netflix and Spotify like recommendation engines

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Manage episode 243278356 series 2550866
Content provided by Sanket Gupta. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Sanket Gupta or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

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

--- Send in a voice message: https://podcasters.spotify.com/pod/show/the-data-life-podcast/message Support this podcast: https://podcasters.spotify.com/pod/show/the-data-life-podcast/support
  continue reading

27 episodes

Artwork
iconShare
 
Manage episode 243278356 series 2550866
Content provided by Sanket Gupta. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Sanket Gupta or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

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

--- Send in a voice message: https://podcasters.spotify.com/pod/show/the-data-life-podcast/message Support this podcast: https://podcasters.spotify.com/pod/show/the-data-life-podcast/support
  continue reading

27 episodes

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