A code along workshop for the Flatiron School Meetup The Science Behind Netflix Recommendations : Talk | London. The original materials for this workshop were developed by Flatiron NYC Coach Yish Lim and were adapted for online Zoom broadcast by David John Baker.

This version of the workshop is being presented on:

• March, 24th 2020 and can be accessed here via Zoom

You can access the slides from here.

You can access our collaborative document here.

Here is a binder you can launch the notebook from.

The dataset used is The Movies Dataset found on Kaggle, specifically from the file ratings small.csv: https://www.kaggle.com/rounakbanik/the-movies-dataset

If you do want to run the code on your own, download Jupyter Notebook and run these lines in your terminal:

1. git clone https://github.com/davidjohnbaker1/science-of-netflix.git
2. cd science-of-netflix
3. jupyter notebook

Yish has wrote a couple posts on Medium about recommendation engines: https://medium.com/@yishuen

• A Primer to Recommendation Engines
• Collaborative Filtering on Utility Matrices

If you want to read more about the math behind SVD, check out Chapter 9. We highly recommend it if you want to understand how SVD works using gradient descent + alternating least squares.

• http://infolab.stanford.edu/~ullman/mmds/book.pdf

Here are some other links to podcast episodes on recommendation engines:

• Data Skeptic: http://dataskeptic.libsyn.com/recommender-systems-live-from-farcon-2017
• Linear Digressions: http://lineardigressions.com/episodes/2017/6/25/factorization-machines

Feel free to reach out to either Yish or Dave if you have any questions