Movie Recommender: Code-Along

A code-along Jupyter notebook for the Flatiron School Meetup, "The Science Behind Netflix Recommendations: Workshop", presented on:

• June 5, 2019
• October 21, 2019
• Feb 3, 2020 (Webinar)
• Jun 8, 2021 (Webinar)

Slides

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

Accessing the Code:

The easiest way to see the code is by clicking here:

If you're reading the code in Binder, you won't be able to run codealong, but you can view codealong-clean.

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/yishuen/meetup-movie-recommender.git
2. cd meetup-movie-recommender
3. jupyter notebook

Additional Readings and Resources:

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

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

A great book on big data. My favorite chapter is Chapter 9, and I 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

A couple really good podcast episodes that got me started with 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 me!