Netflix-Movie-Recommendation-System

1.1 Problem Description

Netflix is all about connecting people to the movies they love. To help customers find those movies, they developed world-class movie recommendation system: CinematchSM. Its job is to predict whether someone will enjoy a movie based on how much they liked or disliked other movies. Netflix use those predictions to make personal movie recommendations based on each customer’s unique tastes. And while Cinematch is doing pretty well, it can always be made better. Now there are a lot of interesting alternative approaches to how Cinematch works that netflix haven’t tried. Some are described in the literature, some aren’t. We’re curious whether any of these can beat Cinematch by making better predictions. Because, frankly, if there is a much better approach it could make a big difference to our customers and our business. Credits: https://www.netflixprize.com/rules.html

1.2 Problem Statement

Netflix provided a lot of anonymous rating data, and a prediction accuracy bar that is 10% better than what Cinematch can do on the same training data set. (Accuracy is a measurement of how closely predicted ratings of movies match subsequent actual ratings.)

1.3 Sources

• https://www.netflixprize.com/rules.html • https://www.kaggle.com/netflix-inc/netflix-prize-data • Netflix blog: https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429 (very nice blog) • surprise library: http://surpriselib.com/ (we use many models from this library) • surprise library doc: http://surprise.readthedocs.io/en/stable/getting_started.html (we use many models from this library) • installing surprise: https://github.com/NicolasHug/Surprise#installation • Research paper: http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf (most of our work was inspired by this paper) • SVD Decomposition : https://www.youtube.com/watch?v=P5mlg91as1c

1.4 Real world/Business Objectives and constraints

Objectives:

Predict the rating that a user would give to a movie that he has not yet rated.
Minimize the difference between predicted and actual rating (RMSE and MAPE)

Constraints:

Some form of interpretability.

2. Machine Learning Problem

2.1 Data

2.1.1 Data Overview

Get the data from : https://www.kaggle.com/netflix-inc/netflix-prize-data/data

Data files :

• combined_data_1.txt • combined_data_2.txt • combined_data_3.txt • combined_data_4.txt • movie_titles.csv

The first line of each file [combined_data_1.txt, combined_data_2.txt, combined_data_3.txt, combined_data_4.txt] contains the movie id followed by a colon. Each subsequent line in the file corresponds to a rating from a customer and its date in the following format:

CustomerID,Rating,Date

MovieIDs range from 1 to 17770 sequentially.

CustomerIDs range from 1 to 2649429, with gaps. There are 480189 users.

Ratings are on a five star (integral) scale from 1 to 5.

Dates have the format YYYY-MM-DD.

2.2 Mapping the real world problem to a Machine Learning Problem

2.2.1 Type of Machine Learning Problem

For a given movie and user we need to predict the rating would be given by him/her to the movie. The given problem is a Recommendation problem It can also seen as a Regression problem

2.2.2 Performance metric

• Mean Absolute Percentage Error: https://en.wikipedia.org/wiki/Mean_absolute_percentage_error • Root Mean Square Error: https://en.wikipedia.org/wiki/Root-mean-square_deviation

2.2.3 Machine Learning Objective and Constraints

Minimize RMSE.
Try to provide some interpretability.

Code Execution

• Create the Environment

conda env create -f environment.yml

Activate the environment :

For Windows:

conda activate my_environment

For macOS and Linux:

source activate my_environment

Then run the notebooks available in the directory

• Install the packages directly using

pip install requirements.txt