Netflix-Recommender

About the project

Back in 2006, Netflix organized a competition with a grand prize of $1,000,000. The aim of the competition was to enhance the effectiveness of Netflix's Recommender system.

My Approach

In this project, I will be leveraging deep learning to build a recommender system using the dataset from the competition. Graph Neural Networks have been shown to perform very well on tasks such as recommendation systems. I will use the LightGCN model (2020) for this mission.

Instructions:

1. download the file movie_titles.csv.
2. download the file combined_data_1.txt
3. run the script "data_fix.py".
4. delete all lines except the first 4500 in the file "movie_titles.csv".

Requirements

Ensure you have the following dependencies installed:

1. Python 3.9
2. Torch 1.11.0
3. Pandas 1.4.3
4. NumPy 1.21.5
5. PyG

Several things that I learned in this project are:

1. PyTorch Geometric - a library for graph neural networks (PyG).
2. Creating a model that can learn with negative samples.
3. Graph Neural Nets and Graph machine learning.
4. Information about recommendation systems, such as collaborative filtering, etc.