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MovieLens Recommendation System

Description

This repository contains the code for a movie recommendation system built using the MovieLens 32M dataset. The system uses a hybrid approach that combines collaborative filtering, content-based filtering and graph-based recommendations.

Data Source

The MovieLens 32M dataset can be downloaded from: https://grouplens.org/datasets/movielens/32m/

Getting Started

  1. Clone the repository:
git clone https://github.com/ShoreDataLab/MovieLens-RecSys
  1. Change to the project directory:
cd MovieLens-RecSys
  1. Install required dependencies:
pip install -r requirements.txt

Scripts and Notebooks

The repository includes a variety of scripts and notebooks for building and evaluating different recommendation algorithms:

  • notebooks/01_eda.ipynb: Preforming exploratory data analysis on the MovieLens dataset
  • 02_features.ipynb: Performing feature engineering for the model
  • models/movie_rec_model.ipynb.py: Builds recommendation model and evaluates the performance

Evaluation

The recommendation system's performance is evaluated using metrics like precision and recall.

Further Development

This repository provides a foundation for building and evaluating movie recommendation systems. Consider the following for further development:

  • Experimenting with different recommendation algorithms
  • Tuning hyperparameters of recommendation models
  • Incorporating additional data sources