This repository contains various models for creating a movie recommendation system. Each model uses different methods to provide accurate and personalized movie recommendations. The main methods included are:
- Collaborative Filtering: Recommends movies based on user interactions.
- Content-Based Filtering: Recommends movies similar to those a user has liked in the past.
- Matrix Factorization: Uses techniques like Singular Value Decomposition (SVD) to uncover latent factors in user-item interactions.
- Hybrid Methods: Combines multiple approaches to enhance recommendation accuracy.
Each method is implemented in its respective subfolder, with detailed documentation and code examples provided.
To install the necessary packages, run:
pip install <requirements>To run a specific model, navigate to its subfolder and execute the corresponding script. For example:
cd collaborative_filtering
python <main>.py