Recommendation System using Matrix Factorization

This project builds a recommendation engine using Matrix Factorization to predict user-item ratings.

Recommend items to users based on learned latent features.

• Matrix Factorization (SVD)
• Latent Factor Modeling

• RMSE (Root Mean Squared Error)

data/ – dataset
notebooks/ – experiments
src/ – training & recommendation code
results/ – evaluation outputs

MovieLens Dataset – GroupLens Research

1. Install dependencies: pip install -r requirements.txt
2. Run notebook or train.py

Packiaraj R