Recommender System using Goodbooks-10k

This project explores collaborative filtering, matrix factorization, and deep learning models to build a high-quality recommender system using the Goodbooks-10k dataset. The system includes data preprocessing with Pandas/Polars, feature engineering, model evaluation with RMSE, and a neural recommender implemented with PyTorch. The long-term goal is to deploy the system behind a FastAPI service and expose recommendations through a Next.js interface.

Full project documentation coming soon...