This repository is part of the Bachelor’s Thesis (TFG) by Francisco de Paula Algar Muñoz at the University of Córdoba (UCO), titled: Application of Recommendation Systems in Educational Environments (PDF).
Note
Most of the project is written in Spanish, only this repository has been translated into English.
The goal of this project is to develop a recommendation system for e-learning based on a benchmark dataset, allowing evaluation of its performance compared to previous models.
ucorecsys [-h] (-i MODEL_PATH | -t | -e | -s | -st) [-ds {mars,itm}] [-cv {kfold,loo}] [--top_k TOP_K] [--epochs EPOCHS] [--batch_size BATCH_SIZE] [--output_model MODEL_OUT] [-lr LR] [-k K_SPLITS] [--seeds SEEDS] [-p] [-v]-h, --help Show this help message and exit -i MODEL_PATH, --inference MODEL_PATH Run inference on a trained model (provide path to model file). -t, --train Train the model. -e, --eval Evaluate the model. -s, --surprise Run Surprise evaluation. -st, --stats_test Run statistical tests (default: False). Common Options: -ds {mars,itm}, --dataset {mars,itm} Dataset to use (default: mars). -cv {kfold,loo}, --cvtype {kfold,loo} Cross-validation type (default: kfold). --top_k TOP_K Top-k value for ranking metrics (default: 10). Training Options: --epochs EPOCHS Number of training epochs (default: 50). --batch_size BATCH_SIZE Training batch size (default: 128). --output_model MODEL_OUT Path to save the trained model (default: model.pt). -lr LR Learning rate (default: 0.001). Evaluation Options: -k K_SPLITS, --k_splits K_SPLITS Number of CV splits (default: 5). --seeds SEEDS Random seeds (default: [0, 1, 42]). Miscellaneous Options: -p, --plot Generate plots. -v, --verbose Enable verbose output.Note
To run this project, you need to have the uv package manager installed.
Follow these steps to run the project:
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Clone the repository
git clone https://github.com/Pacatro/UcoRecSys.git cd UcoRecSys -
Install dependencies and create a virtual environment
uv sync --extra cpu # For CPU uv sync --extra cu128 # For CUDA 12.8
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Run the project
uv run src/main.py -h
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Train the model on the MARS dataset for 10 epochs, generating a Top-15 recommendation list and saving the model as
tfg.pt:uv run src/main.py -t -ds mars --epochs 10 --top_k 15 --output_model tfg.pt
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Run inference using the previously trained model:
uv run src/main.py -i tfg.pt
Francisco de Paula Algar Muñoz
Amelia Zafra Gómez
Cristóbal Romero Morales
MIT - Created by Paco Algar Muñoz