A recommendation system project that suggests games to users using multiple models

• train.py: Train and save models using config
• evaluate.py: Evaluate models on classification and ranking metrics
• main.py: Load a trained model and recommend top 10 games for a user
• data_utils.py: Data loading and preprocessing
• recommender/: Implementation of all models
• config.yaml: Configuration for model hyperparameters

# Install dependencies
pip install -r requirements.txt

# Train a model
python train.py

# Recommend top 10 games for a user
python main.py

All hyperparameters are defined in config.yaml. You can switch between models easily by changing model_name.

📦 Dependencies

• Python 3.8+
• PyTorch
• pandas, numpy, yaml

This repository does not contain raw data due to file size limitations on GitHub.

To run the project, please follow the steps below:

Download the dataset from the following link: 👉 Download Dataset from Google Drive

Unzip the downloaded file.

Place the extracted csv files into data/processed