User Guide

Welcome to the Cornac User Guide. This guide aims to get you started in recommender systems using Cornac, a Python library friendly to most users.

Cornac is a robust Python library tailored to normal users, alongside data scientists and engineers. Cornac aims to equip you with the tools to build, evaluate, and fine-tune recommender systems to meet your needs.

Getting Started

• Installation
  • Install Python
  • Install Cornac
  • Model dependencies
  • Verifying Installation
  • What’s next?
• Quickstart
  • The Cornac Experiment Concept
  • The First Experiment
  • What’s Next?
• For Developers
  • Experimenting with models
  • Hyper-parameter tuning
  • Data loading
  • Training model
  • Obtaining recommendations
  • Model persistence
  • Running an API service
  • Running the API service with Docker
  • What’s Next?
• For Researchers
  • What can you do with Cornac?
  • Conducting experiments
  • Tuning hyper-parameters
  • Adding your own model
  • Development workflow
  • Analyze results
  • Using additional packages
  • Adding a new metric
  • Let us know!
  • What’s Next?

Additional Guides

• Using your Own Data
  • My First Data Prediction
  • Loading data from CSV
• FoodRecce Example
  • Training data
  • How the recommender was built
  • Providing feedback
  • Giving recommendations
  • Updating the Model
  • Conclusion
  • What’s Next?
  • Others