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Cornac: A Comparative Framework for Multimodal Recommender Systems
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   User Guide <user/index>
   Contributors Guide <developer/index>
   API Reference <api_ref/index>


**Cornac** is a comparative framework for multimodal recommender systems. It focuses on making it **convenient** to work with models leveraging **auxiliary data** (e.g., item descriptive text and image, social network, etc). **Cornac** enables **fast** experiments and **straightforward** implementations of new models. It is **highly compatible** with existing machine learning libraries (e.g., TensorFlow, PyTorch).


Quick Links
   `GitHub <https://github.com/PreferredAI/cornac/>`_ |
   `Tutorials <https://github.com/PreferredAI/cornac/tree/master/tutorials#tutorials>`_ |
   `Examples <https://github.com/PreferredAI/cornac/tree/master/examples#cornac-examples-directory>`_ |
   `Models <https://github.com/PreferredAI/cornac#models>`_ |
   `Datasets <https://github.com/PreferredAI/cornac/blob/master/cornac/datasets/README.md#datasets>`_ |
   `Paper <http://www.jmlr.org/papers/volume21/19-805/19-805.pdf>`_ |
   `Preferred.AI <https://preferred.ai/>`_ 

.. topic:: Cornac is suitable for all skill levels.

   From developers to researchers, and enthusiasts, Cornac allows you to implement Recommender Systems that meet your experimental and production needs.

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      **Installation**

      Start here to get Cornac installed and ready through a step-by-step guide
      with helpful explanation.

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         Install Guide

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      **Quickstart Guide**

      Already got Cornac installed? Start your first experiment by going
      through this step-by-step guide. This explains the Cornac experiment concept with codes.

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         Run Your First Experiment

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      **Incorporate your Models**

      Add your own models, datasets and metrics alongside Cornac's offerings to meet your needs.

      This section explains how you can add a new dataset/model/metric so that you can easily run experiments.

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         Add Your Own Implementation

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      **Contributing into Cornac**

      We welcome contributions.
      Learn how you can contribute to the broader community, including data scientists
      and engineers using Cornac for research and real-world applications.

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         Contributor's Guide