Metadata-Version: 2.1
Name: cordial
Version: 0.222
Summary: comprehensive recommender systems
Home-page: https://github.com/Vagif12/cordial
Author: Vagif Aliyev
Author-email: vagal2003@gmail.com
License: MIT
Keywords: machine learning,recommender systems,automation
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: networkx
Requires-Dist: scikit-learn

# cordial
Coridal is an intuitive light-weight API that allows developers to seamlessly use different recommender systems for their needs. 
In just three lines of a code, one can built a powerful content recommender system. Currently, Coridal only provides content recommenders,
but collaborative filtering is coming soon!

# Installation
`pip install cordial`

# Example usage:

```python

# An example with Cordial's BasicRecommender
from cordial.content_recommenders import GraphRecommender,BasicRecommender
recommender = BasicRecommender('disney')
print(recommender.recommend('Toy Story')['result'])

# An example with Cordial's GraphRecommender
from cordial.content_recommenders import GraphRecommender,BasicRecommender
recommender = GraphRecommender('netflix')
print(recommender.recommend('Toy Story')['result'])

# It's as simple as pie!
```

# Todo:
 1. Add support for other documents besides CSV
 2. Add collaborative filtering
 3. Implement as a REST API


