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koolsla (Coleslaw) is a recommendation tool based on Machine Learning with contents. Developed with the power of tf-idf and Cosine Similarity.

The user gives a natural number that corresponds to the ID of a unique dish name. Through tf-idf the plot summaries of 424508 different dishes that reside in the dataset, are analyzed and vectorized. Set of dishes (number set by user) is chosen as recommendations based on their cosine similarity with the vectorized input.

koolsla is mainly an educational project.

You can install koolsla using:

$ pip install koolsla

The code is hosted at https://github.com/abdullahselek/koolsla

Check out the latest development version anonymously with:

$ git clone git://github.com/abdullahselek/koolsla.git
$ cd koolsla

To install test dependencies, run either:

$ pip install -Ur requirements.testing.txt

The test suite can be run against a single Python version which requires pip install pytest and optionally pip install pytest-cov (these are included if you have installed dependencies from requirements.testing.txt)

To run the unit tests with a single Python version:

$ py.test -v

To also run code coverage:

$ py.test --cov=koolsla

To run the unit tests against a set of Python versions:

$ tox

Import recommender:

from koolsla import recommender

Getting recommendations with dish id and recommendation count:

// Returns dictionary of tuples [(dish_id_1, similarity_ratio1), (dish_id_2, similarity_ratio2), (dish_id_3, similarity_ratio3)]
recommendatons = recommender.recommend(82, 3)

After getting the code from https://github.com/abdullahselek/koolsla, run command:

$ pip install -r requirements.txt

And it's ready to use, there is detailed help menu which you can follow. One of the most used function for recommendation:

$ python koolsla.py -d 25 --recommend 3

For the help menu:

$ python koolsla.py --help