A very simple content-based recommendation engine. Great for learning, but also ready for real-world use.
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Updated readme with the correct requirement file
Latest commit 6c40259 Oct 21, 2016

readme.md

Content-based Recommendation Engine

Description

This is a production-ready, but very simple, content-based recommendation engine that computes similar items based on text descriptions. It comes with a sample data file (the headers of the input file are expected to be identical to the same file -- id, description) of 500 products so you can try it out.

This code was built to accompany this blog post that explains the motivation of the content engine and walks through the approach.

It is a flask-based REST webservice designed to be deployed to Heroku and relies on Anaconda for installation of the scientific computing dependencies, and Redis to store precomputed similarities.

Read the comments in engine.py to see how it works. It's very simple!

web.py contains the two endpoints:

  1. /train -- calls engine.train() which precomputes item similarities based on their descriptions in sample-data.csv using TF-IDF and cosine similarity.

  2. /predict -- given an item_id, returns the precomputed 'most similar' items.

Try it out!

First, make sure you have a local redis instance running. The engine expects to find redis at redis://localhost:6379, but you can set REDIS_URL env var if you have it running elsewhere.

You'll also need Anaconda installed (a scientific distribution of Python). Create a new virtualenv with the needed dependencies:

conda create -n crec --file conda-requirements.txt

Now, in the virtualenv (source activate crec):

python web.py

Then, in a separate terminal window, train the engine:

curl -X GET -H "X-API-TOKEN: FOOBAR1" -H "Content-Type: application/json; charset=utf-8" http://127.0.0.1:5000/train -d "{"data-url": "sample-data.csv"}"

And make a prediction!

curl -X POST -H "X-API-TOKEN: FOOBAR1" -H "Content-Type: application/json; charset=utf-8" http://127.0.0.1:5000/predict -d "{"item":18,"num":10}"

Deploying

This engine is designed to be deployed to Heroku. First, create a new app:

heroku create

You'll then need to set the buildpack for the app to use Anaconda; a packaging system for scientific computing libraries in Python.

heroku buildpacks:set https://github.com/kennethreitz/conda-buildpack.git

Be sure to set your environmental variables (in settings.py) and provide your own training data. Then just:

git push heroku master

Running tests

Well...technically it's running test, singular :)

python -m unittest tests