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Last pushed: a year ago
Short Description
Experimental recommendation engine API using Flask
Full Description

recontent

Content recommendation API
Using Python3

Running the app with Docker

  1. Install Docker, see https://www.docker.com/products/docker#/mac for the instructions to install it on a Mac.
  2. Then, in the top directory of this repository, you should be able to run the app using docker-compose. Just type docker-compose up --build. This will download the needed packages and run the app in the foreground.
  3. The first time you run the container, it will download and crunch the simple_wiki example. This will take about 10 minutes. You will see Running on http://0.0.0.0:5000/ (Press CTRL+C to quit) when it is done.
  4. Browse to localhost:5000, you should see 'Content recommendation rocks!' as message.
  5. Try out the test API by using curl: curl localhost:5000/api/recommend/v1.0/<corpus_name> -X GET -d 'url=http://www.symmetrymagazine.org/article/scientists-salvage-insights-from-lost-satellite', you should see a json document returned from the engine. See the list of available corpora below, you will need to add it to the API call.

Available corpora

The available corpora are:

  1. wiki-simple: this currently returns the document ids and scores of the 5 most relevant articles from the simple wikipedia corpus.

Adding a recommendation engine

If you write a new recommendation engine, it needs to inherit from Recommender base class, see /app/recommender/base.py. The API currently picks a recommendation engine randomly. To register your engine, add the module to the /app/recommender/ directory. Then, explicitly import the class in /app/app.py. This way, the class will be added to the list of subclasses of the Recommender base class.

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fajifr
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