Use Cases
Content Recommender, Game recommenders, Price category recommenders, Product recommender, Service Recommenders, etc.
Visit Github for an example of Goodreads data:


Before you reach this stage, ensure you’ve:
  1. Created a schema
  1. Integrated code to process your data and installed Begin’s Python SDK
  1. Created a project with the objective “Recommender”
Learning usually takes about 30 minutes. Once complete, your machine learning algorithm is ready to be used on your mobile device, browser, connected vehicles, or server applications.


In order to fetch recommendations, follow the initialization step:
import Begin as bg applier = bg.AlgorithmsApplier(app_id=APP_ID, license_key=LICENSE_KEY)
To use your trained algorithm, input the following code
applier.recommend(project_id = PROJECT_ID, user_id = USER_ID, limit = 10, page = 1) Out: [{'object_id': '25190693', 'person_id': '11', 'similarity': 0.7192863865075461}, ... ]
This will return the recommended list of IDs for the given user.
If you performed the initial processing in Python, you can still use the algorithm with other SDKs like Android or iOS SDKs.