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 beginai as bg worker = bg.BeginWorker(app_id=APP_ID, license_key=LICENSE_KEY)
To use your trained algorithm, input the following code:
worker.recommend(project_id = PROJECT_ID, user_id = USER_ID, limit = 10, page = 1)
This will return the recommended list of target IDs for the given user along with the similarity index.
Note: limit and page are optional values, if not provided the SDK will default to the top 20 results for page 1
If you performed the initial processing in Python, you can still use the algorithm with other SDKs like Android or iOS SDKs.
Note: Begin’s algorithms can learn continuously with additional user data while your code is deployed. Your project can learn from datasets on your mobile application, server or other applications without you having to worry about merging the learning or merging the data. Begin will take care of merging the learning.