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Recommender

Recommender

Algorithm
Recommender
Use Cases
Content Recommender, Game recommenders, Price category recommenders, Product recommender, Service Recommenders, etc.

Prerequisites

Before you start using our Recommender algorithm, ensure you’ve:
  1. Created a schema
  1. Integrated code to process your data and installed Begin’s iOS 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.

Procedure

In order to fetch recommendations, follow the initialization step:
import Begin

let beginWorker = BeginWorker.init(appId: "APP_ID", licenseKey: "LICENSE_KEY")
You can monitor the learning on your dashboard. In time, enough users will interact with your app and provide enough learning material for using the recommender.
💡
NOTE: The timeframe will vary depending on the number of users interacting with your software. For example, if you have 100k active users, your model is usually ready in two hours.
To use your trained algorithm, input the following code:
BeginWorker.shared().recommend(projectId: "project_id", userId: "user_id", success:  { [self] (result) in
    print("Begin API Response: \(result)")
  }) { (message) in
    print("Begin API Response \(message)")
  }
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.