Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Getting item recommendations (Amazon SDKs)

The following code samples show different variations of how to get item recommendations with the Amazon SDKs.

Getting item recommendations

The following code shows how to get Amazon Personalize recommendations for a user from a campaign. To get recommendations from a recommender, replace the campaignArn parameter with the recommenderArn.

Specify the ID of the user you want to get recommendations for, and the Amazon Resource Name (ARN) of your campaign or recommender. A list of the top 10 recommended items for the user displays. If you use User-Personalization-v2, each recommended item includes a list of reasons for why the item was included in recommendations. For more information, see Recommendation reasons (User-Personalization-v2).

To change the number of recommended items, change the value for numResults. The default is 25 items. The maximum is 500 items. If you used a RELATED_ITEMS recipe to train the solution version backing the campaign, replace the userId parameter with itemId and specify the item ID.

If you enabled metadata in recommendations for your campaign or recommender, you can specify the Items dataset metadata columns to include in the response. For a code sample, see Including item metadata with recommendations. For information about enabling metadata, see Item metadata in recommendations.

If you recorded events for a user before they logged in (an anonymous user), you can get recommendations for this user by providing the sessionId from those events as if it is their userId. For more information about recording events for anonymous users, see Recording events for anonymous users.

SDK for Python (Boto3)

import boto3

personalizeRt = boto3.client('personalize-runtime')

response = personalizeRt.get_recommendations(
    campaignArn = 'Campaign ARN',
    userId = 'User ID',
    numResults = 10
)

print("Recommended items")
for item in response['itemList']:
    print (item['itemId'])

SDK for Java 2.x

    public static void getRecs(PersonalizeRuntimeClient personalizeRuntimeClient, String campaignArn, String userId) {

        try {
            GetRecommendationsRequest recommendationsRequest = GetRecommendationsRequest.builder()
                    .campaignArn(campaignArn)
                    .numResults(20)
                    .userId(userId)
                    .build();

            GetRecommendationsResponse recommendationsResponse = personalizeRuntimeClient
                    .getRecommendations(recommendationsRequest);
            List<PredictedItem> items = recommendationsResponse.itemList();
            for (PredictedItem item : items) {
                System.out.println("Item Id is : " + item.itemId());
                System.out.println("Item score is : " + item.score());
            }

        } catch (AwsServiceException e) {
            System.err.println(e.awsErrorDetails().errorMessage());
            System.exit(1);
        }
    }

SDK for JavaScript v3

// Get service clients module and commands using ES6 syntax.
import { GetRecommendationsCommand } from
  "@aws-sdk/client-personalize-runtime";

import { personalizeRuntimeClient } from "./libs/personalizeClients.js";
// Or, create the client here.
// const personalizeRuntimeClient = new PersonalizeRuntimeClient({ region: "REGION"});

// Set the recommendation request parameters.
export const getRecommendationsParam = {
  campaignArn: 'CAMPAIGN_ARN', /* required */
  userId: 'USER_ID',      /* required */
  numResults: 15    /* optional */
}

export const run = async () => {
  try {
    const response = await personalizeRuntimeClient.send(new GetRecommendationsCommand(getRecommendationsParam));
    console.log("Success!", response);
    return response; // For unit tests.
  } catch (err) {
    console.log("Error", err);
  }
};
run();

Including item metadata with recommendations

If you enabled metadata in recommendations for your campaign or recommender, you can specify the Items dataset metadata columns to include in the response. For information about enabling metadata for a campaign, see Item metadata in recommendations. For information about enabling metadata for a recommender, see Enabling metadata in recommendations.

The following code sample shows how to specify the metadata columns as part of your request for recommendations.

import boto3

personalizeRt = boto3.client('personalize-runtime')

response = personalizeRt.get_recommendations(
  campaignArn = 'Campaign ARN',
  userId = 'User ID',
  numResults = 10
  metadataColumns = {
    "ITEMS": ['columnNameA','columnNameB']
  }
)

print("Recommended items")
for item in response['itemList']:
  print(item['itemId'])
  print(item['metadata'])