# Getting real-time item recommendations
You can get real-time item recommendations from an Amazon Personalize recommender or custom campaign with the Amazon Personalize console, AWS Command Line Interface (AWS CLI), or AWS SDKs.
**Topics**
+ [
## Getting item recommendations (console)
](#get-real-time-recommendations-console)
+ [
## Getting item recommendations (AWS CLI)
](#get-item-rec-cli)
+ [
## Getting item recommendations (AWS SDKs)
](#get-item-rec-sdk)
## Getting item recommendations (console)
To get recommendations with the Amazon Personalize console, you provide the request information on the details page of either a recommender (Domain dataset group) or a custom campaign.
**To get recommendations**
1. Open the Amazon Personalize console at [https://console.aws.amazon.com/personalize/home](https://console.aws.amazon.com/personalize/home) and sign into your account.
1. Choose the dataset group that contains the campaign or recommender you are using.
1. In the navigation pane, choose **Campaigns** or **Recommenders**.
1. Choose the target campaign or recommender.
1. For a campaigns, under **Test campaign results**, enter your recommendation request details based on the recipe you used. For a recommenders choose **Test recommender** and enter your recommendation request details based on your use case.
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](recording-events.md#recording-anonymous-user-events).
1. Optionally choose a filter. For more information, see [Filtering recommendations and user segments](filter.md).
1. If you use contextual metadata, provide data for each context. For each context, for the **Key** enter the metadata field. For the **Value** enter the context data. For more information, see [Increasing recommendation relevance with contextual metadata](contextual-metadata.md).
1. If you enabled metadata in recommendations for your campaign or recommender, for **Items dataset columns**, choose the metadata columns that you want to include in recommendation results. For information about enabling metadata for a campaign, see [Item metadata in recommendations](campaigns.md#create-campaign-return-metadata). For information about enabling metadata for a recommender, see [Enabling metadata in recommendations for a domain recommender in Amazon Personalize](create-recommender-return-metadata.md).
1. If you want to promote a subset of items, optionally complete the **Promotion** fields. For more information see [Promoting items in real-time recommendations](promoting-items.md).
1. Choose **Get recommendations**. A table containing the user’s top 25 recommended items 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 with User-Personalization-v2](recommendations.md#recommendation-reasons).
## Getting item recommendations (AWS CLI)
Use the following code to get recommendations from a campaign. To get recommendations from a recommender, replace the `campaign-arn` parameter with the `recommender-arn`.
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 with User-Personalization-v2](recommendations.md#recommendation-reasons).
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\$1ITEMS recipe to train the solution version backing the campaign, replace the `user-id` parameter with `item-id` and specify the item ID.
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](recording-events.md#recording-anonymous-user-events).
```
aws personalize-runtime get-recommendations \
--campaign-arn campaign arn \
--user-id User ID \
--num-results 10
```
## Getting item recommendations (AWS SDKs)
The following code shows how to get Amazon Personalize recommendations for a user from a campaign with the AWS SDKs. 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 with User-Personalization-v2](recommendations.md#recommendation-reasons).
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\$1ITEMS 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 (AWS SDKs)](getting-recommendations-with-metadata.md#getting-recommendations-with-metadata-sdk). For information about enabling metadata, see [Item metadata in recommendations](campaigns.md#create-campaign-return-metadata).
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](recording-events.md#recording-anonymous-user-events).
------
#### [ 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 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();
```
------