User-Personalization recipe

Training a new solution version with the User-Personalization recipe (console)

1. Open the Amazon Personalize console at https://console.amazonaws.cn/personalize/home and sign into your account.

2. Create a Custom dataset group with a new schema and upload your dataset with impressions data. Optionally include CREATION_TIMESTAMP and Unstructured text metadata data in your Items dataset so Amazon Personalize can more accurately calculate the age of an item and identify cold items.

   For more information on importing data, see Step 2: Preparing and importing data.

3. On the Dataset groups page, choose the new dataset group that contains the dataset or datasets with impressions data.

4. In the navigation pane, choose Solutions and recipes and choose Create solution.

5. On the Create solution page, for the Solution name, enter the name of your new solution.

6. For Solution type, choose Item recommendation to get item recommendations for your users.

7. For Recipe, choose aws-user-personalization. The Solution configuration section appears providing several configuration options.

8. In Solution configuration, if your Interactions dataset has EVENT_TYPE or both EVENT_TYPE and EVENT_VALUE columns, optionally use the Event type and Event value threshold fields to choose the interactions data that Amazon Personalize uses when training the model.

   For more information see Choosing the interactions data used for training.

9. Optionally configure hyperparameters for your solution. For a list of User-Personalization recipe properties and hyperparameters, see Properties and hyperparameters.

10. Choose Create and train solution to start training. The Dashboard page displays.

    You can navigate to the solution details page to track training progress in the Solution versions section. When training is complete, the status is Active.

Creating a campaign and getting recommendations (console)

When your solution version status is Active you are ready to create your campaign and get recommendations as follows:

1. On either the solution details page or the Campaigns page, choose Create new campaign.

2. On the Create new campaign page, for Campaign details, provide the following information:

   • Campaign name: Enter the name of the campaign. The text you enter here appears on the Campaign dashboard and details page.

   • Solution: Choose the solution that you just created.

   • Solution version ID: Choose the ID of the solution version that you just created.

   • Minimum provisioned transactions per second: Set the minimum provisioned transactions per second that Amazon Personalize supports. For more information, see the CreateCampaign operation.

3. For Campaign configuration, provide the following information:

   • Exploration weight: Configure how much to explore, where recommendations include items with less interactions data or relevance more frequently the more exploration you specify. The closer the value is to 1, the more exploration. At zero, no exploration occurs and recommendations are based on current data (relevance).

   • Exploration item age cut off: Enter the maximum item age, in days since the latest interaction, to define the scope of item exploration. To increase the number of items Amazon Personalize considers during exploration, enter a greater value.

     For example, if you enter 10, only items with interactions data from the 10 days since the latest interaction in the dataset are considered during exploration.

     Note

     Recommendations might include items without interactions data from outside this time frame. This is because these items are relevant to the user's interests, and exploration wasn't required to identify them.

4. Choose Create campaign.

5. On the campaign details page, when the campaign status is Active, you can use the campaign to get recommendations and record impressions. For more information, see Step 5: Get recommendations in "Getting Started."

   Amazon Personalize automatically updates your latest solution version every two hours to include new data. Your campaign automatically uses the updated solution version. For more information see Automatic updates.

   To manually update the campaign, you first create and train a new solution version using the console or the CreateSolutionVersion operation, with trainingMode set to update. You then manually update the campaign on the Campaign page of the console or by using the UpdateCampaign operation.

   Note

   Amazon Personalize doesn't automatically update solution versions you created before November 17, 2020.

To train a solution with the User-Personalization recipe using the Amazon SDK

1. Create a new solution using the create_solution method.

   Replace solution name with your solution name and dataset group arn with the Amazon Resource Name (ARN) of your dataset group.

   import boto3
   
   personalize = boto3.client('personalize')
   
   print('Creating solution')
   create_solution_response = personalize.create_solution(name = 'solution name', 
                               recipeArn = 'arn:aws:personalize:::recipe/aws-user-personalization', 
                               datasetGroupArn = 'dataset group arn',
                               )
   solution_arn = create_solution_response['solutionArn']
   print('solution_arn: ', solution_arn)

   For a list of aws-user-personalization recipe properties and hyperparameters, see Properties and hyperparameters.

2. Create a new solution version with the updated training data and set trainingMode to FULL using the following code snippet. Replace the solution arn with the ARN of your solution.

   import boto3
           
   personalize = boto3.client('personalize')
           
   create_solution_version_response = personalize.create_solution_version(solutionArn = 'solution arn', 
                                                                  trainingMode='FULL')
   
   new_solution_version_arn = create_solution_version_response['solutionVersionArn']
   print('solution_version_arn:', new_solution_version_arn)

3. When Amazon Personalize is finished creating your solution version, create your campaign with the following parameters:

   • Provide a new campaign name and the solution version arn generated in step 2.

   • Modify the explorationWeight item exploration configuration hyperparameter to configure how much to explore. Items with less interactions data or relevance are recommended more frequently the closer the value is to 1.0. The default value is 0.3.

   • Modify the explorationItemAgeCutOff item exploration configuration hyperparameter parameter to provide the maximum duration, in days relative to the latest interaction, for which items should be explored. The larger the value, the more items are considered during exploration.

   Use the following Python snippet to create a new campaign with an emphasis on exploration with exploration cut-off at 30 days. Creating a campaign usually takes a few minutes but can take over an hour.

   import boto3
           
   personalize = boto3.client('personalize')
   
   create_campaign_response = personalize.create_campaign(
       name = 'campaign name',
       solutionVersionArn = 'solution version arn',
       minProvisionedTPS = 1,
       campaignConfig = {"itemExplorationConfig": {"explorationWeight": "0.3", "explorationItemAgeCutOff": "30"}}
   )
   
   campaign_arn = create_campaign_response['campaignArn']
   print('campaign_arn:', campaign_arn)

   With User-Personalization, Amazon Personalize automatically updates your solution version every two hours to include new data. Your campaign automatically uses the updated solution version. For more information see Automatic updates.

   To manually update the campaign, you first create and train a new solution version using the console or the CreateSolutionVersion operation, with trainingMode set to update. You then manually update the campaign on the Campaign page of the console or by using the UpdateCampaign operation.

   Note

   Amazon Personalize doesn't automatically update solution versions you created before November 17, 2020.

To get recommendations and record impressions

1. Call the get_recommendations method. Change the campaign arn to the ARN of your new campaign and user id to the userId of the user.

   import boto3
               
   rec_response = personalize_runtime.get_recommendations(campaignArn = 'campaign arn', userId = 'user id')
   print(rec_response['recommendationId'])             

2. Create a new event tracker for sending PutEvents requests. Replace event tracker name with the name of your event tracker and dataset group arn with the ARN of your dataset group.

   import boto3
           
   personalize = boto3.client('personalize')
   
   event_tracker_response = personalize.create_event_tracker( 
       name = 'event tracker name',
       datasetGroupArn = 'dataset group arn'
   )
   event_tracker_arn = event_tracker_response['eventTrackerArn']
   event_tracking_id = event_tracker_response['trackingId']
   print('eventTrackerArn:{},\n eventTrackingId:{}'.format(event_tracker_arn, event_tracking_id))               
                   

3. Use the recommendationId from step 1 and the event tracking id from step 2 to create a new PutEvents request. This request logs the new impression data from the user’s session. Change the user id to the ID of the user.

   import boto3
               
   personalize_events.put_events(
        trackingId = 'event tracking id',
        userId= 'user id',
        sessionId = '1',
        eventList = [{
        'sentAt': datetime.now().timestamp(),
        'eventType' : 'click',
        'itemId' : rec_response['itemList'][0]['itemId'],        
        'recommendationId': rec_response['recommendationId'],
        'impression': [item['itemId'] for item in rec_response['itemList']],
        }]
   )