Getting started prerequisites - Amazon Personalize

Getting started prerequisites

The following steps are prerequisites for the getting started exercises.

  1. Create an AWS account and an AWS Identity and Access Management user, as specified in Sign up for AWS.

  2. Create an IAM policy that provides users and Amazon Personalize full access to your Amazon Personalize resources. Then attach the policy to your Amazon Personalize user or group. See Creating a new IAM policy.

  3. Create an AWS Identity and Access Management (IAM) service role, as specified in Creating an IAM role for Amazon Personalize. Use the role ARN when you upload the movie training data.

  4. Prepare your training data and upload the data to your Amazon S3 bucket:

  5. Give your Amazon Personalize service role permission to access your Amazon S3 resources, as specified in Giving Amazon Personalize access to Amazon S3 resources.

Creating the training data (Domain dataset group)

To create training data, download, modify, and save the movie ratings data to an Amazon Simple Storage Service (Amazon S3) bucket. Then give Amazon Personalize permission to read from the bucket.

To create the training data

  1. Download and unzip the movie ratings zip file, ml-latest-small.zip from MovieLens under recommended for education and development (F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. https://doi.org/10.1145/2827872).

  2. Open the ratings.csv file. This file contains the interactions data for this tutorial.

    1. Delete the rating column.

    2. Rename the userId and movieId columns to USER_ID and ITEM_ID respectively.

    3. Add an EVENT_TYPE column set the value for every record to watch. If you're using Microsoft Excel, you can set the EVENT_TYPE for every record by entering watch in the first cell in the column and then double-clicking the bottom-right corner of the cell. Your header should be the following:

      USER_ID,ITEM_ID,TIMESTAMP,EVENT_TYPE

      These columns must be exactly as shown for Amazon Personalize to recognize the data. The first few rows of your data should look as follows:

      USER_ID,ITEM_ID,TIMESTAMP,EVENT_TYPE 1,1,964982703,watch 1,3,964981247,watch 1,6,964982224,watch 1,47,964983815,watch 1,50,964982931,watch .... ....

    Save the ratings.csv file.

  3. Upload ratings.csv to your Amazon S3 bucket. For more information, see Uploading files and folders by using drag and drop in the Amazon Simple Storage Service User Guide.

  4. Give Amazon Personalize permission to read the data in the bucket. For more information, see Giving Amazon Personalize access to Amazon S3 resources.

Creating the training data (Custom dataset group)

To create training data, download, modify, and save the movie ratings data to an Amazon Simple Storage Service (Amazon S3) bucket. Then give Amazon Personalize permission to read from the bucket.

  1. Download and unzip the movie ratings zip file, ml-latest-small.zip from MovieLens under recommended for education and development (F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. https://doi.org/10.1145/2827872).

  2. Open the ratings.csv file. This file contains the interactions data for this tutorial.

    1. Delete the rating column.

    2. Replace the header row with the following:

      USER_ID,ITEM_ID,TIMESTAMP

      These headers must be exactly as shown for Amazon Personalize to recognize the data.

    Save the ratings.csv file.

  3. Upload ratings.csv to your Amazon S3 bucket. For more information, see Uploading files and folders by using drag and drop in the Amazon Simple Storage Service User Guide.

  4. Give Amazon Personalize permission to read the data in the bucket. For more information, see Giving Amazon Personalize access to Amazon S3 resources.