AWS Personalize Service

An AWS Personalize Service is a cloud-based personalization platform that is an AWS PaaS.

• Context:
  • It can (typically) have an AWS Personalize API.
  • It can support the creation of a Univariate Timeseries Modeling Systems and Multivariate Time Series Systems.
  • It can implement ARIMA, Exponential Smoothing (ETS), Non-Parametric Time Series (NPTS), CNN Quantile Regression (CNN-QR), Phophet, DeepAR+.
  • …
• Example(s):
  • AWS Personalize, 2020.
  • AWS Personalize, 2019.
  • AWS Personalize, 2018.
  • …
• Counter-Example(s):
  • Adobe Target.
  • Optimizely Personalization [1].
  • AWS Fraud Detector.
• See: Experience Optimization Platform.

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References

2018a

• "Amazon Personalize – Real-Time Personalization and Recommendation for Everyone.” Blog post
  • QUOTE: ... To help all AWS customers do the same, we are very happy to announce Amazon Personalize, a fully-managed service that puts personalization and recommendation in the hands of developers with little machine learning experience.

    How does Amazon Personalize simplify personalization and recommendation? As explained in a previous blog post, you could already build recommendation models on Amazon SageMaker using algorithms such as Factorization Machines. However, it’s fair to say that this requires extensive data preparation and expert tuning in order to get good results.

    Creating a recommendation model with Amazon Personalize is much simpler. Using AutoML, a new process that automates complex machine learning tasks, Personalize performs and accelerates the difficult work required to design, train, and deploy a machine learning model.

    Amazon Personalize supports both datasets stored in Amazon S3 and streaming data sets, e.g. events sent in real-time from a JavaScript tracker or server-side. The high-level process looks like this:

    • Create a schema describing the dataset, using Personalize-reserved keywords for user ids, item ids, etc.
    • Create a dataset group that contains datasets used for building the model and for predicting: user-item interactions (aka “who liked what”), users and items. The last two are optional, as we will see in the example below.
    • Send data to Personalize.
    • Create a solution, i.e. select a recommendation recipe and train it on the dataset group.
    • Create a campaign to predict new samples.
    • With data stored in Amazon S3, sending data to Personalize simply means adding your data files to the dataset group. Ingestion is triggered automatically.
  • Working with streaming data is different. One way to send events would be to use the AWS Amplify JavaScript library, which is integrated with the event tracking service in Personalize. Another way would be to send them server-side via the AWS SDK in your favourite language: ingestion can happen from any source with the code hosted inside of AWS (e.g. in Amazon EC2 or AWS Lambda) or outside.

2018b

• "Add Real-Time Personalization and Recommendations to Your Applications (AIM395).” Slides from AWS re:Invent 2018.

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