Serverless ML’less recommendation engines

Recommendation engines are most common in today’s business. However, building a fully automated recommendation platform is not an easy task. As soon as we think about recommendations, there are so many things that come into consideration.

  • Machine learning and AI
  • Huge computation needs
  • The cost involved in the recommendation
  • Accuracy in predictions and so on

The modern cloud platforms provide options to implement recommendations with very minimal steps.

Who are the major vendors in this place?

1 – Google’s Recommendation AI – Enables retailers to deliver highly personalized product recommendations at scale using state-of-the-art machine learning models. This is easy to implement if the solution already uses a merchant center and tag manager from Google. Google recommendation engine is being migrated to Retail API.

Google Recommendations/Retail AI

2 – AWS Personalize – Enables developers to build applications with the same machine learning (ML) technology used by for real-time personalized recommendations – no ML expertise required. The main advantage of the ‘personalize’ is the multiple domains it offers to build personalization.

AWS – Personalize

3 – Adobe Target – Helps you optimize and customize real-time suggestions across channels, apps, pages, email messages, and other delivery options to increase engagement and conversion while reducing management effort. This is a recommended solution if the application already uses other Adobe products like Target, Analytics, and Magento.

Adobe Target – Personalization

What are the most common steps involved in implementing the serverless recommendation engine?

  1. Collective product catalog
  2. Unified user events
  3. Tune the algorithm based on the business needs
  4. Algorithm tuning interval
  5. Determine placement of the recommendation in the application

Where can the recommendations help in other areas?

  1. A/B testing
  2. Multivariate testing
  3. User engagement
  4. Business promotions
  5. Alternate items based on the available inventory
  6. Sales predictions

What are the advantages of the serverless approach?

Serverless recommendation engines come with all the benefits that the serverless cloud has to offer. However, the main advantage is the least effort is needed in knowing ML/AI.

What are the drawbacks of this approach?

  1. As all processing happens under the cover, debugging gets a bit complicated
  2. Understanding pricing can be a bit tricky

There are only a handful of major cloud providers and almost all the providers have competing functionalities and quoting vendor lock-in as a drawback wouldn’t make sense anymore.

Some considerations while building recommendation engines –

  1. How to avoid unconscious bias in the recommendation?
  2. What are the ways to introduce new products?

Share your thoughts on the out-of-the-box recommendation engines.

Happy learning!

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