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Question 188 - MLS-C01 discussion

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A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.

How should a data scientist adjust the solution?

A.
Use the event tracker in Amazon Personalize to include real-time user interactions.
Answers
A.
Use the event tracker in Amazon Personalize to include real-time user interactions.
B.
Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
Answers
B.
Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
C.
Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
Answers
C.
Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
D.
Add event type and event value fields to the interactions dataset in Amazon Personalize.
Answers
D.
Add event type and event value fields to the interactions dataset in Amazon Personalize.
Suggested answer: A

Explanation:

The best option is to use the event tracker in Amazon Personalize to include real-time user interactions. This will allow the model to learn from the feedback of the customers during the marketing campaign and adjust the recommendations accordingly. The event tracker can capture click-through, add-to-cart, purchase, and other types of events that indicate the user's preferences. By using the event tracker, the company can improve the relevance and freshness of the recommendations and avoid the decrease in sales.

The other options are not as effective as using the event tracker. Adding user metadata and using the HRNN-Metadata recipe in Amazon Personalize can help capture the user's attributes and preferences, but it will not reflect the changes in user behavior during the marketing campaign. Implementing a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker can also provide personalized recommendations, but it will require more time and effort to train and deploy the model. Adding event type and event value fields to the interactions dataset in Amazon Personalize can help capture the importance and context of each interaction, but it will not update the model with the latest user feedback.

References:

Recording events - Amazon Personalize

Using real-time events - Amazon Personalize

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asked 16/09/2024
George Sanchez
40 questions
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