MLS-C01 · Question #238
A retail company wants to build a recommendation system for the company's website. The system needs to provide recommendations for existing users and needs to base those recommendations on each…
The correct answer is C. Use an Amazon Personalize USER_PERSONALIZATION recipe to train a model. Create a real-. The User-Personalization (aws-user-personalization) recipe is optimized for all personalized recommendation scenarios. It predicts the items that a user will interact with based on Interactions, Items, and Users datasets. When recommending items, it uses automatic item
Question
A retail company wants to build a recommendation system for the company's website. The system needs to provide recommendations for existing users and needs to base those recommendations on each user's past browsing history. The system also must filter out any items that the user previously purchased. Which solution will meet these requirements with the LEAST development effort?
Options
- ATrain a model by using a user-based collaborative filtering algorithm on Amazon SageMaker.
- BUse an Amazon Personalize PERSONALIZED_RANKING recipe to train a model. Create a real-
- CUse an Amazon Personalize USER_PERSONALIZATION recipe to train a model. Create a real-
- DTrain a neural collaborative filtering model on Amazon SageMaker by using GPU instances. Host
How the community answered
(30 responses)- A7% (2)
- B13% (4)
- C77% (23)
- D3% (1)
Explanation
The User-Personalization (aws-user-personalization) recipe is optimized for all personalized recommendation scenarios. It predicts the items that a user will interact with based on Interactions, Items, and Users datasets. When recommending items, it uses automatic item
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