A company is planning to use an Amazon SageMaker prebuilt algorithm to create a recommendation model. The algorithm must be able to make predictions on high-dimensional sparse data. Which SageMaker al

The correct answer is B. Factorization Machines. The Factorization Machines algorithm in SageMaker is specifically designed for recommendation systems and works well with high-dimensional sparse data such as user-item interactions. It efficiently models variable interactions and is the best choice for building a recommendation

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Question

A company is planning to use an Amazon SageMaker prebuilt algorithm to create a recommendation model. The algorithm must be able to make predictions on high-dimensional sparse data. Which SageMaker algorithm should the company choose for the recommendation model?

Options

AK-nearest neighbors (k-NN)
BFactorization Machines
CPrincipal component analysis (PCA)
DSequence-to-Sequence (seq2seq)

How the community answered

(43 responses)

A
2% (1)
B
91% (39)
C
2% (1)
D
5% (2)

Explanation

The Factorization Machines algorithm in SageMaker is specifically designed for recommendation systems and works well with high-dimensional sparse data such as user-item interactions. It efficiently models variable interactions and is the best choice for building a recommendation model in this scenario.

Topics

#Amazon SageMaker algorithms#Recommendation models#High-dimensional sparse data#Factorization Machines

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