MLA-C01 Question 147
Single answerA company wants to use Amazon SageMaker to build a recommendation system for its e-commerce platform. The system should predict which products users are likely to purchase based on their past interactions with the platform. Which built-in SageMaker algorithm should the company use for this task?
- A
XGBoost
- B
Factorization Machines
- C
K-Means
- D
BlazingText
Show answer and explanation
Correct answer: B
Explanation
Factorization Machines is the most appropriate SageMaker built-in algorithm for building recommendation systems. It is designed to handle sparse and high-dimensional data, which is common in user-product interaction datasets. This makes it ideal for predicting user preferences and personalizing recommendations based on past interactions.
- A. Incorrect.
XGBoost is a supervised learning algorithm commonly used for regression and classification tasks, but it is not specifically designed for recommendation systems.
- B. Correct.
Factorization Machines is a supervised learning algorithm that is well-suited for recommendation systems, particularly when handling sparse datasets, such as user-product interaction data.
- C. Incorrect.
K-Means is an unsupervised learning algorithm used for clustering tasks, and it is not applicable for building recommendation systems.
- D. Incorrect.
BlazingText is a supervised or unsupervised algorithm used for natural language processing tasks, such as text classification and word embedding, and is not suited for recommendation systems.