MLA-C01 Question 148
Single answerYou are tasked with building a recommendation system for an e-commerce platform to suggest products based on user behavior. The dataset contains user-item interaction data, such as views, purchases, and ratings. Which Amazon SageMaker built-in algorithm is the most appropriate for this use case?
- A
XGBoost
- B
Factorization Machines
- C
Linear Learner
- D
BlazingText
Show answer and explanation
Correct answer: B
Explanation
Factorization Machines is the most appropriate SageMaker built-in algorithm for this use case because it is designed to handle sparse data, such as user-item interactions, which are common in recommendation systems. The algorithm can learn latent factors for users and items, enabling it to make accurate predictions for recommendations.
- A. Incorrect.
XGBoost is a gradient-boosting algorithm primarily used for structured/tabular data and supervised learning tasks like regression and classification. While powerful, it is not optimal for recommendation systems based on user-item interactions.
- B. Correct.
Factorization Machines is specifically designed for large sparse datasets, such as user-item interaction matrices, making it highly suitable for recommendation systems.
- C. Incorrect.
Linear Learner is best suited for linear regression or classification tasks. It is not specialized for handling sparse user-item interaction data typically found in recommendation systems.
- D. Incorrect.
BlazingText is used for natural language processing (NLP) tasks, such as word embeddings or text classification, and is not suitable for recommendation systems.