MLA-C01 Question 145
Single answerA machine learning engineer wants to build a recommendation system that suggests items to users based on historical user-item interaction data. The engineer prefers to use an AWS SageMaker built-in algorithm. Which algorithm is the most appropriate for this use case?
- A
Factorization Machines
- B
XGBoost
- C
BlazingText
- D
K-Means
Show answer and explanation
Correct answer: A
Explanation
The Factorization Machines algorithm in AWS SageMaker is specifically designed for tasks involving sparse datasets, such as recommendation systems. It models interactions between features effectively, making it the most appropriate choice for this use case. Other algorithms like XGBoost, BlazingText, and K-Means are designed for different types of machine learning problems and are not suitable for this specific requirement.
- A. Correct.
Factorization Machines is the correct algorithm for building recommendation systems based on sparse user-item interaction data. It is designed for supervised learning tasks such as regression and classification and is well-suited for this scenario.
- B. Incorrect.
XGBoost is a popular algorithm for structured data and is commonly used for tasks like regression, classification, and ranking. However, it is not specifically designed for recommendation systems with sparse data.
- C. Incorrect.
BlazingText is used for natural language processing tasks such as word embeddings and text classification. It is not suitable for building recommendation systems.
- D. Incorrect.
K-Means is a clustering algorithm used for unsupervised learning tasks. It is not designed for recommendation systems or supervised learning with sparse data.