MLA-C01 Question 133
Select 2A retail company wants to build a recommendation system to suggest products to customers based on their browsing and purchase history. Which machine learning algorithms would be most appropriate for this use case?
- A
Collaborative Filtering
- B
Linear Regression
- C
Principal Component Analysis (PCA)
- D
Content-Based Filtering
- E
K-Means Clustering
Show answer and explanation
Correct answers: A, D
Explanation
Recommendation systems are best built using algorithms like Collaborative Filtering and Content-Based Filtering. Collaborative Filtering focuses on relationships between users and items, while Content-Based Filtering focuses on the attributes of items and user preferences. Linear Regression, PCA, and K-Means Clustering are not designed to handle the specific requirements of recommendation tasks.
- A. Correct.
Collaborative Filtering is a popular machine learning algorithm used for recommendation systems. It predicts user preferences by analyzing historical interactions and finding patterns of similarity between users or items.
- B. Incorrect.
Linear Regression is used for predicting continuous numerical values based on input features. It is not suitable for recommendation systems, which focus on discrete choices or ranking items.
- C. Incorrect.
Principal Component Analysis (PCA) is a dimensionality reduction technique. While it can be used to preprocess data for recommendation systems, it is not itself a recommendation algorithm.
- D. Correct.
Content-Based Filtering is another widely-used approach for recommendation systems. It uses the attributes of items and the user’s preferences to recommend similar items.
- E. Incorrect.
K-Means Clustering is an unsupervised learning algorithm used for grouping data into clusters. It is not specifically designed for recommendation systems and is less effective for this use case.