MLA-C01 Question 134
Single answerA company wants to improve its product recommendation system on their e-commerce platform. They currently have historical user interaction data, including clicks, purchases, and ratings. The goal is to recommend products that users are most likely to purchase, even if those products have not been interacted with by the specific user. Which machine learning algorithm would be the most appropriate to solve this problem?
- A
Linear Regression
- B
Collaborative Filtering
- C
K-Means Clustering
- D
Support Vector Machines (SVM)
Show answer and explanation
Correct answer: B
Explanation
The most appropriate algorithm for this recommendation system scenario is Collaborative Filtering. This approach is widely used in recommendation systems to analyze user interaction data and predict user preferences for items they have not interacted with. Linear Regression and Support Vector Machines are not designed for such tasks, and while K-Means Clustering can group similar users or items, it does not provide the same level of personalization as Collaborative Filtering.
- A. Incorrect.
Linear Regression models are used for regression tasks, where the goal is to predict a continuous numerical value. It is not suitable for recommendation systems that rely on user interaction data to predict preferences.
- B. Correct.
Collaborative Filtering is specifically designed for recommendation systems. It analyzes patterns in user interaction data (e.g., clicks, purchases, ratings) to recommend items to users that they are likely to interact with, even if they have not interacted with those specific items before.
- C. Incorrect.
K-Means Clustering is an unsupervised learning algorithm used for grouping similar data points into clusters. While clustering can provide basic group-based recommendations, it is not as effective as Collaborative Filtering for personalized recommendations.
- D. Incorrect.
Support Vector Machines (SVM) are used for classification or regression tasks. While SVMs can be used in certain recommendation scenarios, they are not the most appropriate choice for this use case when collaborative filtering can directly leverage user interaction data.