Best Recommender Systems Test - 34+ MCQs: Recommender Systems test: Dive Deep into Recommendation Algorithms with Challenging Questions and Answers

1. Which technique is beneficial for handling sparse user-item interaction matrices?

Matrix Completion

Regularization

Feature Engineering

Ensemble Methods

2. Which evaluation metric is used to measure the novelty of recommendations?

Coverage

Diversity

Precision

Recall

3. Which challenge is associated with deploying recommender systems in real-world e-commerce platforms?

Ensuring algorithmic fairness

Handling user privacy concerns

Optimizing for high throughput

Improving item discoverability

4. Which technique leverages matrix factorization for generating recommendations?

Singular Value Decomposition (SVD)

K-nearest Neighbors (KNN)

Decision Trees

Linear Regression

5. What is the role of normalization in recommender systems?

To reduce data dimensionality

To scale features uniformly

To remove outliers

To improve system security

6. How does transfer learning benefit recommender systems?

By leveraging knowledge from related tasks

By simplifying data preprocessing

By enhancing model generalization

By reducing the need for labeled data

7. Which of the following is a collaborative filtering technique?

User-based Collaborative Filtering

Content-based Filtering

Association Rule Learning

Deep Learning

8. Which technique is commonly used in collaborative filtering?

K-Nearest Neighbors

Decision Trees

Linear Regression

Naive Bayes

9. What are latent factors in matrix factorization techniques?

Hidden features used to represent users or items

Algorithmic errors in predictions

Temporal factors affecting recommendations

Feedback mechanisms in recommendation systems

10. What does the term 'Cold Start Problem' refer to in the context of recommender systems?

The challenge of recommending items for new users or items

Encrypting user data during recommendation

Compressing large recommendation datasets

Filtering out irrelevant items from recommendations

Recommender Systems MCQ Test 3