Recommender Systems MCQ Test: Recommender Systems MCQs - Practice Questions
1. What is the primary goal of a recommender system?
2. In hybrid recommender systems, how are techniques combined?
3. Why is the cold start problem significant in recommender systems?
4. Which technique is commonly used in collaborative filtering?
5. What role does adversarial training play in improving recommendation algorithms?
6. What is the main difference between collaborative filtering and content-based filtering?
7. Which technique is beneficial for handling sparse user-item interaction matrices?
8. What does the term 'Cold Start Problem' refer to in the context of recommender systems?
9. Which algorithm is often used for matrix factorization in recommender systems?
10. Which of the following is an advantage of a hybrid recommender system?
11. Which evaluation metric is used to measure the novelty of recommendations?
12. Which of the following is a collaborative filtering technique?
13. Which approach focuses on modeling the trustworthiness of users in recommender systems?
14. How do autoencoders contribute to the field of recommender systems?
15. Which challenge is associated with deploying recommender systems in real-world e-commerce platforms?
16. In a time-decay model, what does the decay factor represent?
17. What is the primary objective of reinforcement learning-based recommender systems?
18. How does temporal dynamics impact recommendation algorithms?
19. What are the limitations of matrix factorization techniques in recommender systems?
20. Which approach considers both user-item interactions and item-item relationships?