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Machine Learning: Recommendation Systems Questions
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A recommendation system is a type of Information filtering Systems that seeks to predict the rating or the preference a user might give to an item. Basically, a recommendation system is an algorithm that suggests relevant items to users.

Some common Recommendation Systems with Machine Learning are:
1) Collaborative Filtering.
2) Content-based Filtering.
3) Personalized Video Ranker.
4) Candidate Generation Network.
5) Knowledge-based Recommender systems.

Machine Learning: Recommendation Systems Questions
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18 Questions

1. What is the simplest baseline for RS?

2. what is a limitation of MF?

3. Differential privacy

4. Collaborative filtering can be viewed as ____________ task.

5. Power Law of Participation

6. Grey sheep

7. Given the Power Law of Participation, what do you do?

8. What is an algorithm for privacy in collaborative filtering?

9. Shilling attacks

10. If you have a new item, what is the best recommendation?

11. What is the power law curse?

12. What is the general plan for collaborative filtering?

13. What are the 3 approaches to recommender systems?

14. What are 2 nice-to-have properties of Recommender Systems?

15. Parametric models _________ number of parameters. On the other hand, nonparametric models_______________.

16. What are gottacha for building RS on implicit feedback?

17. If you have new user, what is the best recommendation?

18. What is a neighborhood in RS?