Recommendation Engines
Q1. Given this directed graph.
- Construct its adjacency matrix.
- How would this matrix change if the graph is now undirected?
- What can you say about the adjacency matrices of two isomorphic graphs?
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Machine Learning
Unsupervised Learning
Interview questions on Unsupervised Learning.
26 questions
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Recommendation Engines
Q2. What is a recommendation system?
Recommendation Engines
Q3. What is the importance of recommendation system?
Recommendation Engines
Q4. Define Items, Queries and Embedding in context of recommendation system?
Recommendation Engines
Q5. What are the main components of a recommendation system?
Recommendation Engines
Q6. What is candidate generation in a recommendation system, and what are the main approaches to accomplish it?
Recommendation Engines
Q7. How do content-based and collaborative filtering methods utilize embedding spaces to represent items and queries?
Recommendation Engines
Q8. Define similarity measures in context of recommendation system?
Recommendation Engines
Q9. What are different methods to measure degree of similarity?
Recommendation Engines
Q10. How should one choose the appropriate similarity metric for candidate generation in recommendation systems?
Recommendation Engines
Q11. How can we utilize content-based filtering in the candidate generation process?
Recommendation Engines
Q12. State the advantages and disadvantages of content-based filtering?
Recommendation Engines
Q13. What is the cold start problem in recommendation systems, and how can you address it?
Recommendation Engines
Q14. State explicit and implicit feedback with examples?
Recommendation Engines
Q15. How does collaborative filtering work?
Recommendation Engines
Q16. What are the advantages and disadvantages of collaborative filtering?
Recommendation Engines
Q17. How can user and item embeddings be learned using matrix factorization in collaborative filtering?
Recommendation Engines
Q18. What is the issue with learning embeddings using matrix factorization?
Recommendation Engines
Q19. How can we use Deep Neural Network model for recommendation?
Recommendation Engines
Q20. What is the benefit of using matrix factorization to learn embeddings over softmax DNN?
Recommendation Engines
Q21. What is the benefit of using softmax DNN over matrix factorization for learning embeddings?
Recommendation Engines
Q22. What strategies can be employed to efficiently compute the nearest neighbors in the embedding space of a recommendation system, and how do they work?
Recommendation Engines
Q23. Why should we avoid using candidate generators to rank items in a recommendation system, and what are the benefits of separating candidate generation from the ranking process?
Recommendation Engines
Q24. How does the choice of scoring function affects the ranking of items and quality of recommendations?
Recommendation Engines
Q25. What is the benefit of re-ranking in recommendation system?
Recommendation Engines
Q26. Imagine we build a user-item collaborative filtering system to recommend to each user items similar to the items they’ve bought before.
- You can build either a user-item matrix or an item-item matrix. What are the pros and cons of each approach?
- How would you handle a new user who hasn’t made any purchases in the past?