Recommendation system Viva questions

Top 20 Recommendation System Interview Questions and Answers

Top 20 Recommendation System Interview Questions and Answers

1. What is a recommendation system?

A recommendation system filters and suggests items like products or movies based on user preferences or behavior.

2. What are the main types of recommendation systems?

The main types are Content-based filtering, Collaborative filtering, and Hybrid systems.

3. How does collaborative filtering work?

It finds similar users or items and recommends based on shared preferences or behaviors.

4. What is the cold start problem?

It’s the challenge of recommending for new users or new items due to lack of interaction data.

5. How can you solve the cold start problem?

Use onboarding surveys, content-based filtering, or popularity-based methods initially.

6. What is matrix factorization?

It decomposes the user-item matrix into two smaller matrices representing latent features to find hidden patterns.

7. Role of cosine similarity in recommendation systems?

It measures the angle between vectors to find how similar users or items are.

8. What is overfitting in a recommendation model?

Overfitting happens when a model performs well on training data but poorly on unseen data due to learning noise.

9. Common evaluation metrics for recommendation systems?

Metrics include Precision, Recall, F1-score, RMSE, MAE, and NDCG.

10. Implicit vs. explicit feedback?

Explicit feedback includes ratings; implicit feedback includes clicks, views, or purchases.

11. User-based vs. Item-based collaborative filtering?

User-based recommends based on similar users; item-based on similar items.

12. How is content-based filtering implemented?

By analyzing item features and recommending items similar to those a user liked using methods like TF-IDF.

13. Deep learning in recommendation systems?

Deep learning uses neural networks and embeddings to learn complex user-item patterns for personalization.

14. What is a hybrid recommendation system?

It combines content-based and collaborative filtering to increase accuracy and overcome limitations.

15. What are latent factors?

Latent factors are hidden variables inferred from interaction data representing deeper user or item characteristics.

16. Real-world examples of recommendation systems?

Netflix, Amazon, Spotify, and YouTube use recommendation systems to personalize user experience.

17. What is sparsity in recommendation systems?

Sparsity refers to the lack of sufficient user-item interactions, making it harder to make accurate predictions.

18. How to handle scalability?

Use distributed systems, approximate nearest neighbors, and model optimization to scale recommendations.

19. Role of embeddings in recommendation systems?

Embeddings represent users and items in dense vector space, enabling efficient similarity calculations.

20. Dealing with biased data?

Bias is handled by balancing datasets, using fairness-aware algorithms, and continuous retraining.

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