Interview Practice Questions

Explain the difference between explicit and implicit feedback in recommendation systems.

What is the NDCG metric and what is it used for?

When should you use an ALS model compared to SVD?

What are the advantages and disadvantages of hybrid approaches in recommendation systems?

Explain how LightFM works and what it is used for.

What is the cold start problem and how can it be addressed?

Explain the concept of contextual bandits and how they are applied in recommendation systems.

How can graph neural networks be used to enhance recommendation systems?

When and why should A/B testing be used to evaluate recommendations?

What is collaborative filtering and how does it differ from content-based filtering?

Explain how the Singular Value Decomposition (SVD) method works.

What is cold start for users and how can it be addressed?

Compare the metrics Precision@K and Recall@K.

Why is it important to balance exploration and exploitation in recommendation systems?

Explain how Graph Neural Networks (GNN) work in the context of recommendations.

What is LightFM and how does it combine collaborative and content-based filtering?

How do two-tower models work in recommendation systems?

Why is A/B testing an important aspect of evaluating recommendation systems?

What is NDCG and how is it used to evaluate recommendation systems?

Compare matrix factorization models such as SVD and NMF in terms of their application in recommendation systems.