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RecSys interview questions
Recommendations must find relevant items while respecting product constraints. Review candidate retrieval, ranking and evaluation. Explain how cold starts, user feedback and the difference between offline metrics and online experiments affect your choices.
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Name the main challenges in recommendation systems and ways to address them.
What would you do if an A/B test showed no difference in the primary metric, GMV?
Which neural network architectures for recommendation systems do you know?
Which offline and online metrics would you use for recommendations? How would you run an A/B test?
How would you collect target labels for a search ranking task?
Do you have experience with transformer models for ranking?
How would you incorporate user session context into ranking, for example when recommending accessories for a product the user has already selected?
How would you optimize ranking for margin or revenue rather than simply the number of sales?
How would you use multimodal product features, such as text and images, to improve ranking?
How does implicit ALS (alternating least squares) work with implicit feedback?
Describe your most recent recommendation systems experience: what did you do, how did you do it, and what were the results?
Describe your implementation of a transformer-based recommender, SASRec: data, training, and results.
What is the difference between NDCG and MAP in ranking?
What types of loss functions are used in ranking tasks?
Describe your personal contribution to item-to-item and transformer-based recommendation projects.
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