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About this interview
A technical interview on Recommendation Systems, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Compare collaborative filtering, content-based, and hybrid approaches
Explain matrix factorization and embedding-based retrieval for candidate generation
Describe the candidate-generation + ranking two-stage architecture
Explain the cold-start problem and ranking metrics (NDCG, MAP, recall@k)
Topics covered
Recommendation ParadigmsCollaborative FilteringContent-Based FilteringCold-Start ProblemFeedback SignalsMatrix FactorizationEmbedding-Based RetrievalTwo-Stage ArchitectureTraining DataRanking MetricsHybrid SystemsServing and ScalabilityBias and FairnessExploration and Exploitation
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Can you walk me through the three main families of recommendation approaches — collaborative filtering, content-based, and hybrid — and give me a quick intuition for when you'd reach for each one?
“Explain NDCG. How does it differ from precision at k, and why is it the right metric when relevance is graded rather than binary?
“How do you think about the re-ranking stage that follows candidate generation and ranking? What kinds of business logic or model-based filters would you apply here, and why do them at this stage rather than earlier?