recommendation

Recsys underpins half of applied ML, and this question checks whether you fit the approach to data availability and hold a real answer for the cold-start problem that breaks naive systems. Here is the foundations answer.

GNNs power recommendations, fraud, and molecule modeling by learning over graph structure. What matters is the message-passing mechanism, why k-hop matters, and why you keep them shallow. Here is the answer.

The most common ML system design round. Interviewers reward the funnel structure: candidate generation, then ranking, then re-ranking, with the right model at each stage plus an honest plan for cold start, freshness, and feedback loops. This lays out that structure.

How large recommenders and search draw candidates from millions of items in milliseconds. The signal is why the user and item towers stay separate, how that enables precomputed embeddings plus an ANN index, and where ranking takes over.

PYMK is graph recommendation at billion-node scale. The interviewer is testing for one instinct: do you generate candidates from the social graph, or naively try to score every pair? Here is the design that holds up through the follow-ups.

New users and new items carry zero interaction history, so collaborative filtering has nothing to work with. The candidates who pass treat it as a lifecycle, not a single trick. Here is how.

Matrix factorization is the classic collaborative-filtering method and the conceptual seed of modern embedding-based recsys. What matters is the latent-factor idea and how it leads to two-tower/neural models.

Recommending events (or jobs) breaks ordinary recsys in two spots: items expire and items are local. What matters is how you handle perishability, geography, and the fact that every item is a cold-start item. Here is the design.

A recommender trains on data its own past recommendations produced, so it learns to confirm its own beliefs. What matters is spotting the loop, naming the biases it breeds, and knowing the exploration and debiasing fixes that break it.

With billions of candidate items and only tens of milliseconds to choose the next 10, a feed is a latency problem first. The interview probes the two-stage architecture, how you set the objective when engagement fights integrity, and the biases that silently corrupt your training labels.

A track runs three minutes, a session runs an hour, and a fresh release has zero plays on launch day. The interview centers on mixing collaborative filtering with audio content embeddings, handling cold start on both sides, and reading a skip as the loud negative it really is.