recommendation

Recsys underpins half of applied ML, and this question checks whether you match approach to data availability and have 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. The signal is the message-passing mechanism, why k-hop matters, and why you keep them shallow. Here is the answer.

The modal ML system design round. The structure interviewers reward is the funnel: candidate generation then ranking then re-ranking, with the right model at each stage and an honest plan for cold start, freshness, and feedback loops. Here is that structure.

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

PYMK is graph recommendation at billion-node scale. The interviewer is screening for one instinct: do you generate candidates from the social graph, or naively try to score all pairs? Here is the design that survives the follow-ups.

New users and new items have 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 root of modern embedding-based recsys. The signal is the latent-factor idea and how it leads to two-tower/neural models.

Recommending events (or jobs) breaks normal recsys in two places: items expire and items are local. The signal 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. The signal is recognizing the loop, naming the biases it breeds, and knowing the exploration and debiasing fixes that break it.