ranking

One model, several objectives, one shared backbone. The signal is knowing exactly why sharing helps (regularization, data efficiency) and the failure mode that sinks naive setups. Here is the answer.

Ranking is not regression: getting the absolute score wrong is fine, getting the order wrong is not. The signal is the pointwise/pairwise/listwise split and why pairwise (LambdaMART) is still the practical default. Here is the answer.

For search and recommendation, A/B tests can be slow and noisy because they compare different users. Interleaving compares two rankers within the same user's results and detects winners with far less traffic. Here is how.

Rank inside a PARTITION BY and filter to rank <= N. The function you pick decides how ties behave, plus the window frame for a running total per group.

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.

E-commerce search is where retrieval, ranking, and business goals collide, and it carries a hard exact-match requirement (brand, size, SKU). The signal is query understanding plus hybrid retrieval plus business-aware ranking. Here is the design.

Notification systems fail by spamming users into muting them, and a mute is a permanently lost channel. The signal is treating it as a per-user send/hold/batch/suppress decision optimized for long-term trust, not for clicks or volume.

Every keystroke needs ranked suggestions back in tens of milliseconds, at search-engine QPS. The interesting part is the data structure and what you precompute, not the query path. Here is the design that survives the latency budget.

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.

Rank each item by relevance alone and you ship ten near-identical results and a filter bubble. The signal interviewers want is a re-ranking stage that scores the list as a set, plus the metric that proves it works.

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.

Sales has finite rep-hours, so the model's job is to point them at the leads worth a call. The signal interviewers want is value-weighted prioritization, careful conversion labels, and proof of revenue uplift over a holdout.

Showing the top 10 is easy. Showing a player their global rank among 50M others, updated live, is the real challenge. The answer hinges on the right data structure, sharding, and approximate ranks for the long tail. Here is how leaderboards scale.