position bias

Asking a model which answer is better is one line of code. Getting a number you would let block a deploy takes position-bias control, a confidence interval, and a human-labeled set the judge is measured against. Here is the harness.

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.

Watch time is the label everyone optimizes and hardly anyone measures cleanly. You see minutes only for videos people chose to play, the distribution is savagely skewed, and the slot they saw it in shifted the number. The interview asks whether you can predict a biased label honestly.

A search box is a recommender carrying a strong prior: the query. The difficult pieces are reading ambiguous intent, retrieving in two stages within a tight latency budget, and learning from clicks without teaching the model that whatever appeared first is best.