This question evaluates a candidate's competency in designing a production-grade machine learning ranking system for re-ranking autocomplete suggestions, encompassing label definition for long-term success, counterfactual bias correction, feature engineering (including multilingual and Unicode handling), model selection, serving constraints, and evaluation strategies. It is commonly asked in the Machine Learning domain to assess practical application-level understanding of offline/online evaluation, bias mitigation, latency and memory trade-offs, and robustness to feedback loops and distributional drift.
You are building a re-ranking system for search autocomplete. For each keystroke, a candidate generator proposes suggestions; your job is to re-rank them to maximize user success. You have impression-level logs with fields:
Assume suggestions are shown as a slate (top K suggestions) each time a prefix changes.
Design the ML system and specify:
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