I started with collaborative filtering and then pivoted to learning-to-rank pretty quickly, but I fumbled when they pushed on contextual constraints.
Start by outlining a standard multi-stage ranking pipeline (candidate generation, ranking, re-ranking) and then discuss how to incorporate user roles, context, and constraints at each stage. Emphasize the trade-offs between relevance, diversity, and business rules, and how you would evaluate and iterate on the system.
Pro tip: Show that you think beyond model accuracy by discussing how to balance multiple objectives (e.g., engagement, satisfaction, business metrics) and how to handle cold-start and fairness across user roles. Mention the importance of online evaluation and feedback loops.
Ask clarifying questions to understand the product goals, user roles, context (e.g., device, time), and any business or regulatory constraints. This ensures the solution aligns with stakeholder needs.
Propose a pipeline: candidate generation (e.g., collaborative filtering, content-based), ranking (e.g., learning-to-rank with GBDT or neural networks), and re-ranking (e.g., diversity, business rules). Explain how each stage handles constraints.
Describe how to featurize user roles (e.g., admin vs. regular user) and context (e.g., time, location) into the models, and how to use conditional models or multi-task learning to adapt rankings.
Explain how to enforce constraints (e.g., fairness, diversity, freshness) via post-processing, constrained optimization, or rule-based filters in re-ranking.
Outline offline metrics (e.g., NDCG, MAP) and online A/B tests, and discuss how to monitor for bias, feedback loops, and long-term impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.