This is the kind of question that feels deceptively scoped at first.
Start by clarifying the business goal (e.g., maximize revenue, engagement, or customer satisfaction) and the constraints (latency, real-time vs. batch). Then propose a two-stage system: first, generate candidate rows and their content, then rank them using a learning-to-rank model that predicts user engagement or conversion for each row. Emphasize offline evaluation with counterfactual methods and online A/B testing to validate the ranking.
Pro tip: Don't just focus on the model—highlight the importance of a feedback loop: logging user interactions with rows, handling position bias, and continuously retraining to adapt to changing user preferences and inventory.
Ask about the primary business metric (e.g., revenue, CTR, add-to-cart rate) and technical constraints (latency, scalability, real-time updates). This shapes the entire design.
Identify features for each row: user features (past behavior, demographics), row features (type, content, historical performance), and context features (device, time, page). Consider interactions between user and row.
Choose a learning-to-rank approach (e.g., pointwise, pairwise, listwise) with a model like GBDT or neural network. Train on logged data, addressing position bias via techniques like inverse propensity scoring.
Evaluate offline using metrics like NDCG or precision@k, but beware of bias. Plan an online A/B test to measure impact on business metrics, with proper randomization and guardrail metrics.
Deploy the model with a fallback (e.g., rule-based) for cold start. Set up logging and monitoring, and iterate with continuous retraining and experimentation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.