I started with relevance signals and went into purchase history, ratings, seller reliability, that kind of thing.
Start by clarifying the goal of the ranking system (e.g., maximize revenue, customer satisfaction, or long-term loyalty) and the key constraints (e.g., scale, latency, fairness). Then outline a multi-objective ranking framework that balances relevance, personalization, business metrics, and diversity, and describe how you would measure and iterate on it.
Pro tip: Emphasize the trade-offs between short-term business metrics (like conversion) and long-term customer trust, and propose a hybrid approach that combines machine learning with business rules to avoid over-optimizing for any single objective.
Ask clarifying questions to understand the primary goal (e.g., revenue, customer satisfaction, market share) and constraints (e.g., latency, scalability, fairness, regulatory).
Identify key metrics such as click-through rate, conversion rate, revenue per session, customer lifetime value, and satisfaction scores, and discuss how to balance them.
Outline the types of signals (query relevance, personalization, product quality, popularity, business rules) and the machine learning approach (e.g., learning-to-rank) to combine them.
Explain how to handle trade-offs between objectives (e.g., relevance vs. profitability) and ensure diversity in results to avoid filter bubbles and maintain customer trust.
Describe offline evaluation (A/B testing, counterfactual analysis) and online experimentation (interleaving, multi-armed bandits) to continuously improve the system.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.