I went straight to ranking signals and started listing things like recency, ratings, price competitiveness.
Start by clarifying the goal of search relevance for Agoda's context—helping users find the most suitable accommodations quickly. Then, outline a structured approach that covers user needs, relevance signals, ranking models, and evaluation metrics, emphasizing iterative testing and business impact.
Pro tip: Anchor your answer in Agoda's marketplace dynamics: balance relevance for both travelers and hotel partners, and highlight how you'd measure success through both user engagement and conversion metrics.
Clarify what 'relevance' means for Agoda's search results page: matching user intent (e.g., location, dates, preferences) with the most suitable properties. Align with business goals like conversion and partner fairness.
List the signals that influence relevance: user query (destination, dates, filters), property attributes (price, location, amenities, reviews), and contextual factors (seasonality, user history). Prioritize signals based on impact.
Propose a ranking approach: start with a baseline (e.g., sort by price or popularity), then layer personalized and contextual signals. Consider machine learning models (e.g., learning to rank) to optimize for multiple objectives.
Define offline metrics (e.g., NDCG, precision@k) and online metrics (e.g., CTR, booking conversion, revenue per search). Set up A/B testing to measure changes and guardrail metrics to avoid negative impacts.
Plan for continuous improvement: gather user feedback, analyze search logs, and run experiments. Balance short-term wins with long-term investments in relevance models.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.