The question was about a competitor's product, which threw me off a bit since I was interviewing at a food delivery company.
Start by clarifying the platform's goals and the guest experience metrics that matter, then segment guests and identify pain points in the current recommendation system. Propose a data-driven solution that balances personalization, diversity, and business objectives, and outline how you would measure success and iterate.
Pro tip: Anchor your answer in a north-star metric like booking conversion or guest satisfaction, and explicitly discuss trade-offs between short-term engagement and long-term guest trust. Showing awareness of marketplace dynamics (host supply, pricing, availability) will set you apart.
Ask clarifying questions to understand the platform's objectives (e.g., increase bookings, improve guest satisfaction, optimize host revenue) and define success metrics such as conversion rate, repeat booking rate, or NPS.
Segment guests (e.g., business travelers, families, budget-conscious) and identify their needs and frustrations with current recommendations, such as irrelevant listings, lack of diversity, or poor location matching.
Briefly assess the existing recommendation approach (e.g., collaborative filtering, content-based) and its limitations, such as popularity bias, cold-start issues, or failure to incorporate contextual factors like trip purpose or seasonality.
Suggest specific enhancements, such as incorporating contextual signals (trip type, group size, budget), using multi-armed bandits for exploration, adding diversity constraints, or leveraging embeddings for better personalization.
Outline an A/B testing plan with clear success metrics, guardrail metrics (e.g., host cancellation rates), and a process for continuous learning and refinement based on guest feedback and behavioral data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.