Start by clarifying the goal: ensuring accurate and up-to-date restaurant hours on Google Maps to improve user experience and trust. Then outline a multi-source data collection strategy (e.g., owner-provided, user-reported, automated extraction) and a validation process that combines automated checks with user feedback and machine learning. Finally, discuss how to handle conflicts and edge cases, and propose metrics to measure success.
Pro tip: Emphasize the importance of designing for scalability and freshness: hours change frequently (holidays, temporary closures), so the system must handle updates efficiently. Also, consider incentives for business owners to keep information accurate.
Define what 'accurate hours' means (e.g., regular hours, holiday hours, temporary closures) and the constraints (e.g., scale, latency, cost). Identify key stakeholders: users, business owners, Google.
Identify and prioritize data sources: business owners (via Google My Business), users (crowdsourcing), third-party APIs, web scraping, and phone calls. Design incentives for owners to provide updates.
Implement a multi-layered validation approach: cross-reference multiple sources, use ML to detect anomalies, leverage user reports (e.g., 'hours are wrong'), and possibly automated calls to verify. Establish a confidence score for each data point.
Define rules for resolving conflicting information (e.g., owner-provided > user-reported > third-party). Design a feedback loop to continuously improve accuracy, including periodic re-verification.
Define success metrics (e.g., percentage of restaurants with accurate hours, user reports of incorrect hours, owner engagement). Monitor and iterate on the system based on these metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.