You're tasked with developing a new recommendation system, but the product team hasn't clearly defined the success metrics, and the available user interaction data is sparse and inconsistent. How would you approach this ambiguous situation to deliver a valuable recommendation engine?

Employ a CIRCLES framework. First, Clarify the business objective with stakeholders, defining 'success' qualitatively. Then, Identify user segments and their needs. Research existing recommendation systems and data sources. Construct a minimal viable product (MVP) with basic heuristics. Launch and iterate, gathering initial feedback. Evaluate performance using proxy metrics (e.g., click-through rate, time spent) and A/B testing. Finally, Synthesize learnings to refine metrics and the system. This iterative approach manages ambiguity and data scarcity by focusing on rapid learning and value delivery.

I'd approach this using a phased, iterative strategy, starting with a modified CIRCLES framework. First, I'd Clarify the core business objective with the product team, translating their vague 'success' into actionable, even if initially qualitative, goals (e.g., 'increase user retention,' 'improve content discovery'). Next, I'd Identify existing data sources, no matter how sparse, and perform extensive exploratory data analysis to understand their limitations and potential. I'd then Research common recommendation system patterns for similar domains to inform initial model choices. For the Construct phase, I'd prioritize a simple, rule-based or content-based MVP, leveraging available data and domain knowledge. This MVP would be quickly Launched with robust instrumentation for proxy metrics (e.g., click-through rate, session duration, conversion rates). Finally, I'd Evaluate performance, gather user feedback, and Synthesize these insights to progressively refine both the success metrics and the recommendation algorithm, moving towards more sophisticated models as data accumulates and clarity emerges. This ensures continuous value delivery while systematically addressing ambiguity and data scarcity.