I went straight into user types and search intent which felt right at the time, but I think I underweighted the ranking and relevance side of things.
Start by clarifying the goal and scope of the search engine (e.g., searching for users, hashtags, or content) and the target users. Then, outline a high-level design covering data sources, indexing, ranking, and user experience, while addressing key challenges like scale, relevance, and privacy. Finally, discuss how you would measure success and iterate.
Pro tip: Emphasize the unique aspects of Instagram's content (visual, social graph, real-time) and how they influence search design. Also, consider the trade-offs between different approaches and how you would prioritize features based on user needs and business goals.
Ask clarifying questions to understand what type of search (users, hashtags, posts, places) and the primary user needs. Define success metrics such as engagement, retention, or satisfaction.
List the data available: user profiles, captions, hashtags, comments, visual content, social graph, location, and real-time trends. Consider which signals are most relevant for ranking.
Outline the components: query understanding, indexing (text, visual, graph), retrieval, ranking, and presentation. Discuss how to handle scale, latency, and freshness.
Explain how to rank results using relevance signals (text match, visual similarity, social proximity, popularity) and personalize based on user behavior and interests.
Propose metrics to evaluate success (CTR, search success rate, time to result) and how to iterate using A/B testing and user feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.