Introduction
To improve Quora's question suggestion system for helping users find more relevant topics to follow or answer, we need to analyze the current user experience, identify pain points, and develop innovative solutions. I'll approach this challenge by examining user segments, analyzing their journey, and proposing data-driven improvements that align with Quora's broader objectives.
Step 1
Clarifying Questions (5 mins)
Why it matters: This will help us focus our efforts on the most impactful areas. Expected answer: Increasing user engagement is the primary goal. Impact on approach: We'd prioritize solutions that encourage more frequent and meaningful interactions.
Why it matters: Understanding current performance will help us set realistic improvement targets. Expected answer: Key metrics include click-through rate on suggestions, time spent on the platform, and number of answers per user. These have been plateauing recently. Impact on approach: We'd focus on solutions that directly impact these specific metrics.
Why it matters: The level of available data will influence the sophistication of our solution. Expected answer: We have detailed data on user interactions, topic preferences, and browsing patterns. Impact on approach: We'd explore advanced machine learning techniques to better utilize this rich data set.
Why it matters: This could provide valuable insights and ensure our solution aligns with broader product strategies. Expected answer: Recent success with a collaborative filtering algorithm in the "Related Questions" feature. Impact on approach: We might explore adapting this algorithm for the question suggestion system.
At this point, you can ask interviewer to take a 1-minute break to organize your thoughts before diving into the next step.
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