Introduction
Enhancing Udemy's course recommendation system to better match learners with relevant content is a critical challenge that directly impacts user satisfaction, engagement, and the platform's overall success. I'll approach this problem by first clarifying key aspects of the current system and user behavior, then analyzing user segments and pain points. From there, I'll generate and evaluate potential solutions, prioritize them, and propose metrics for measuring success.
Step 1
Clarifying Questions
Why it matters: Determines the baseline we're working from and potential areas for improvement. Expected answer: Algorithm primarily uses user's past course selections and completion rates. Impact on approach: Would focus on expanding data inputs and refining the weighting system.
Why it matters: Influences how we approach continuity in recommendations across platforms. Expected answer: Yes, about 40% of users access courses on multiple devices. Impact on approach: Would prioritize a unified recommendation experience across devices.
Why it matters: Affects how we balance recommending proven courses vs. introducing new content. Expected answer: New courses added daily, but take time to be prominently recommended. Impact on approach: Would explore ways to more quickly surface relevant new content.
Why it matters: Aligns our solution with broader company objectives. Expected answer: Primary focus is on improving user retention and course completion rates. Impact on approach: Would prioritize solutions that encourage sustained engagement and course progress.
Tip
Thank you for those insights. Before we move on to user segmentation, I'd like to take a minute to organize my thoughts based on your responses.
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