Strategy question
How do we determine what content to recommend on Quora?
- Quora
- Strategy
- Medium
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What this question tests
Tests understanding of content ranking strategy for a Q&A platform, balancing relevance, quality, and creator incentives.
How to approach it
- Clarify the goal: Quora's content recommendation should maximize time spent reading useful answers while encouraging quality question-and-answer creation.
- Define input signals: topic affinity from a user's past views/upvotes, question freshness, and answer quality signals like upvotes, answer length, and credentialed-author status.
- Weigh personalization against discovery: blend a user's known interests with some exploration into adjacent topics to avoid an overly narrow filter bubble.
- Address quality control: downweight low-effort or duplicate answers, and account for early-upvote bias where the first answer gets disproportionate visibility regardless of quality.
- Address creator incentives: ranking should reward genuinely helpful, well-sourced answers, since if only viral takes get shown, expert contributors stop writing.
- Define success: session time and return rate, paired with a quality guardrail like reported answer helpfulness or flagged misinformation rate.
What a strong answer includes
- Names specific, realistic signals, topic affinity, upvotes, credentialed authorship, instead of a vague 'relevance algorithm.'
- Explicitly addresses early-upvote bias, a known real ranking problem for platforms like Quora and Reddit.
- Connects ranking design to creator incentives, recognizing this is a two-sided content marketplace, not just a viewer-facing feed.
- Balances personalization with discovery, avoiding a purely narrow, engagement-maximizing filter bubble.
Common mistakes
- Describing ranking purely as 'show what's most popular,' ignoring first-mover bias and quality signals.
- Ignoring the creator side, focusing only on what keeps readers engaged.
Likely follow-up questions
- How would you detect and demote low-quality but highly upvoted answers?
- How would you balance a new question with no engagement history yet?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop