Product design question

How would you enhance the AI-driven recommendation system at Flipkart.

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What this question tests

Whether the candidate can identify concrete weaknesses in an existing recommendation system and propose improvements grounded in e-commerce specifics.

How to approach it

  1. Clarify scope: homepage recommendations, product page 'similar items', or post-purchase suggestions, since each has different goals.
  2. Name likely pain points: over-recommending already-purchased or out-of-stock items, and weak personalization for infrequent or new shoppers (the cold start problem).
  3. Prioritize by impact: cold start for new users likely limits conversion the most, since Flipkart depends heavily on first-time buyer conversion.
  4. Propose improvements: blending collaborative filtering with contextual signals (festival season, browsing session intent) and using category-level popularity for new users until enough data exists.
  5. Define success as click-through rate and conversion rate on recommended items, segmented by new versus returning shoppers.

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