Explain Recommendation Cold-Start Strategies.

Recommendation cold-start strategies are methods used to deliver relevant suggestions for new users or items when historical data is unavailable (#definition).

When to Use

• Onboarding new users with no activity history
• Recommending newly added items (songs, videos, products)
• After privacy resets (GDPR/ATT opt-outs)
• Seasonal or geographic shifts in behavior
• Launching a new product/app with zero initial data

Example

A shopping app suggests popular items in the user’s country, then gradually mixes in new items to learn preferences (#example).

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Why Is It Important

Cold-start handling improves day-1 engagement, reduces churn, and accelerates data collection for collaborative filtering and personalized ranking.

Interview Tips

• Break down cold-start into user-level, item-level, and system-level problems.

• Suggest layered solutions:

  • Content-based filtering
  • Popularity/heuristics
  • Contextual bandits → full personalization

• Mention metrics: CTR, coverage, regret minimization.

Trade-offs

• Popularity-first = fast engagement but risks homogenization
• Exploration = faster learning but short-term lower relevance
• Profile-based onboarding = accuracy vs. friction
• Privacy-conscious defaults = trust vs. weaker personalization (#tradeoffs).

Pitfalls

• Over-relying on global top-N lists
• Ignoring freshness and locale differences
• Asking long onboarding forms
• Missing feedback loops and diversity