Split cold start into three problems
New users, new items, and new sellers need different strategies. A generic "use popularity" answer is weak because it can lock the marketplace into incumbents.
Candidate generation
Use content-based retrieval from item text, images, category, price, geography, seller metadata, and onboarding signals. Add popularity priors, but cap them so they do not dominate every request.
For new users, ask lightweight onboarding preferences or infer from acquisition source, locale, and first-session behavior.
Exploration strategy
Use an exploration budget for new supply and uncertain user preferences. Contextual bandits can help, but start with controlled randomization if the team lacks reliable logging.
Metrics
- new-user activation and retention
- new-item first meaningful exposure
- seller liquidity
- conversion by cohort
- diversity and concentration metrics
- regret or opportunity cost from exploration
Failure modes
- Popularity trap: popular items get all exposure and new supply never learns.
- Low-quality exploration: users see irrelevant items and churn.
- Delayed labels: conversion can take time, so early proxies must be validated.
- Fairness tension: exposure fairness can conflict with immediate relevance.
What the architect signal looks like
End with a ramp plan: start with interpretable content-based priors, add exploration, measure new-cohort health, then introduce learned bandits once logging is trustworthy.