Cold Start in Recommendation Models

Global Baseline (safe, non-personalized)

• Popularity & recency trends with minimal bias and diversity constraints.
• Use catalog‑level controls (e.g., de-dupe, diversity by category/brand).

Cohort‑Aware Prior (hierarchical / Bayesian flavor)

• Treat cohorts as priors on preferences, not hard filters.
• Combine cohort priors with global baseline via learned weights (per cohort).
• This avoids overfitting cohorts and preserves personalization as data accrues.

Content‑Based Recommendations (robust for item cold‑start too)

• Build item embeddings from metadata (text, category, attributes) and optionally image features.
• Recommend via nearest neighbors in embedding space; weight by cohort priors.

Lookalike Modeling (user embedding → nearest neighbor)

• Map new users to similar existing users using shared signals (device, referrer, campaign, geography, time-of-day).
• Use their top items as seed recommendations.

Contextual Bandits (controlled exploration for new users only)

• Allocate a small exploration budget (e.g., 5–10%) for new users using contextual bandits (Thompson Sampling or LinUCB).
• Keep bandit scope restricted to cold‑start segment; this avoids penalizing the rest.

Hybrid Model Router

• A router decides for each request:
  • If user has ≥N events → warm-start (ALS/BPR/LTR).
  • Else → cold-start route (cohort prior + content KNN + bandit).
• This isolation keeps warm users stable.

Graceful Handover

• As interactions arrive, gradually shift from cold‑start mixture to warm‑start model via learned blending (e.g., a calibrated meta‑model).