Cold Start Strategies

New candidates enter the system with zero impressions. Promovolve uses three structural strategies depending on the state of the candidate pool — plus a continuous newcomer bonus that boosts under-sampled creatives across all phases.

Strategy 1: Full Cold Start

Condition: All candidates in the slot have 0 impressions.

Algorithm: Use categoryScore from the auction phase as a prior, with noise:

sampledCTR = categoryScore + random(-0.1, +0.1)
score = sampledCTR × CPM^α

The categoryScore = classifierConfidence × rankerWeight provides a signal from the TaxonomyRankerEntity. The ±0.1 noise ensures different candidates are selected across requests even when they have identical category scores.

Strategy 2: Warmup Phase

Condition: All candidates have fewer than 10 impressions (WarmupImpressions = 10).

Algorithm: Round-robin — always select the candidate with the fewest impressions:

select = argmin(candidate.impressions)

No Thompson Sampling runs during warmup. This guarantees every candidate gets at least 10 impressions before exploitation begins.

Why 10? At 10 impressions with a typical 2-5% CTR, the expected number of clicks is 0-1. The Beta distribution Beta(1, 10) or Beta(2, 9) has sufficient shape to distinguish different CTRs but is still wide enough for continued exploration after warmup ends.

Strategy 3: Partial Cold Start

Condition: Some candidates have data (≥ 10 impressions) and some are new (0 impressions).

Algorithm: Epsilon-greedy with ExplorationRate = 0.30:

if random() < 0.30:
    select randomly from cold candidates (impressions == 0)
else:
    run Thompson Sampling on all candidates

The 30% rate is aggressive by design — new candidates need data quickly. Once they accumulate impressions, Thompson Sampling’s Beta posterior handles exploration naturally.

Note: When Thompson Sampling runs in the else branch, it runs on all candidates including cold ones. Cold candidates use categoryScore + random(-0.15, +0.15) as their sampled CTR, plus a fold rate sampled from Beta(1, 1) (uniform [0, 1]) so cold creatives have a real fold component instead of a hardcoded zero. Without the fold prior, a cold creative’s engagement = sampledCTR + 0 could never beat a warm fold-rich one’s sampledCTR + 2.0 × foldRate — the dominant exploration mechanism would silently fail. They still benefit from the Newcomer Bonus on top.

Newcomer Bonus: Decaying Additive Boost

The three strategies above are structural — they redirect selection on specific conditions. Cutting across all of them is a continuous additive bonus applied during the score combiner that tilts the auction toward creatives with few impressions:

engagement = sampledCTR + FoldWeight × sampledFold + newcomerBonus(impressions)

newcomerBonus(n) = max(0, NewcomerBoost × (1 - n / NewcomerDecayImpressions))

With NewcomerBoost = 0.5 and NewcomerDecayImpressions = 50, the curve is:

This is a UCB (Upper Confidence Bound) flavored adjustment grafted onto Thompson Sampling. Pure TS already over-prefers high-variance candidates, but in practice the variance gain from a small impression count isn’t always enough to outpace a confident warm creative with established stats. The decaying bonus closes that gap explicitly: brand new creatives get a guaranteed exploration runway, and the boost fades smoothly so the system isn’t permanently subsidizing newcomers that turned out to be poor performers.

The bonus continues past WarmupImpressions = 10 (where the forced round-robin ends) so the creative gets help during the early exploitation period when its posterior is wide but no longer being protected by the warmup phase.

Strategy Selection Flow

Are all candidates at 0 impressions?
  └── Yes → Full Cold Start (categoryScore ± 0.1 noise)
  └── No  → Are all candidates under 10 impressions?
              └── Yes → Warmup (round-robin by fewest impressions)
              └── No  → Are some candidates at 0 impressions?
                          └── Yes → Partial Cold Start (30% epsilon-greedy)
                          └── No  → Standard Thompson Sampling

Key Constants