Serve Time: Thompson Sampling

The auction cached a pool of candidates. Now a reader is here, and the system must pick — quickly, per impression, for every slot on the page at once. This is where Promovolve learns.

One request per page

The ad tag sends a single batch request listing every slot. The site’s AdServer entity checks, in order: the requesting host is the site’s verified host (403 otherwise); the page’s classification is inside the freshness window (204 otherwise); reader dog-ears are honored first (a valid pin takes its slot and skips everything below); and the pacing gate — see Budget Pacing — decides whether this impression should be spent at all.

What remains is selection over the candidate pool.

Score by sampling, not by averages

Each creative carries a rolling 60-minute window of impressions, clicks, and folds, bucketed by minute. (CTR below is click-through rate: of the readers who saw this creative, the share who opened it.) From it, two Beta distributions:

sampledCTR  ~ Beta(clicks + 1,  impressions − clicks + 1)
sampledFold ~ Beta(folds + 1,   impressions − folds  + 1)

Every request, every candidate draws from its distributions — it does not use its mean. The score:

engagement = sampledCTR + 2.0 × sampledFold + newcomerBonus
score      = engagement × CPM^α

Sampling is the whole trick (this is Thompson Sampling). A creative with 1,000 impressions draws values tightly around its true rate; a creative with 9 impressions draws wildly. Uncertain creatives therefore sometimes draw high and win — exploration — in exact proportion to how uncertain they are. No exploration schedule, no epsilon, no phases: confidence itself allocates the experiments, and as data accumulates the draws narrow and the best creative simply wins most often.

Clicks here are magazine-opens — in this format the click is the expand. Folds are dog-ears, weighted 2× because a reader bookmarking the ad is a stronger signal than one merely opening it.

The exponent α is the publisher’s one tuning knob (bidWeight): 0.5 by default, so a $10 bid beats a $1 bid by ~3.2×, not 10× — price matters, but a creative readers engage with can beat a richer one that they ignore. Publishers wanting discovery set 0.3; wanting revenue, 0.7.

Cold start, inside the same formula

The cold start is the ranking problem’s awkward first day: how do you score an ad with no history? New creatives get two helps, both expressed as scores — there is no separate cold-start code path, round-robin, or forced serving:

• Zero impressions: the CTR draw is replaced by the creative’s category affinity score (how well its category matched the page at auction time) plus uniform noise of ±0.15, and the fold draw comes from a Beta(1, 3) prior. A relevance-informed guess instead of a coin flip.
• First 50 impressions: an additive newcomer bonus starting at +0.5 and decaying linearly to zero — a guaranteed runway against confident incumbents, gone by the time the creative has real data.

Selection stays a single argmax — take the highest score, nothing else — at every lifecycle stage.

Filling the page

Slots are assigned greedily, largest first, each taking the remaining candidate with the highest sampled score — under two hard rules: a creative appears at most once per page, and a campaign appears at most once per page. A page plastered with one advertiser is bad for the reader, the publisher, and the advertiser; the constraint is enforced at assignment time, where it can’t be gamed.

The winner’s budget is reserved before the response is sent, and the impression is recorded server-side at selection — billing does not depend on a tracking pixel surviving the reader’s browser. Clicks (expands), folds (dog-ears), and CTA events arrive later through tracking endpoints and update the Beta windows. Dog-eared re-encounters update none of this: they live in separate counters, excluded from learning, spend, and reporting’s primary metrics.