Every algorithm ends by ordering what it found. The score blend is the account-wide setting that
decides how that ordering weighs its inputs — and it applies to every widget on the site at once.
Settings → Site profile → Recommendations. Changes reach live widgets within minutes; no
republish, no rebuild.
The four weights
| Weight | What it does | Default |
|---|---|---|
| Purchases vs views | How much a purchase outweighs a view when ranking popularity. | ×1 |
| Freshness | How much a recent item is favoured over an older one. | ×1 |
| Click & purchase feedback | How much the engine learns from what visitors actually clicked and bought out of the widgets themselves. | ×1 |
| Visitor’s price range | Per-visitor, not global — see below. | Off |
placeholder. It is where to start, and where to go back to if a change makes things worse.
Presets, by kind of site
A travel site is not a shop, and a publisher is neither. The presets fill the weights with editorial starting points
in one click, and you can tweak from there:
Balanced · Content & publishing · Travel &
booking · B2B · Services
The visitor’s price range
The fourth weight is different in kind from the other three: it is per visitor. It nudges up items
priced between roughly half and double that person’s own average — taken from what they have viewed, carted
or bought.
- It is off by default, and no preset ever switches it on. Turning it on is always
an explicit choice. - Visitors with no priced history are unaffected — there is no average to work from, so nothing is nudged.
Per-widget boosts, in context
A single widget can still be tuned on its own, under Fine-tune the algorithm in the recommendation
editor — Boost by profit margin, Boost new items, Reserve a slot for new items.
That card shows the account blend live underneath it (“Account-wide blend now: Purchases ×1 ·
Freshness ×1 · Click & purchase feedback ×1 · Visitor’s price range
Off”), so a per-widget boost is never set blind to the account setting it stacks on.
Related
- Recommendation algorithms — what each one picks before the blend orders it.
- Site profile settings
- The data behind recommendations