Recommendation Algorithms — What Each One Picks

Every recommendation widget is a frame; what goes in it is decided per visitor, at the moment it renders, by the
algorithm you pick. This is what each one draws on, and what it needs before it can return anything.

Pick the page first — it changes the algorithm, not just the placement

The picker asks for a page before it offers you anything: Home, Category,
Product, Cart, Thank you, Search,
404.

That is not a filter on where the widget appears — the Target step decides that. Behavioural algorithms
anchor to something different on each page: the product being viewed on a Product page, everything
recently viewed on Home, the cart contents on Cart. The same algorithm on two pages is two different questions.

The groups

Group	What it is for
Personalized & Behavioral	Good defaults for home and category pages, where the visitor’s intent is not yet clear.
Cross-Sells, Co-Views & Upsells	Relations between items — what goes with what, what people looked at together.
Visitor History	This person’s own past: what they viewed, saved, bought.
Catalog Events	Things that happened to the catalog: a price drop, a restock, a new arrival.
Custom	Straight from the catalog with your own filters and no algorithm behind it.

Personalized & Behavioral

Personalized Recommendations — the visitor’s profile plus the behaviour of similar visitors.
Most Popular — the most viewed items in the period you choose.
Best Sellers — highest purchase count.
Most-Added to Wishlist — most often saved to favourites.
Trending Now — see below; this one is rate-of-change, not a ranking.
Inspired by Past Purchases — what people who bought the same things went on to view and buy.

Inspired by Past Purchases is not the visitor’s own order history. It is what other
people who bought the same things went on to buy. The card for their own purchases is Buy it Again,
under Visitor History. This is the single most common mix-up in the whole picker.

Trending Now — and whose trend

Trending Now compares today’s activity against the item’s own weekly average, so a steady
best-seller does not trend and a small item catching fire does.

Trending by picks the signal: Views, Purchases, Add to cart, or Wishlist.
From picks whose trend it is: the whole site, the visitor’s categories, or
the visitor’s interests. The last two are personal trending — what is taking off in the corner of
the catalogue this person actually cares about.

Site-wide is the default on purpose: a scoped trend depends on the visitor, so it is no longer safe to reuse in an
email or an ad. Give that up deliberately, not by accident.

Cross-Sells, Co-Views & Upsells

Cross-Sells — the bought-together relations, chosen under its own Based on menu.
Others Who Viewed Also Viewed, Others Who Viewed Also Added to Cart,
What Others Viewed Then Bought — the viewed-together family.
Up-Sells — from an up-sell catalogue you upload.

The co-view cards carry a From scope of their own: anywhere, the same category, or
the same interests — so “also viewed” can be held inside the aisle the visitor is standing
in. One relation, one card that names it: the bought-together flavours live on Cross-Sells, the viewed-together
flavours on the co-view cards.

Visitor History

Recently Viewed — what they looked at, most recent first.
Buy it Again — their own purchases.
Your Wishlist — what they saved.

Catalog Events

Price Dropped — items whose price fell.
Back in Stock — items that returned to stock after selling out.
New in Stock — items that arrived.

Back in Stock

The engine records the exact moment an item’s stock crosses in either direction, automatically, from your
normal feed imports. There is nothing to configure on the feed.

The window can be since the visitor’s last visit, or a fixed span from yesterday up to
three months — the same longer windows now available on Price Dropped.

It is offered on all seven page types, and Cart and Thank-you are deliberate: a restock of something a visitor
abandoned is one of the strongest recovery triggers there is. It combines with every filter and fallback, and with
Skip items the visitor already… — though leave has purchased before switched off if you
are writing a repurchase reminder.

Flag out-of-stock items rather than deleting them. An account whose feed hard-deletes sold-out rows
gives the engine no row to stamp when the item comes back, so the restock is invisible. Flagging is the recommended
feed setup. History also starts when the feature does — there are no stamps for restocks that happened
before.

Similar Items (AI), and Similar Content (AI)

Every other algorithm on this page needs traffic first: an item nobody has viewed, carted or bought is invisible to
them. Similar Items (AI) reads the catalogue itself — titles, brands, descriptions, categories
(for articles: title, topic, author, description) — and computes each item’s nearest neighbours by meaning.
A brand-new product gets recommendations on day one.

Switch it on: Catalog settings → data settings → Similar items (AI). One
switch covers both catalogues, products and content. Use it: the Similar Items (AI) card in
the recommendation editor, under Cross-Sells; Similar Content (AI) for articles.

It runs nightly, and only over items that were added or changed — so a stable catalogue
costs very little after the first pass.
It uses AI credits and respects the account’s daily AI cap; if it hits the cap it resumes
the next night.
Until you switch it on the card serves nothing and the widget’s fallback covers — the widget is never
broken, just not yet AI-fed.
Available on Home, Product, Cart and Thank-you for products; Home and Article for content.

Quality follows your catalogue text. Bare titles with no categories or descriptions produce weak
neighbours — the algorithm can only read what is there. Switching it off stops the nightly refresh; pairs already
computed keep serving.

Content recommendations

The content catalogue has the same shape, and three algorithms of its own worth naming:

What Readers Went On to Convert On — a read→goal collaborative filter: what readers of
the same articles engaged with before reaching a goal.
Content You Liked — this reader’s own favourited and saved articles; the per-person
twin of Most Liked Content.
Inspired by Reached Goals — seeded by the goals this visitor reached; the content twin of
Inspired by Past Purchases.

Output — how many, and what happens when it comes back short

Recommendations to show — how many slots.
Fallback algorithms — a second (and third) algorithm behind the first. A union is usually a
top-up rather than an only-when-empty fallback, so several can serve at once. The
QA bar will tell you which one actually filled
each slot.
Fill empty cells — off by default. Off means only what the algorithm returned is shown, and
an empty result leaves an empty block. The fallback above is separate and applies either way.

Fine-tune the algorithm

Gentle multipliers on the chosen ranking — Boost by profit margin, Boost new
items, Reserve a slot for new items. The algorithm still leads; these nudge.

These apply to this widget only. The footer shows the account-wide setting live —
“Account-wide blend now: …” — so you are never tuning one widget blind to the
account it sits in. See the score
blend.

Score blend & presets — the account-wide weights behind every “Recommended” ordering.
Recommendation fallbacks
Filters on recommendations
Interests — what the interest-scoped options draw on.
Recommendations aren’t appearing