How personalized recommendations differ from other strategies
Recommender systems select products on different principles. The difference is whose data is used:
| Strategy | Basis | Who it suits |
|---|---|---|
| Popular products | Aggregate across the whole store | Everyone equally, cold start |
| Similar items | Attributes of the current product | Product page context |
| Frequently bought together | Transaction history | Upsell and cross-sell |
| Personal | The profile of one specific shopper | Returning visitors |
Personalized recommendations require accumulated data about the shopper — at minimum a few sessions
with views and actions.
How the personal profile is built
The algorithm assembles an affinity profile from events:
- Product views — a weak signal, but high in volume
- Add to cart — a strong signal of intent
- Purchases — the strongest signal, weighted highest
- Search queries — intent stated explicitly
- Dwell time — scrolling down a product page beats a glance at it
From these events the system builds affinity vectors across brands, categories, price bands and
product attributes (colour, size, material).
Example affinity profile:
Brands: Nike 0.82, Adidas 0.61, Puma 0.38
Categories: Sneakers 0.91, Apparel 0.45
Price: $80–170 — the shopper's working range
Where to place personalized recommendations
Homepage — a For You widget above the fold or between promo blocks. High reach, and a good place
to personalize for returning visitors.
Category page (PLP) — You might like between listing rows or above them. Holds the shopper when
the current page is not quite what they wanted.
Product page (PDP) — Recently viewed and Picked for you below the description. Reduces the loss
of shoppers the current product did not convince.
Cart — Others buy this with it, based on the profile and the cart contents.
Email — a personal selection injected into a campaign sent from your own ESP, built from the
profile without any session context.
Tip: do not put personalized recommendations in a slot where another strategy performs better.
For a Similar items block on the PDP, content-based filtering is more precise. For cross-sell, use
frequently bought together. Keep personal selections for For You and Recently viewed.
Cold start: what to do with new users
There is no personal profile on a first visit. The algorithm accumulates data step by step:
- Visit 1 — contextual data only (device, traffic source, geolocation, time of day). Popular or
trending products are shown. - 3–5 product views — the first affinity signal. Recommendations start to adapt.
- 1 purchase — the profile becomes substantially richer. Personalization becomes accurate.
How to measure performance
For personalized recommendations, metrics belong in this order:
- Widget CTR — are recommended products being clicked
- Attributed revenue — revenue from orders that included a widget click, counted against an
attribution window of 7–14 days - Share of orders with a widget interaction
- RPV — revenue per visitor for shoppers who saw the widget, against a control group
Every one of these has to be compared in an A/B test: the personal algorithm against popular
products or against the previous algorithm.