User Affinity

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Overview

User Affinity is a sort expression that personalizes browse results for each individual shopper. It appears in the Add Expression menu as User Affinity with a New badge. Like Soft Boost, User Affinity does not sort on its own — it is a modifier that lifts products matching the current shopper’s learned tastes within the next sort expression below it. Because it always modifies a following sort attribute, User Affinity cannot be the last expression in a sort order: it requires a sort attribute beneath it to modify. User Affinity only ever promotes matching products. It never demotes — products that don’t match a shopper’s affinities keep their organic position.

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How User Affinity works

As a shopper browses, Layers builds a lightweight picture of their tastes from their own activity. When that shopper loads a collection that uses a User Affinity expression, products that match their inferred preferences are lifted up within the underlying sort, while everything else stays in its organic order. If a shopper has no learned affinities yet — for example, a brand-new visitor — the expression is automatically skipped and the base sort applies unchanged. Shoppers always see a complete, sensibly ordered collection.

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Signals

Affinities are learned from each shopper’s own cart adds and purchases within their session context:

• Purchases weigh more heavily than cart adds. A completed purchase is a stronger signal of taste than an item a shopper added to their cart.
• Signals are interpreted in the shopper’s session context, so the personalization reflects that shopper’s recent behavior.

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Eligible attributes

Affinities are powered only by attributes in the Categorical and Feature attribute classes. These are the attributes that describe what a product is — brand, color, material, product type, size, and similar characteristics — so matching reflects the kinds of products a shopper gravitates toward. If a store has no categorical or feature attributes, User Affinity is unavailable. When you add the expression, the form shows how many categorical and feature attributes currently power affinities.

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Affinity weights

The Affinity Weights picker lets you choose which categorical and feature attributes drive matching and tune a per-attribute weight. Raise the weight on the attributes that matter most for your catalog (for example, brand for a multi-brand store, or material for an apparel store) and lower or disable attributes that are less meaningful.

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Configuration parameters

User Affinity reuses the same boost-mode model as Soft Boost.

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Boost mode

Determines how matching products are lifted within the underlying sort.

• Amplify strong performers (multiplicative) — pushes matching products that already rank well on the underlying metric even higher. Use this when matches have real metric data, so personalization reinforces proven performers.
• Lift matches into view (additive) — surfaces matching products regardless of their metric value. Use this for new arrivals, restocks, or products with little performance data, where you want affinity matches to appear even without a strong base score.

The UI defaults to Amplify strong performers.

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Boost strength

A global multiplier on the affinity lift. Higher values produce a stronger personalization effect; lower values keep the lift subtle so organic ranking remains dominant.

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Percentile target

Additive mode only. The percentile that matching products are lifted toward, expressed against the underlying metric. A higher percentile lifts affinity matches further up the results.

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Min positions between / decay rate

Anti-clumping spacing controls. These keep affinity matches from bunching together so personalized results still feel varied across the page.

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Direction

Controls the direction of the affinity lift relative to the underlying sort.

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Preview as a shopper

You don’t have to wait for live traffic to see how User Affinity behaves. The Preview profile shopper picker lets you simulate a shopper’s cart and purchase products, then preview how User Affinity reorders a collection for that profile. When you select or build a profile, the preview shows:

• The reordered collection as that shopper would see it, with affinity matches lifted into place.
• A plain-language summary of the shopper’s inferred preferences, so you can sanity-check that the affinities make sense before publishing.

Use this to compare how the same collection looks for different kinds of shoppers and to confirm your Affinity Weights and boost settings produce the experience you intend.

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Best practices

• Always leave a sort attribute beneath it. User Affinity modifies the expression below it, so it can never be the last expression in the sort order.
• Pick the right boost mode. Use Amplify strong performers when affinity matches already have metric data, and Lift matches into view for new arrivals or low-data products you still want personalized into view.
• Tune Affinity Weights to your catalog. Emphasize the categorical and feature attributes that best capture taste in your store and de-emphasize the rest.
• Preview across profiles. Use Preview profile to simulate several shopper types before publishing, and confirm the inferred-preference summary matches your intent.
• Start subtle. Begin with a modest boost strength so personalization complements your organic ranking rather than overwhelming it.

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See also

• Soft Boost — the same boost-mode model, condition-based rather than personalized
• Attribute classes — the categorical and feature classes that power affinities
• Preview & annotations — preview sort orders and simulate shopper profiles
• Sort orders — full guide to creating and managing sort orders