Overview
Personalized Outfit Recommendations automatically generate complete outfit suggestions using enriched product attributes and machine learning. These recommendations highlight items that work well together, based on a shopper’s style and intent, helping to answer common questions like “How can I pair this?” or “How many ways can I wear this?”
Typically displayed on the product detail page, the feature suggests garments, shoes, and accessories that complement the viewed item. Suggestions can also appear on basket pages, post-purchase pages, and in post-purchase emails.
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Personalized outfit recommendations showing complementary garments, shoes, and accessories.
Looking for alternative products instead of outfit pairings? See Similar Items.
Benefits
Attribute-Based Styling: Outfits are generated using enriched product attributes for accurate, style-aware combinations.
Higher Basket Value: Shoppers are more likely to add additional items when seeing complete looks.
Faster Decision-Making: Shoppers receive instant styling inspiration and learn how to wear an item effectively.
Flexible Implementation: Works through either a ready-to-use widget or a customizable API integration.
Consistent Brand Experience: The layout can be customized to match the website’s design.
How outfit recommendations are built
Each outfit is built around a single starting item: the product a shopper is viewing, has added to their basket, or has purchased. Mapp Fashion combines that item with complementary garments, shoes, and accessories, guided by styling logic that reflects how items are worn together. Styling rules keep combinations sensible, so that colors, product types, and styles work together instead of clashing. Only items that are in stock are included, so every recommended outfit can be purchased as shown.
Outfits are grouped by occasion, so a shopper can see looks for different contexts, such as casual, evening, or business.
An outfit can also be built around an accessory. When a shopper views a bag or a pair of shoes, Mapp Fashion can suggest complete looks that feature that item.
Where outfits come from
As an alternative to automatically generated outfits, Shop the Look and Model Wears let you provide finished outfit combinations yourself, which Mapp Fashion then displays. This gives you full control over specific looks, such as campaign or editorial styling.
How recommendations are personalized
Recommendations adapt to each shopper. Outfits are ranked by how well they match the shopper’s style, based on their browsing and purchase behavior. The more a shopper interacts, the more relevant the recommendations become.
For how the available data affects recommendation quality, see Privacy & Cookies.
Prioritizing Specific Pairings with Fashion Sets
Products that share a Set ID form a Fashion Set (sometimes called a co-ord), such as a matching two-piece or a curated capsule pairing. Assign a shared Set ID to the relevant products in your product data (see Product Attribute Model). This overrides personalization: instead of being ranked individually by the shopper's style profile (see How recommendations are personalized above), the two products are always shown together as a pairing.
This also applies to outfits generated for Post-Purchase Emails.
Note
Set ID is available only for Product Catalogs running in Fashion-enabled mode.
Integration
Setup by Mapp
Mapp works with each brand to define its styling logic. Mapp stylists study the brand to understand its identity, so that outfits reflect how the brand wants to be represented. This shapes how product types are paired (for example, which shoes suit a dress) and which styling strategies apply for different occasions.
Integration Options
Developers can choose between two main approaches:
Widget Integration
A ready-to-use widget can be embedded on product detail pages for fast setup and minimal coding. It supports customization of layout, styling, and placement for fast deployment with minimal setup. For more information, see Mapp Fashion-hosted Widgets.
API Integration
Use the Mapp Fashion API to fetch personalized recommendations and render them in a custom UI.
For a complete integration guide, please visit our developer documentation.