Web modules: Recommendation Strategies

This article provides a comprehensive overview of all web modules currently available in the platform. Each module plays a specific role in powering personalized recommendations, merchandising strategies, and user experiences across your website. The documentation includes detailed descriptions, technical names, and key functionalities for each module. For your convenience, a Glossary is included at the end of this article to explain technical terms and internal jargon.

Item modules: Contextual
Technical name	Personalized Rerank Opportunities	Candidate set	Explicit Schema Rules	Back-filling	Merchandising Boost	Integration to Merchandising & CDP audience check	OData-filter	FullInfo
GetMerchandisingItemsWeb	✓	Merchandising Products	Popularity & Discount			✓		✓
GetPIMRelatedItemsForBasketWeb	✓	Products Bought Together	Target Group, Category, Brand, etc.	Trending (visit)	✓	✓	✓	✓
GetPIMRelatedItemsWeb	✓	Products Bought Together	Target Group, Category, Brand, etc.	Trending (visit)	✓	✓	✓	✓
GetPopularItemsInBrandWeb	✓	Top in Brand			✓	✓	✓	✓
GetPopularItemsInCategoryWeb	✓	Popular Items			✓	✓	✓	✓
GetSimilarItemsWeb	✓	Similar Content	Category and Brand	Top Viewed	✓	✓	✓	✓
GetNumOfUsersRightNowWeb		Get Number of Users
Item modules: Global
Technical name	Personalized Rerank Opportunities	Candidate set	Explicit Schema Rules	Back-filling	Merchandising Boost	Integration to Merchandising & CDP audience check	OData-filter	FullInfo
GetPopularItemsWeb	✓	Trending and Popular	Category and Brand		✓	✓	✓	✓
GetPopularBrandsWeb	✓	Popular and Trending						✓
GetPopularCategoriesWeb	✓	Popular and Trending						✓
Item modules: User
Technical name	Personalized Rerank Opportunities	Candidate set	Explicit Schema Rules	Back-filling	Merchandising Boost	Integration to Merchandising & CDP audience check	OData-filter	FullInfo
GetUserBrandHistoryWeb		User History						✓
GetUserCrossSellingItemsWeb		User History		Favorite > Popular, Trend	✓	✓	✓	✓
GetUserItemHistoryWeb		User History					✓	✓
GetUserItemRecommendationsWeb		User History		Favorite Category & Brand	✓	✓	✓	✓
GetUserLookAlikeItemsWeb		User History, Similar Items		Favorite > Popular, Trend	✓	✓	✓	✓

All 'item' modules listed above also have the following settings:

- Content- / Product- / Category- / Brand-filter (not "GetNumOfUsersRightNowWeb)

- Split testing (not "GetNumOfUsersRightNowWeb")

Content modules: Contextual
Technical name	Personalized Rerank Opportunities	Candidate set	Explicit Schema Rules	Back-filling	Merchandising Boost	Integration to Merchandising & CDP audience check	OData-filter
GetContentBasedOnItemWeb	✓	Product to Content			✓	✓	✓
GetContentBasedOnProductBrandWeb	✓	Brand to Content			✓	✓	✓
GetContentBasedOnProductCategoryWeb	✓	Product-Category to Content		Category to Content	✓	✓	✓
GetItemsBasedOnContentWeb	✓	Content to Products	Category and Brand	Top Viewed	✓	✓	✓
GetSimilarContentWeb	✓	Similar Content	Category	Top Viewed	✓	✓	✓
Content modules: Global
Technical name	Personalized Rerank Opportunities	Candidate set	Explicit Schema Rules	Back-filling	Merchandising Boost	Integration to Merchandising & CDP audience check	OData-filter
GetMerchandisingContentWeb	✓	Merchandising Products	Popularity			✓
GetPopularContentWeb*		Trending and Popular			✓	✓	✓
* Trending: Reflects the change in popularity over the selected time period — indicating whether a product has become more popular. Content modules: User
Technical name	Personalized Rerank Opportunities	Candidate set	Explicit Schema Rules	Back-filling	Merchandising Boost	Integration to Merchandising & CDP audience check	OData-filter
GetUserContentHistoryWeb		Recent History					✓
GetUserContentRecommendationsWeb		Similar Content		Top in Favorite Content	✓	✓	✓

All 'content' modules listed above also have the following settings:

- Content- / Product- / Category- / Brand-filter

- FullInfo

- Split testing

Glossary

This glossary provides definitions for technical terms used in this article.

Backfilling

Definition: Products to be added at the end of the prioritized list of products when personalized recommendations aren’t available.

Candidate set
Definition: Defines the logic of the module - how the product selection is made, which data sources and filters are applied, and which attributes influence the final candidate list shown to the user. In Merchandising, this list (typically 50 products) is dynamic and behavior-based, forming the limited set within which boosting actions can take place.

Content- / Product- / Category- / Brand-filter

Definition: Use this filter to remove items from the output based on a comma-separated list.

Explicit Schema Rules
Definition: Module specific filtering. The rules are set up via a customized schema.

Fullinfo

Definition: All mapped parameters from the item Schema are included in the API output. This enriches the recommendation data with additional item attributes.

Integration to Merchandising and CDP (Audience Check)

Definition: Connect the Recommendation Egine with the CDP.

Merchandising Boost

Definition: Boosting products with a given value from the product Schema via Merchandising Boosted Campaigns.

ODatafilter

Definition: Use this filter to remove groups of products from the output. Can be used on all mapped parameters from the product schema. Read more.

Personalized Rerank Opportunities
Definition: Reranking the products based on User & Cookie; buy, basket and visit + Items similar or related to the user interactions. Read more.

Split testing
Definition: Split testing (A/B testing) compares two setups (A and B) by assigning users based on their cookie id to see which performs better.