Recipes for multi-seller platforms, focused on seller-based recommendations and time-sensitive listings. Featured recipes (☆) are listed first. Expand a recipe to see its use cases, requirements and how it works.
Recipes for other industries: Cross-industry · Retail · Media · Automotive · Online Learning · Location-Based Businesses
New users
For users who have recently joined or started engaging.
Top wishlisted items based on input category and gender
Top wishlisted items based on input category and gender with size…
Top wishlisted items based on input category and gender
Top wishlisted items based on input category and gender with size…
Use cases
Use provided input category to provided relevant recommendations by gender.
Requirements
User gender.
Catalog with category and size (where applicable).
Event activity to indicate popularity.
Inputs
Input category.
Gender category ranking to use.
Category ranking to use if gender unavailable.
Should size filter be applied?
If yes, provide activity that should be used to determine size.
How it works
Based on user gender and input category, show top wishlisted items.
Active users
For users who engage regularly.
☆ Featured · Next Best Items
Post activity follow up alerts
☆ Featured · Next Best Items
Post activity follow up alerts
Use cases
Post activity follow up alerts.
Abandoned search/browse/cart follow ups.
Requirements
View or equivalent events with product_ids.
Item catalogs with matching product_ids and size in tags ( as applicable).
Inputs
Recent activity timeline.
Precomputed rankings based on other users with similar activity => Next best product, Cross-sell products.
Category backfill.
Site backfill.
How it works
Top items to consider after recent activity based on users with similar history and custom filters.
Feed type
Product-Product
☆ Featured · Store name affinity items
Popular, Trending or Recent Items by user affinity store names
☆ Featured · Store name affinity items
Popular, Trending or Recent Items by user affinity store names
Use cases
Daily/Weekly newsletter with top/trending items in user affinity store names.
App/Site live content with top/trending items in user affinity store names.
Requirements
Item catalogs with store name.
Items activity/event timeline.
Inputs
Number of items to recommend per affinity store name (Default 2).
Store Name Ranking.
Site-wide Backfill (if recommended products less than return count). Set 'None' if not required.
How it works
Popular, Trending or Recent Items by user affinity store names.
Daily sponsored feed
User Personalized Feed like Items For You / Top Picks for You
Daily sponsored feed
User Personalized Feed like Items For You / Top Picks for You
Use cases
User Personalized Feed like Items For You / Top Picks for You.
Daily/Weekly Newsletters.
Homepage recommendations.
Requirements
Category in product catalog.
User event timeline activity.
sponsored attribute on catalog.
Tags on catalog.
Inputs
User Recent Activity.
Consider items from category.
Use tag similarity to recommend products based on recent activity.
Provide next best product ranking to use for recent activity 2. Site-wide backfill (optional).
How it works
Personalized list of items generated from user recent activity that are from combination of items from tag similarity, next best product and category trending products that are sponsored.
Live content - top wishlisted items by gender category and size filter
Top wishlisted items in live content payload and affinity categories…
Live content - top wishlisted items by gender category and size filter
Top wishlisted items in live content payload and affinity categories…
Use cases
Use on webpage or app to show relevant recommendations to user based on current browse, past affinity and gender filtered by size (as applicable).
Requirements
User gender (on user profile or in live content call payload).
Catalog with category and size (where applicable)).
Event activity to indicate user affinity, top wishlisted items etc.
Inputs
Should user affinity categories be used along with payload categories?
Number of items per category.
Gender category and category rankings to use.
Should size filter be applied?
If yes, provide activity to determine size filter.
How it works
Based on user gender, user affinity categories and seed categories provided as part of live content payload, show top wishlisted items including optional filtering on size Live content payload sample - There must be atleast 1 seed_item_id or seed_category to drive the recommendation.
{ "page_number": 1, "seed_item_ids": '592189667' , "seed_categories": "Accessoarer" , "exclude_item_ids": '592189667' , "exclude_categories": , "gender": "male" }Similar items to previous block (product + seller_id) with size filter
Use products in previous blocks to recommend similar items
Similar items to previous block (product + seller_id) with size filter
Use products in previous blocks to recommend similar items
Use cases
Use products in previous blocks to recommend similar items.
Multi block personalization based on recent activity.
Requirements
Events with product_ids.
Item catalogs with matching product_ids and seller_ids and size (where applicable).
Inputs
Previous block name.
Number of items to consider from recent activity. Default value is 1.
Seller backfill for given seller_id level. Select None, is not required.
Site backfill. Select None, is not required.
Apply size filter?
If yes, provide activity to determine size filter.
How it works
Items related to previous block with backfills and optional size filtering. Similarity is based on user session and transaction activity with other users. This is backfilled with other products based on the seller_id of previous block products.
Intermittently active users
For users who engage now and then.
☆ Featured · New/trending from your favorite sellers
Re-engage users based on past activity
☆ Featured · New/trending from your favorite sellers
Re-engage users based on past activity
Use cases
Re-engage users based on past activity.
Expand item discovery based on users activity.
Daily/Weekly customised feed to users.
Requirements
Item catalogs with seller.
User recent activity timeline.
Inputs
Recent event timeline.
Seller top items ranking to use for each seller.
Site level backfill ranking (if recommended products less than return count). Set 'None' if not required.
Apply size filter?
If yes, provide recent activity to use for determining size filter.
How it works
Based on recent activity sellers, recommend top ranking products from the sellers with size filter.
Churned users
For users who have stopped engaging.
☆ Featured · Ending soon items
Alert users to expiring items and promote related discounts
☆ Featured · Ending soon items
Alert users to expiring items and promote related discounts
Use cases
Alert users to expiring items and promote related discounts.
Daily or Weekly Newsletters.
Backfill for other recommendations.
Requirements
Catalog of items.
end_date attribute in ISO8601 format.
Inputs
Site wide pre-ranked list of items sorted by end_date timestamp.
How it works
Items in catalog with end_date less than next few days indicating expiry or removal.
Affinity Category tags Items
Items matching category and tags that user has affinity towards
Affinity Category tags Items
Items matching category and tags that user has affinity towards
Use cases
Daily/Weekly newsletter with top/trending items in user affinity categories tags.
App/Site live content with top/trending items in user affinity categories tags.
Requirements
Item catalogs with category and tags.
User activity timeline with events.
Inputs
Number of items to recommend per affinity category and tag (Default 2).
Items ranking to fetch candidate at category and tags.
Site level backfill ranking (if recommended products less than return count). Set 'None' if not required.
How it works
Popular, Trending, Recent or Deal Items in user affinity categories and tags.
Items matching User Seller Attributes
Popular items corresponding to user selected sellers
Items matching User Seller Attributes
Popular items corresponding to user selected sellers
Use cases
Popular / Trending items personalization based on specific user attribute.
Mix and match with pre-computed user rankings.
Daily / Weekly Newsletters.
User specific promotions / sponsored items.
Requirements
User attribute value matching seller values from product catalog.
Inputs
User Attribute Name.
Seller Ranking.
Site-wide backfill (optional).
How it works
Popular items from user favorite sellers joined with item catalogs. The list of sellers must match what is available in catalogs and are case sensitive.
All users
For users at any lifecycle stage.
Live content - top wishlisted items by seed sellers
Based on seed_seller_ids provided, return top x products by seller.…
Live content - top wishlisted items by seed sellers
Based on seed_seller_ids provided, return top x products by seller.…
Use cases
Use on webpage or app to show relevant recommendations to user based on current browse, past affinity => Various selections of live content payload and affinities.
Requirements
Catalog with category, tags, seller_id.
Event activity to indicate user affinity, top wishlisted items etc.
Inputs
Live content payload with seed_seller_ids.
Supplement with user favorite sellers?
Seller ranking.
Apply size filter?
If yes, provide activity to use to determine size filter.
How it works
Based on seed_seller_ids provided, return top x products by seller. If affinity seller option selected, supplement with user favorite sellers.
Live content - top wishlisted items by seed sellers & gender
Based on seed_seller_ids and gender provided, return top x products…
Live content - top wishlisted items by seed sellers & gender
Based on seed_seller_ids and gender provided, return top x products…
Use cases
Use on webpage or app to show relevant recommendations to user based on current browse, past affinity => Various selections of live content payload and affinities.
Requirements
Catalog with category, tags, seller_id.
Event activity to indicate user affinity, top wishlisted items etc.
Inputs
Live content payload with seed_seller_ids.
Supplement with user favorite sellers?
Seller ranking.
Apply size filter?
If yes, provide activity to use to determine size filter.
How it works
Based on seed_seller_ids and gender provided, return top x products by seller. If affinity seller option selected, supplement with user favorite sellers.
Live content - site items ending soon / top x
This can be used for anonymous users or new users where sufficient…
Live content - site items ending soon / top x
This can be used for anonymous users or new users where sufficient…
Use cases
Use on webpage or app to show ending soon items or top wishlisted items for anonymous or new users.
Requirements
Catalog with end date.
Event activity to indicate top items.
Inputs
Optional live content payload with page number, exclude_item_ids and exclude_categories.
Choose - ending soon or top wishlisted => The recipe uses a combination of rankings to create an effective candidate set.
Apply size filter?
User activity to use to determine size filter.
How it works
This can be used for anonymous users or new users where sufficient Depending on choice - top wishlisted or ending soon, top items at site level are selected and filtered per choice. Sensitive content is filtered out.
User Coupons matching brand in event / live content payload
User coupons based on brand provided in event or in live content…
User Coupons matching brand in event / live content payload
User coupons based on brand provided in event or in live content…
Use cases
Website/App personalized offers based on user search on specific brands.
Requirements
Live content call/ event call with brand(s) in payload.
Item catalogs matching the seed brand(s).
Inputs
Brand attribute name in live content payload.
Brand ranking.
How it works
User Coupons matching brand in event / live content payload.
Connect with Blueshift for personalized recommendations
Whether you're exploring new ways to use recommendation recipes or looking to design a custom model for your business, our team can guide you through the next steps.
Reach out to [email protected] or your Customer Success Manager (CSM) for assistance.
What's next
Learn how Recommendations Studio works and understand the workflow for creating personalized recommendations.
How Recommendations Studio works →