These recipes work across industries and use common personalization patterns. Featured recipes (☆) are listed first. Expand a recipe to see its use cases, requirements and how it works.
Recipes for other industries: Retail · Media · Marketplaces · Automotive · Online Learning · Location-Based Businesses
New users
For users who have recently joined or started engaging.
Category popular, trending, recent items
Popular or trending or recent items by category
Category popular, trending, recent items
Popular or trending or recent items by category
Use cases
Show case your most popular or trending offerings to your new customers in specific categories.
Alert all users to recent additions to your catalogs in specific categories.
Alert all users to seasonal or sponsored promotions in specific categories.
Win back your inactive customers with exclusive but popular items in specific categories.
Backfill other recommendations.
Requirements
Events with product_ids in payload.
Item Catalogs matching those product_ids.
Category attribute optionally with levels.
Inputs
Category Name.
One of the precomputed rankings across all categories activity or catalogs.
How it works
Popular, Trending, Recent items based on all user activity on your sites and apps at each level of the category.
Tag popular, trending or recent items
Popular or trending or recent items by selected tag
Tag popular, trending or recent items
Popular or trending or recent items by selected tag
Use cases
Show case your most popular or trending offerings to your new customers by specific tag.
Alert all users to recent additions to your catalogs by specific tag.
Alert all users to seasonal or sponsored promotions by specific tag.
Win back your inactive customers with exclusive but popular items by specific tag.
Backfill other recommendations.
Requirements
Events with product_ids in payload.
Item Catalogs matching those product_ids.
Tags attribute consisting of array of tags per item.
Inputs
Tag Name.
One of the precomputed rankings across all tags.
How it works
Popular, Trending, Recent items based on all user activity on your sites and apps by each tag.
Brand popular, trending, recent items
Popular or trending or recent items by brand
Brand popular, trending, recent items
Popular or trending or recent items by brand
Use cases
Show case your most popular or trending offerings to your new customers by specific brand.
Alert all users to recent additions to your catalogs by specific brand.
Alert all users to seasonal or sponsored promotions by specific brand.
Win back your inactive customers with exclusive but popular items by specific brand.
Backfill other recommendations.
Requirements
Events with product_ids in payload.
Item Catalogs matching those product_ids.
Brand attribute in item catalogs.
Inputs
Brand Name.
One of the precomputed rankings for each brand.
How it works
Popular, Trending, Recent items based on all user activity on your sites and apps by each brand.
Active users
For users who engage regularly.
☆ Featured · Next best items/Similar items using LLM
Cross-catalog recommendations based on rich text similarity using LLMs
☆ Featured · Next best items/Similar items using LLM
Cross-catalog recommendations based on rich text similarity using LLMs
Use cases
Cross-catalog recommendations based on rich text similarity using LLMs.
Requirements
Catalogs with rich text data.
Inputs
User recent activity / previous block.
Next best item/Similar Item LLM rankings.
Backfill ranking based on category/site if insufficient results.
How it works
Recommend items from other categories based on text similarity using LLMs.
Next best items
Post activity follow up alerts
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.
Inputs
Recent activity timeline.
Precomputed rankings based on other users with similar activity.
Category backfill.
Site backfill.
How it works
Top items to consider after recent activity based on users with similar history.
Feed type
Product-Product
Similar items to recent activity
Personalized items related to recent user interaction
Similar items to recent activity
Personalized items related to recent user interaction
Use cases
Personalized items related to recent user interaction.
For scenarios lie: More Like This/ Because you liked.
Requirements
Events with product_ids.
Item catalogs with matching product_ids.
Inputs
User recent activity event(s) for given lookback window.
Number of items to consider from recent activity. Default value is 1.
Category backfill for given category level. Select None, is not required.
Site backfill. Select None, is not required.
How it works
Items similar to recent activity with backfills. Similarity is based on user session and transaction activity with other users who have similar product interactions.
Feed type
Product-Product
Similar items to previous block
Use products in previous blocks to recommend similar items
Similar items to previous block
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.
Inputs
Previous block name.
Number of items to consider from recent activity. Default value is 1.
Category backfill for given category level. Select None, is not required.
Site backfill. Select None, is not required.
How it works
Items related to previous block with backfills. Similarity is based on user session and transaction activity with other users.
Feed type
Product-Product
User recommended items
Use user-specific rankings to get customized recommendations
User recommended items
Use user-specific rankings to get customized recommendations
Use cases
Use user-specific rankings to get customized recommendations.
Mix and match with pre-computed user rankings.
Alert users across channels including In-App or Onsite.
Newsletters.
User specific promotions / sponsored items.
Requirements
User activity events.
Item Catalogs.
Inputs
User ranking.
Site-wide backfill (optional).
How it works
Personalized list of items for user generated based on similar users interaction data.
Items to Articles using LLM
Based on items browsed, direct users to blogs/articles related to it
Items to Articles using LLM
Based on items browsed, direct users to blogs/articles related to it
Use cases
Based on items browsed, direct users to blogs/articles related to it.
Requirements
Item catalog.
Article catalog.
Inputs
Recent activity to determine items engaged with.
LLM ranking to use.
How it works
Recommend articles based on items that user engaged with.
Items to Videos using LLM
Videos related to items engaged with using LLM
Items to Videos using LLM
Videos related to items engaged with using LLM
Use cases
Based on items browsed, direct users to how-to's/informational videos related to it.
Requirements
Item Catalog.
Videos catalog.
Inputs
Recent activity to determine items engaged with.
LLM ranking to use.
How it works
Recommend videos based on items that user engaged with.
Churned users
For users who have stopped engaging.
☆ Featured · Site-wide popular, trending or recent items
Popular, trending, recent items across all of your sites and apps
☆ Featured · Site-wide popular, trending or recent items
Popular, trending, recent items across all of your sites and apps
Use cases
Show case your most popular or trending offerings to your new customers.
Alert all users to recent additions to your catalogs.
Alert all users to seasonal or sponsored promotions.
Win back your inactive customers with exclusive but popular items.
Backfill other recommendations.
Requirements
Events with product_ids in payload.
Item Catalogs matching those product_ids.
Inputs
One of the precomputed rankings across site wide activity or catalogs.
How it works
Popular, Trending, Recent items based on all user activity on your sites and apps.
☆ Featured · Category affinity items
Daily or weekly newsletters
☆ Featured · Category affinity items
Daily or weekly newsletters
Use cases
Daily or weekly newsletters.
Backfill for other recommendations.
Requirements
Events with product_ids.
Catalog of items with those product_ids.
Category attribute for each item in the catalog.
Inputs
Pre-computed category level rankings.
Number of items to consider per category. Default value is 2.
Site wide backfill if user does not have sufficient affinities or items. Select None, if not required.
How it works
AI generated list of items personalized to each user based on categories they have affinity towards. The list of categories is updated daily based on user activity across all sites and apps.
All users
For users at any lifecycle stage.
☆ Featured · Recent activity
Wishlist reminders
☆ Featured · Recent activity
Wishlist reminders
Use cases
Wishlist reminders.
Re-order reminders.
Replay Purchase history.
Replay Cart history.
Replay Watch / Read / Stream history.
Requirements
Events saved on timeline with product_ids in payload.
Item Catalogs matching those product_ids.
How it works
Use items and their attributes from last 366 days of events to personalize templates and messaging across all channels.
Live content - popular items by category
Live content - popular items matching seed categories
Live content - popular items by category
Live content - popular items matching seed categories
Use cases
Website/App widget populated personalized for users based on categories engaged with.
Website/App results personalized based on user search on specific categories.
Requirements
Live content call with seed_categories in payload.
Item catalogs matching the seed categories.
Past account (all users) activity with items belonging to seed categories.
Inputs
Variable name providing seed categories in live api call payload (recommendation context).
Category ranking to fetch candidate items in seed categories.
Number of items to recommend per seed category (Default: 2).
Site level backfill ranking (if recommended products less than return count). Set 'None' if not required.
How it works
Live content - Popular items in seed categories sent in context as part of live api call payload excluding items user has already viewed/purchased (customizable).
Curated items
Editorial control over what items are sent to users
Curated items
Editorial control over what items are sent to users
Use cases
Editorial control over what items are sent to users.
Seasonal trends.
Sponsored promotions.
Newsletters with specific themes.
Backfill other recommendations.
Requirements
Item catalogs.
Inputs
List of items.
delimiter string/char like comma, semicolon, colon or pipe.
How it works
Curated or Editorial list of items from the catalog. Multiple items can be entered using a delimiter string of your choice, specifying the delimiter in the 'Input Delimiter' field.
Items matching user category attributes
Popular items matching user attribute joined on category attribute
Items matching user category attributes
Popular items matching user attribute joined on category attribute
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 category values from product catalog.
Inputs
User Attribute Name.
Category Ranking.
Site-wide backfill (optional).
How it works
Popular items from user favorite categories joined with item catalogs. The list of categories must match what is available in catalogs and are case sensitive.
Live content - next best items to seed items
Live content on site or in app when users are actively in session
Live content - next best items to seed items
Live content on site or in app when users are actively in session
Use cases
Live content on site or in app when users are actively in session.
Requirements
Live Content request payload with seed_items.
Item Catalogs with matching seed_items.
Inputs
Name of event payload attribute for seed_items.
Name of event payload attribute for exclude_items.
Name of event payload attribute for exclude_categories.
How it works
Based on seed items in recommendation context, recommend next best products and category top items.
Feed type
Product-Product
Predictive content
Bring your existing user to item recommendations as feed imports
Predictive content
Bring your existing user to item recommendations as feed imports
Use cases
Bring your existing user to item recommendations as feed imports.
Mix and match with pre-computed user rankings.
Alert users across channels including In-App or Onsite.
Newsletters.
User specific promotions / sponsored items.
Requirements
User Attributes or Activity.
Item Catalogs.
Inputs
User feed ranking.
Site-wide backfill (optional).
How it works
Personalized list of items per user pre-computed by the system.
Items matching user attribute
Personalized item_ids on user profile
Items matching user attribute
Personalized item_ids on user profile
Use cases
Personalized item_ids on user profile.
Mix and match with pre-computed user rankings.
Daily / Weekly Newsletters.
User specific promotions / sponsored items.
Requirements
User attribute value matching item_id values from product catalog.
Inputs
User Attribute Name.
Site-wide backfill (optional).
How it works
Items from user attributes joined with catalogs. The list of item_ids must match what is available in catalogs and are case sensitive.
User imported feeds
Bring your existing user to item recommendations as feed imports
User imported feeds
Bring your existing user to item recommendations as feed imports
Use cases
Bring your existing user to item recommendations as feed imports.
Mix and match with pre-computed user rankings.
Alert users across channels including In-App or Onsite.
Newsletters.
User specific promotions / sponsored items.
Requirements
User to Item Feeds.
Item Catalogs.
Inputs
User feed ranking.
Site-wide backfill (optional).
How it works
Personalized list of items imported via feeds.
Feed type
User-Product
Parent products of previous block
Recommend parent parts corresponding to any prior block
Parent products of previous block
Recommend parent parts corresponding to any prior block
Use cases
Recommend parent parts corresponding to any prior block.
Requirements
Prior block with items.
Parent sku for items in catalog.
Inputs
Previous block names.
How it works
Parent items of items in previous block.
Event triggered campaign - replay triggering event
Event triggered campaign - replay triggering event
Event triggered campaign - replay triggering event
Event triggered campaign - replay triggering event
Use cases
Use the exact products that triggered the campaign to drive recommendations with context of latest action.
Requirements
Events triggered campaign based on event that saves product to timeline.
Catalog corresponding to product_ids in the event.
Inputs
Event triggered campaign payload (No manual input).
How it works
Replay products from the event that triggered the campaign.
Child items of previous block
Child items for previous blocks
Child items of previous block
Child items for previous blocks
Use cases
Child items for previous blocks.
Requirements
Catalog of products with parent and child item ids.
Pre-computed child items ranking.
Inputs
Previous block name.
Number of items to consider from previous block. Default value is 4.
Child items ranking.
Number of child items per parent item. Default value is 1.
How it works
Child items of previous block parent items such that child item parent_sku == parent item sku.
Recent activity with site-wide backfill
Wishlist reminders
Recent activity with site-wide backfill
Wishlist reminders
Use cases
Wishlist reminders.
Re-order reminders.
Replay Purchase history.
Replay Cart history.
Replay Watch / Read / Stream history.
Requirements
Events saved on timeline with product_ids in payload.
Item Catalogs matching those product_ids.
How it works
Use items and their attributes from last 366 days of events to personalize templates and messaging across all channels. Optionally backfill with site-wide rankings if there is no activity.
Custom event with metadata
Replay custom event with product metadata
Custom event with metadata
Replay custom event with product metadata
Use cases
Import recommendations for each user via event payloads and include them in personalized campaigns.
Requirements
Custom events saved in replace mode and sent with product metadata.
Inputs
Name of custom event.
How it works
Replay custom events received per user with product metadata.
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 →