Build your recommendation scheme from the Content tab of Recommendations Studio by adding one or more recommendation blocks.
Preview tip
Recommended items in each block depend on the selected preview user. Select a user who represents your target audience for accurate results.
Recommendation blocks appear on the right side of the Content tab. Select a block to view and configure its settings. You can drag and drop blocks to reorder them, and clone or delete blocks from the block menu.
Each block has five configuration sections that you work through top to bottom: Inputs → Item Exclusions → Item Filters → Additional Restrictions → Output
Add a recommendation block
Each recommendation block provides product recommendations based on a recipe. Select from the Blueshift recipe from the library as the base for the block, then refine the settings to match your requirements.
Click NEW BLOCK.
Select a recipe.
Click SELECT RECIPE. The new block appears on the right side of the Content tab.
Select the block from the right-side panel to open its settings, then configure its Inputs, Item Exclusions, Item Filters, Additional Restrictions, and Output settings.
Click SAVE to save your changes.
Choose a recipe for your feed
If you imported recommendations using CSV or a data warehouse, select a recipe that matches your feed type:
User → Product
Import recommendations for each user using a supported user identifier and one or more product IDs from the catalog. Useful for personalized promotions.
☆ Featured · User feeds · Online learning
Bring your existing user to item recommendations as feed imports
☆ Featured · User feeds · Online learning
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 profiles.
User feeds.
Inputs
User feed ranking.
Site-wide backfill (optional).
How it works
Personalized list of items per user as indicated in external feed uploaded by customer.
Feed type
User-Product
User imported feeds · Cross-industry
Bring your existing user to item recommendations as feed imports
User imported feeds · Cross-industry
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
Product → Product
Import recommendations that suggest related products based on user interactions. For example, users who view or buy a product may also be interested in similar items.
Live content - next best items to seed items · Cross-industry
Live content on site or in app when users are actively in session
Live content - next best items to seed items · Cross-industry
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
Live content - Next Best Items to seed items · Online learning
Live content - Next Best Items to seed items (courses, degrees)…
Live content - Next Best Items to seed items · Online learning
Live content - Next Best Items to seed items (courses, degrees)…
Use cases
Live content on site or in app when users are actively in session to show similar courses/degrees.
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
☆ Featured · Live content - Next Best Items to seed items + category level trending items · Automotive
Next best items based on seed items in live context payload…
☆ Featured · Live content - Next Best Items to seed items + category level trending items · Automotive
Next best items based on seed items in live context payload…
Use cases
Live content on site or in app when users are actively in session to suggest next best items to purchase.
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.
Next best product ranking.
Category backfill level.
Upto 3 category rankings.
How it works
Based on seed items in recommendation context, recommend next best products and category top items. Category will be collected based on seed item and next best item. Category level can be defined as leaf, parent or root. If leaf - leaf level trending items will be added, if leaf + parent, then leaf + parent level trending items will be added. If leaf + parent + root, then the 3 level trending items will be added. Upto 3 category rankings can be selected.
Feed type
Product-Product
Next best items · Cross-industry
Post activity follow up alerts
Next best items · Cross-industry
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
Next best items · Location-based businesses
Post activity follow up alerts
Next best items · Location-based businesses
Post activity follow up alerts
Use cases
Post activity follow up alerts.
Abandoned search/browse/list follow ups.
Homepage recommendations.
Engagement recommendations.
Requirements
View or equivalent events with product_ids.
Item catalogs with matching product_ids.
Inputs
Recent activity timeline.
Number of items to be considered from recent activity.
Product similarity rankings based on other users with similary activity.
City-State level backfill. Select None, if not required. For recommended items count < return count.
Zipcode level backfill. Select None, if not required. For recommended items count < return count.
Site-wide backfill. Select None, if not required. For recommended items count < return count.
How it works
Top items to consider after recent activity based on users with similar history.
Feed type
Product-Product
☆ Featured · Next Best Items · Marketplaces
Post activity follow up alerts
☆ Featured · Next Best Items · Marketplaces
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
Next best items based on recent purchases · Retail
Post purchase follow up alerts
Next best items based on recent purchases · Retail
Post purchase follow up alerts
Use cases
Post purchase follow up alerts.
Shipping/delivery confirmation alerts.
Requirements
Purchase or equivalent events with product_ids.
Item catalogs with matching product_ids.
Inputs
Recent purchase timeline.
Precomputed purchase to purchase rankings based on other users with similar activity.
Category backfill.
Site backfill.
How it works
Top items to consider after last purchase based on users with similar purchase history.
Feed type
Product-Product
Publishing - similar content · Media
Daily/Weekly newsletter to users based on recent activity
Publishing - similar content · Media
Daily/Weekly newsletter to users based on recent activity
Use cases
Daily/Weekly newsletter to users based on recent activity.
Suggested content to view based on past activity in app/email.
Requirements
Users recent activity timeline.
Item catalogs matching the products engaged with tags and title.
Inputs
User recent activity events and timeline.
Similar tag and similar title rankings to use.
Number of items from recent activity to consider for similarity (default 1).
Number of similar items to be recommended for each recent content engaged with (default: 2).
Site level backfill ranking (if recommended products less than return count). Set 'None' if not required.
How it works
Content from user's recent activity used to recommend other content with similar title and tags.
Feed type
Product-Product
Publishing - similar items to previous block · Media
Daily/Weekly newsletter to users based on another block
Publishing - similar items to previous block · Media
Daily/Weekly newsletter to users based on another block
Use cases
Daily/Weekly newsletter to users based on another block.
Suggested content to view based on another block in app/email.
Requirements
Users recent activity timeline.
Item catalogs matching the products engaged with tags and title.
Inputs
Name of previous block with items..
User recent activity events and timeline.
Similar tag and similar title rankings to use.
Number of items from previous block to consider for similarity (default 1).
Number of similar items to be recommended for each prior block product (default: 2).
Site level backfill ranking (if recommended products less than return count). Set 'None' if not required.
How it works
Similar content in catalog to previous block based on title & tags.
Feed type
Product-Product
Related items to last search · Retail
Remind users where they left off in their last search
Related items to last search · Retail
Remind users where they left off in their last search
Use cases
Remind users where they left off in their last search.
Event triggered campaigns on abandoned search.
Requirements
Event search with product_ids.
Item catalog matching those product_ids.
Inputs
Product to product ranking.
Category ranking backfill.
Site-wide backfill.
How it works
Items related to last search based on other users searching for similar items.
Feed type
Product-Product
Similar items to previous block · Cross-industry
Use products in previous blocks to recommend similar items
Similar items to previous block · Cross-industry
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
Similar items to previous block · Retail
Use products in previous blocks to recommend similar items
Similar items to previous block · Retail
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.
Category backfill for given category level. Select None, if not required.
Site backfill. Select None, if not required.
How it works
Items similar to previous block with backfills based on catalog attributes or other users with similar activity.
Feed type
Product-Product
Similar items to previous block with custom filters per selection · Location-based businesses
Use products in previous blocks to recommend similar items
Similar items to previous block with custom filters per selection · Location-based businesses
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.
Max radius within which properties to be recommended.
If insufficient properties, should zip and msa level backfill be considered?
Price variance percentage for filtering. Enter as number (e.g. 15 for 15%).
Should bathrooms also be matched? Currently matching for full bathrooms only. Not half baths.
How it works
Items related to previous block with backfills. Similarity is based on user session and transaction activity with other users. Various filters to be applied can be selected.
Feed type
Product-Product
Similar items to recent activity · Cross-industry
Personalized items related to recent user interaction
Similar items to recent activity · Cross-industry
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
Streaming - next episodes · Media
Queue Next Episodes on apps/sites
Streaming - next episodes · Media
Queue Next Episodes on apps/sites
Use cases
Queue Next Episodes on apps/sites.
Email/Push/SMS follow ups to keep binging.
Requirements
User event timeline activity.
Item catalogs with matching product_ids.
sku (episodes) and parent_sku (shows) 3. season_number and episode_number attributes.
Inputs
Recent activity timeline.
Number of items to consider from selected timeline.
Precomputed next best episodes rankings.
Number of episodes per episode.
How it works
Top episodes to watch next based on recently finished episodes.
Feed type
Product-Product
Streaming - similar items to previous block · Media
Daily/Weekly newsletter to users based on another block
Streaming - similar items to previous block · Media
Daily/Weekly newsletter to users based on another block
Use cases
Daily/Weekly newsletter to users based on another block.
Suggested content to view based on another block in app/email.
Requirements
Users recent activity timeline.
Item catalogs matching the products engaged (episodes).
Episode and Show catalogs linked through parent sku 3. Tags and title in the catalogs at episode and show level.
Inputs
Name of previous block with show items..
User recent activity events and timeline.
Similar tag and similar title rankings to use.
Number of shows from previous block to consider for similarity (default 1).
Number of similar shows to be recommended for each prior block product (default: 2).
Site level backfill ranking (if recommended products less than return count). Set 'None' if not required.
How it works
Similar shows in catalog to previous block (must be shows) based on title & tags.
Feed type
Product-Product
☆ Featured · Streaming - similar shows to recent activity · Media
Daily/Weekly newsletter to users based on recent activity
☆ Featured · Streaming - similar shows to recent activity · Media
Daily/Weekly newsletter to users based on recent activity
Use cases
Daily/Weekly newsletter to users based on recent activity.
Suggested content to view based on past activity in app/email.
Requirements
Users recent activity timeline.
Item catalogs matching the products engaged (episodes).
Episode and Show catalogs linked through parent sku 3. Tags and title in the catalogs at episode and show level.
Inputs
User recent activity events and timeline.
Similar tag and similar title rankings to use.
Number of shows from recent activity to consider for similarity (default 1).
Number of similar shows to be recommended for each recent show engaged with (default: 2).
Site level backfill ranking (if recommended products less than return count). Set 'None' if not required.
How it works
Shows with similar title and tags to recently watched.
Feed type
Product-Product
Configure the recommendation block
After you add a block and select a recipe, configure how recommendations are selected, filtered, and returned using the following sections:
Section | What it covers | Read more |
Inputs | Define what data drives the recommendations, such as events, attributes, or item lists. | |
Exclusions and filters | Exclude items based on past activity or messaging history, and filter results using catalog attributes. | |
Output and ranking | Control ranking, item count, parent-item output, and what happens when no recommendations are returned. |
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