Recipes for streaming, publishing and content discovery, focused on engagement and personalized feeds. 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 · Marketplaces · Automotive · Online Learning · Location-Based Businesses
New users
For users who have recently joined or started engaging.
Streaming - popular shows
Site-wide top watched shows
Streaming - popular shows
Site-wide top watched shows
Use cases
Recommendations for new users or users with insufficient activity based on account top activity seen.
App / Newsletter feed to recommend popular content across site.
Requirements
Episode and Show Item catalogs linked via parent_sku.
Episode and show items activity/event timeline.
Inputs
Site/Account top shows ranking.
How it works
Top watched shows across all users on your sites/apps.
Author popular, trending or recent items
Popular or trending or recent items by specific author
Author popular, trending or recent items
Popular or trending or recent items by specific author
Use cases
Show case your most popular or trending offerings to your new customers from specific authors.
Alert all users to recent additions to your catalogs in specific authors.
Alert all users to seasonal or sponsored promotions in specific authors.
Win back your inactive customers with exclusive but popular items in specific authors.
Backfill other recommendations.
Requirements
Events with product_ids in payload.
Item Catalogs matching those product_ids.
Author attribute.
Inputs
Author Name.
One of the precomputed rankings across all authors activity or catalogs.
How it works
Popular, Trending, Recent items based on all user activity on your sites and apps by specific author.
Active users
For users who engage regularly.
☆ Featured · Streaming - continue watching
Content users started watching but haven't finished
☆ Featured · Streaming - continue watching
Content users started watching but haven't finished
Use cases
Remind users to continue watching video or audio or text content where they left off.
Combine with similar items product recommendations to let them discover related content.
Requirements
Media start event or equivalent with product_ids.
Media finish event or equivalent with product_ids.
Item catalogs with those product_ids.
Inputs
Timeline on media_start events.
Timeline on media_finish events.
How it works
Replay items watched on sites or apps but have not yet been completed and remind them to continue watching.
Daily feed
User Personalized Feed like Items For You / Top Picks for You
Daily 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
Tags in product catalog at sku and parent_sku level.
User event timeline activity.
Inputs
User Recent Activity.
Consider items from tags similarity. Default value is Yes 2. Site-wide backfill (optional).
How it works
Personalized list of items generated from combination of items based on user recent activity and tags similarity items.
Streaming - similar items to previous block
Daily/Weekly newsletter to users based on another block
Streaming - similar items to previous block
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
Publishing - similar items to previous block
Daily/Weekly newsletter to users based on another block
Publishing - similar items to previous block
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
Publishing - similar content
Daily/Weekly newsletter to users based on recent activity
Publishing - similar content
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
Streaming - next episodes
Queue Next Episodes on apps/sites
Streaming - next episodes
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
Intermittently active users
For users who engage now and then.
☆ Featured · Streaming - similar shows to recent activity
Daily/Weekly newsletter to users based on recent activity
☆ Featured · Streaming - similar shows to recent activity
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
My list reminder
My List reminders
My list reminder
My List reminders
Use cases
My List reminders.
Similar Items to My List Items.
Requirements
Events with product_ids in payload.
Inputs
Include events with product_ids which suggests added to list action.
Exclude events with products_ids which suggests removed from list/ already viewed action.
How it works
To remind users about the items added to My List and yet not viewed.
Churned users
For users who have stopped engaging.
☆ Featured · Publishing - new items from authors you like
New items from authors based on prior purchases and wishlists
☆ Featured · Publishing - new items from authors you like
New items from authors based on prior purchases and wishlists
Use cases
Alert users to new arrivals / launches from authors based on prior activity or wishlist.
Backfill other recommendations.
Requirements
Events with product_ids in payload.
Item Catalogs matching those product_ids.
Author attribute on items.
Inputs
Recent activity timeline.
Favorite Author Attribute on User profile 2. One of the precomputed rankings for each author.
How it works
New items from authors you purchased from or liked.
☆ Featured · Tag affinity items
Popular or recent items from users affinity tags
☆ Featured · Tag affinity items
Popular or recent items from users affinity tags
Use cases
Personalization related to tags inclination for popular / trending items.
Daily / Weekly newsletter feed to recommend popular / trending content by tags.
Requirements
Tags at sku and/or parent_sku level.
Users activity/event timeline.
Inputs
Number of items per tag affinity.
Popular Tags ranking to fetch candidate items.
Site level backfill (optional).
How it works
Popular or recent items related to the tags users have affinity towards.
Publishing - new items from favorite authors
Popular items matching user attribute joined on author attribute
Publishing - new items from favorite authors
Popular items matching user attribute joined on author 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 author values from product catalog.
Inputs
User Attribute Name for Authors.
Author Ranking.
Site-wide backfill (optional).
How it works
Popular items from user favorite authors joined with item catalogs. The list of authors must match what is available in catalogs and are case sensitive.
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 →