Recipes for educational platforms, focused on course, teacher and content recommendations. 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 · Marketplaces · Automotive · Location-Based Businesses
All users
For users at any lifecycle stage.
☆ Featured · Courses for you
Homepage recommendations
☆ Featured · Courses for you
Homepage recommendations
Use cases
Homepage recommendations.
Next Best Courses to take.
Requirements
User language attributes.
Catalog with relevant item ids.
Inputs
Use language filter. Tick checkbox, is teachers are to be filtered based on language.
User language attribute name. If selected, mention language attribute for user.
Catalog language attribute name. If selected, mention language attribute from catalog.
Courses Ranking.
How it works
Courses to consider based on user's language attributes at account level.
☆ Featured · Live content - Popular items matching seed_tags in payload
Live content on site or in app when users are actively in session
☆ Featured · Live content - Popular items matching seed_tags in payload
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_tags.
Item Catalogs with matching tags.
Inputs
Name of event payload attribute for seed_tags.
How it works
Based on seed tags in recommendation context, recommend other popular tag items.
☆ Featured · Teachers for you
Post activity follow up alerts
☆ Featured · Teachers for you
Post activity follow up alerts
Use cases
Post activity follow up alerts.
Incomplete search/browse/course follow ups.
Homepage recommendations.
Requirements
View or equivalent events with product_ids.
Item catalogs with matching product_ids.
Inputs
Recent activity timeline.
Use language filter. Tick checkbox, is teachers are to be filtered based on language.
User language attribute name. If selected, mention language attribute for user.
Catalog language attribute name. If selected, mention language attribute from catalog.
Precomputed rankings based on other users with similar activity.
Tags Similarity Ranking.
Site-wide backfill. For recommended teachers < return count. Select None, if not required.
How it works
Teachers to recommend based on other users with similar event activity and user's language attributes.
☆ Featured · User feeds
Bring your existing user to item recommendations as feed imports
☆ Featured · User 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 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
Articles for you
Homepage recommendations
Articles for you
Homepage recommendations
Use cases
Homepage recommendations.
Next Best Articles to read.
Requirements
View or equivalent events with product_ids.
Item catalogs with matching product_ids.
Inputs
User language attribute name.
Catalog language attribute name.
Category Level Ranking.
Backfill Category Name.
How it works
Articles to recommend based on other users with similar event activity and user's language attributes.
Group classes for you
Homepage recommendations
Group classes for you
Homepage recommendations
Use cases
Homepage recommendations.
Next Best Group Class to take.
Requirements
User language attributes.
Catalog with relevant item ids.
Inputs
Use language filter. Tick checkbox, is teachers are to be filtered based on language.
User language attribute name. If selected, mention language attribute for user.
Catalog language attribute name. If selected, mention language attribute from catalog.
Group Class Ranking.
How it works
Group Classes to consider based on user's language attributes at account level.
Live content - Next Best Items to seed items
Live content - Next Best Items to seed items (courses, degrees)…
Live content - Next Best Items to seed items
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
Category Affinity Feed
Recommended classes for Sunday newsletter campaigns
Category Affinity Feed
Recommended classes for Sunday newsletter campaigns
Use cases
Recommended classes for Sunday newsletter campaigns.
'You may also like' styled recommendations.
Requirements
User historical activity for category affinity.
Item catalogs with product_ids and attribute for required filters.
Inputs
Precomputed category to item rankings.
Other category rankings.
Number of items to consider per user's affinity category (default 10).
Custom filters.
How it works
Top recommended items based on user's category affinity with an optional custom filters (language, rating)and option to use other category rankings to supplement selected ranking.
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 →