Recommendations+

Recommendations+ is an upgrade to the Personalized for you (history-based) template. It helps improve click-through rates and user engagement. With this feature, you attract more visitors, get them to explore more products, and increase actions like adding items to the cart.

For a deeper understanding of our recommendations, explore the Recommendations section. You'll find detailed guidance to use and optimize this feature to enhance your user engagement.

Understand the models

Journey-based model

• This model predicts the next best product by considering the order of user interactions, offering more accurate recommendations.
• It uses two separate networks: one focuses on users and the other on items. By combining insights from both networks, it makes personalized recommendations.
• Imagine this model as two sets of eyes—one on users and one on items—working together to suggest what you might like.
• Recommendations+ runs on this model and doesn't require specifying events. It uses view items directly from your project's data mapping by default. When cart update and purchase_item events are available, the system also uses these events to improve the algorithm.

History-based model

• It breaks down user and item data into smaller parts to predict user preferences.
• The model functioning is similar to taking a puzzle apart and using the pieces to figure out what's missing.
• The standard Personalized recommendations for you template runs on this model.

Fallback behavior

Recommendations+ uses up to 15 of each customer's most recent view_item, add_to_cart, and purchase_item events, regardless of how old they are. There's no minimum number of events, so customers with at least one tracked event receive personalized results.

If the model returns fewer items than requested, the engine fills the remaining slots in this order:

1. The most viewed items from approximately the last 3 months, filtered by the recommendation's catalog filter. This list is refreshed daily.
2. Random catalog items.

Customers without tracked events receive the most viewed items.

Benefits of Recommendations plus

• Personalized experiences: Journey-based model delivers personalized recommendations by analyzing each user's behavior in real time.
• Sequential recommender: This feature predicts the next best product by considering the order of user interactions, offering more accurate recommendations.
• Higher click-through rates (CTR): Advanced AI techniques boost product CTR by aligning recommendations with the current user's journey.
• Real-time updates: The system adapts instantly to user behavior, ensuring relevant recommendations for all visitors.
• Scalable with data: The Journey-based model improves with more data, refining recommendations to better match user preferences.

Setup guide

1. Go to Campaigns > Recommendations and click + New Recommendation.
2. Select Personalized recommendations for you from the template list.

3. In the Personalization type step, select Journey-based, which is preselected by default.
   1. If you choose the History-based option, refer to the standard Personalized for you (History-based) guide for detailed setup instructions.
4. Fill in View items in data mapping to avoid the warning below.

Use cases

• Reducing site abandonment: You can place personalized recommendations inside weblayers and trigger them before a visitor leaves the site. This will help reduce abandonment rates and increase the time users spend on the site.
• Increasing product exploration: Convert homepage traffic into product detailed page (PDP) views by using personalized recommendations. This encourages users to explore more products and increases engagement.
• Maximizing interaction on product detail pages: Provide multiple recommendation types on PDPs, such as "personalized" and "similar items," to maximize the chance of user interaction and increase the likelihood of conversions.
• Driving traffic through email newsletters: Include personalized recommendations in regular email newsletters to drive traffic back to the website, increasing the potential for conversions.
• Enhancing cart and site abandonment communications: Include personalized recommendations in cart or site abandonment emails and mobile messages. This helps re-engage users and encourages them to complete their purchases.
• Improving product discovery: Deliver personalized recommendations after a product discovery quiz conducted over mobile messaging. This enhances the user experience and guides users toward products they are likely to be interested in.

Access Recommendations plus

Recommendations+ is available through these AI tools and agents packages, which must be enabled in account administration:

• Loomi Web (2025 packaging: Loomi Web Personalization)
  • Engage users directly on your website and through browser notifications.
  • Channels: Weblayers, Experiments, Browser push
• Loomi Journeys (2025 packaging: Loomi Journey Orchestration)
  • Reach your customers through personalized messages and notifications.
  • Channels: Email, Mobile push, SMS, RCS, WhatsApp, App Inbox, In-app personalization
• Enterprise Marketing AI (2025 packaging: Enterprise Marketing AI)
  • Includes all Loomi Web and Loomi Journeys features.

Recommendations+ isn't included in the base Loomi Platform or the Enterprise Marketing app package.

Limitations

• Real-time inference may have lower throughput than the history-based model, especially for large campaigns.
• Recommendations+ is not available in the advanced engine.
• The engine can process up to 1 million active products. Exceeding this may cause training failures or slow response times.
• Heavy dynamic filters may reduce the number of personalized items, and the engine fills the remaining slots with fallback items.