Available algorithms
Maestra supports three Similar Products algorithms. Pick the one that fits where and how you want to surface recommendations.Similar Products
Recommends items similar to a single product. Best for product detail pages.
Similar Products to Product List
Recommends items similar to a list of products a customer has saved or interacted with. Best for wishlists and “back in stock” flows.
Similar Products to Recently Viewed
Recommends items similar to products a customer viewed in their last session. Best for abandoned-browse campaigns.
Similar Products
Recommends items similar to a specific product.| Property | Value |
|---|---|
| Algorithm type | Recommendations to product |
| Audience | Identified and anonymous customers |
| Invocation | API (recommendation widget), email |
| Recalculation frequency | Once daily |
| Limit | Up to 5 algorithms per project |
| Best for | Product detail pages |
- Product availability in the customer’s zone.
- External product system compatibility (optional).
Similar Products to Product List
Recommends items similar to a list of products associated with a customer (for example, a wishlist or a “notify me when back in stock” list).| Property | Value |
|---|---|
| Algorithm type | Personal recommendations |
| Audience | Identified customers only |
| Invocation | API (recommendation widget), email |
| Recalculation frequency | Real time, accounting for products the customer adds |
| Limit | Up to 3 algorithms per project |
| Best for | ”Back in stock” mechanics, wishlist features |
- Product availability.
- Brand matching (for multi-brand projects).
- Exclusion of items the customer has already purchased.
Similar Products to Recently Viewed
Recommends items similar to products the customer viewed in their last session.| Property | Value |
|---|---|
| Algorithm type | Personal recommendations |
| Audience | Identified customers |
| Invocation | API, email |
| Recalculation frequency | Real time |
| Limit | Up to 2 algorithms per project |
| Best for | Abandoned product view mechanics |
For multi-brand projects, the algorithm generates recommendations within each brand separately.
How to create the algorithm
Follow these steps to set up any of the three Similar Products algorithms.Choose the algorithm variant
Select one of the three Similar Products algorithms based on the use case described above.
Configure general settings
Set up the optional parameters that scope which products the algorithm applies to and which products it can recommend.
- Recommend for products — the target segment of products the algorithm applies to.
- Recommend from — the source segment the algorithm draws recommendations from.
- Filter by price — restricts recommendations to a price range.
- Brand and Product list — used for the Similar Products to Product List algorithm.
- Recommend only from the same external system — enabled by default.
Configure similarity settings
Choose the product fields that define what “similar” means. The system first filters candidates by Exact Match parameters and then sorts the remaining products by similarity.See the Similarity logic section below for the full ranking rules.
- Category selection — choose the primary category that is closest to the product.
- Manufacturer exclusion — available in its own section if you want to exclude specific manufacturers from recommendations.
Similarity logic
The similarity settings control both filtering and ranking. Maestra first removes candidates that fail the Exact Match rules, then sorts what remains using the similarity rules.Exact Match filtering
Candidates must satisfy all of the following for the fields you selected:- Price / old price — only items within ±30% of the current product’s price are kept.
- Manufacturer and other single-value fields — values must match exactly.
- Multi-value fields and categories — the candidate’s value set must overlap with or be contained in the source product’s value set.
Similarity sorting
After filtering, remaining candidates are ranked using these rules:- Price — products within ±33% of the source price rank higher.
- Single-value fields — products with matching values rank higher; empty values are preferred over non-matching filled values.
- Multi-value fields — products are ranked by how many values they share with the source product.
Recommended use cases
Product detail pages
Use Similar Products to surface alternatives directly on a product page.
Back in stock and wishlists
Use Similar Products to Product List to suggest alternatives based on what the customer has saved.
Abandoned browse
Use Similar Products to Recently Viewed to win back customers who left without converting.