Similar Products Recommendation Preset
Surfaces items similar to a single product.
Similar Products to Product List Recommendation Preset
Surfaces items similar to each product on a customer’s list.
Products Similar to Recently Browsed Products Recommendation Preset
Surfaces items similar to what a customer viewed in their last session.
Similar Products Recommendation Preset
Surfaces similar products for a specific item. Higher-priced items get more recommendations; lower-priced items get fewer. You can prioritize product attributes (like price or manufacturer) and even require exact matches on specific attributes — for example, size.- Best used on product detail pages.
- Works for identified and anonymous customers.
- Available through the API (recommendation widget) and email.
- Refresh: once a day.
- Limit: up to 5 instances per project.
- Segment: can be limited to a product segment.
- Auto-filters: checks the product’s area; product external systems match by default.
Similar Products to Product List Recommendation Preset
This preset only generates recommendations for customers with activity in the last 90 days. Everyone else will see recommendations calculated earlier (if the preset is more than 90 days old) — or no recommendations at all.
- Best used for “Back in stock” and Favorites flows.
- Identified customers only.
- Available through the API (recommendation widget) and email.
- Refresh: real time, accounting for products the customer adds.
- Limit: up to 3 instances per project.
- Auto-filters: checks the product’s area and brand (multi-brand projects); excludes items the customer has already bought.
Products Similar to Recently Browsed Products Recommendation Preset
This preset only generates recommendations for customers with activity in the last 90 days. Everyone else will see recommendations calculated earlier (if the preset is more than 90 days old) — or no recommendations at all.
- Best used for “Product browse abandonment” flows.
- Identified customers only.
- Available through the API and email.
- Refresh: real time.
- Limit: up to 2 instances per project.
- In multi-brand projects, the preset generates recommendations within each brand separately.
How to set it up
1
Open Product recommendations
Go to Content → Product recommendations and click Add mechanic.
2
Choose the preset
Pick the Similar Products variation that matches your placement.
3
Name the preset
Enter a clear name so you can find it later, then continue.
4
Configure general settings
Set the parameters that scope which products the preset applies to and which products it can recommend from.
- Recommend for products — the target segment the preset applies to.
- Recommend from — the source segment recommendations are drawn from.
- Filter by price — restricts recommendations to a price range.
- Brand and Product list — used for the Similar Products to Product List Recommendation Preset.
- Recommend only from the same external system — enabled by default.
5
Configure similarity settings
Pick the product fields that define what “similar” means. Maestra Platform first filters candidates by Exact match parameters, then sorts the remaining products by similarity.See Similarity logic below for the ranking rules.
The more fields you select, the fewer recommendations you’ll get. Too many constraints can leave you with none at all.
- Primary category — the category closest to the product.
- Manufacturer exclusions — available in its own block if you want to exclude specific manufacturers.
6
Launch the preset
Activate it. The Product recommendations page shows its current status and the timestamp of the last update.
Similarity logic
The similarity settings drive both filtering and ranking. Maestra Platform 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 source 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.