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Setting Filters On Recommendations

Filter the pool of products or content that recommendation algorithms draw from — by category, price range, stock status, brand, or any custom attribute — for sharper, more relevant recommendations.

Updated 1 month ago 3 min read
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by Admin

Recommendation filters constrain the candidate pool that an algorithm draws from. Without filters, Personyze considers your entire catalog; with filters, it only considers items that match your criteria. The result is sharper, more contextually relevant recommendations — and protection against awkward outputs (out-of-stock items, off-brand products, items outside the visitor’s price range).

Where filters live

In the algorithm step of any recommendation wizard, scroll past the algorithm choice to the Filters area. You’ll find common filters as toggle switches and a button to add custom filters using any product or content attribute.

Common filter examples

In StockAlways-on filter for production. Out-of-stock recommendations create immediate frustration.
Price RangeFilter to ±20% of the currently-viewed product, or to a specific price band per audience segment (premium vs. value).
Same CategoryFor “more like this” widgets — only show items in the same product category as what the visitor is currently viewing.
Different CategoryFor cross-sell — exclude items from the same category as the trigger product. Forces complementary recommendations.
Brand / ManufacturerMatch the brand of the viewed product, or restrict to specific brands per campaign.
Custom AttributesAny field in your product feed — color, size, material, gender, season — can become a filter.

How to build a filter

  1. Click Add Filter.
  2. In the first dropdown, choose the product attribute (e.g., brand, category, price).
  3. In the second dropdown, choose the filter value — either a specific value (e.g., “iPhone”) or a dynamic match (e.g., “same as currently viewed product”).
  4. Repeat for additional filters. Multiple filters combine with AND logic — items must match all of them.

For OR logic, you’d typically use a single filter with multiple values selected (e.g., brand IN [Apple, Samsung, Google]) rather than multiple filter rows.

Dynamic filters (context-aware)

The most powerful filters compare attributes between the recommendation candidate and the current page context. Examples:

  • “Same brand as viewed product” — recommendation pool is filtered to the brand of whatever item the user is currently looking at.
  • “Price within ±25% of viewed product” — keeps recommendations in a comparable price band.
  • “Same category as last 3 viewed” — narrows to recent browsing intent.

These dynamic filters update automatically as the visitor browses — no per-product campaign needed.

Content recommendation filters

Content recommendations use the same filter mechanics but with content-specific attributes (article category, author, publish date, content type, tags). The interface looks slightly different but the logic is the same — pick an attribute, pick a value, combine with AND.

Best practices

  • Start broad, narrow gradually. Heavy filtering can produce empty recommendations. Add filters one at a time and verify in the QA step that you still get good results.
  • Always include in-stock + active. These are the bare minimum for any production recommendation.
  • Pair filters with a fallback. If your filters are restrictive, set a fallback algorithm with looser filters so visitors never see an empty widget.
  • Test on real visitor data. Use the QA step with several real subscriber emails to see what filters produce in practice — especially for dynamic filters that depend on browsing context.