Confirm Category-Scoped Faceted Search per Category Page

[William Davis]

William Davis Yes this should be possible. Under Catalog > Facet Rules you can select the facet and change the Global Handling to "Hidden and Not Applied". Then if you navigate to the specific category you can select the Facet Rules tab and update the "Handling" setting for the facet to display as needed.

global_facet.pngctgy_facet.png

Thanks!

[Nick]

William Davis Yes this should be possible. Under Catalog > Facet Rules you can select the facet and change the Global Handling to "Hidden and Not Applied". Then if you navigate to the specific category you can select the Facet Rules tab and update the "Handling" setting for the facet to display as needed.

global_facet.pngctgy_facet.png

[William Davis]

Objective:
I want to confirm whether Miva supports category-scoped faceted search options, where each category page can display its own relevant facets, even when similar concepts exist across multiple categories.

Context:
My catalog spans multiple categories where similar concepts have different meanings:

• Coins → mint year
• Books → publication year
• Magazines → issue year

From a user perspective, it’s reasonable for each category page to show a “Year” facet, but the underlying meaning and data source would be category-specific.

Questions:

1. Can each category page in Miva display its own category-specific faceted options, based on fields relevant only to that category?
2. Are facet rules intended to be scoped per category so that:
   1. Coins can facet on coin-specific year fields, and
   2. Books can facet on book-specific publication year fields,
3. without those facets appearing or interfering across categories?
4. Is it acceptable (or common practice) for different underlying fields to share the same display label (e.g., “Year”) on different category pages?
5. Are there known pitfalls with faceting when multiple categories use different fields for conceptually similar attributes?

I’m trying to confirm platform capability first, before locking in a long-term data model.