Faceted Search
Faceted navigation available in DAM products (Widget, Hub and Portal)
Last updated
Faceted navigation available in DAM products (Widget, Hub and Portal)
Last updated
At its core, faceted navigation utilizes a faceted classification system to organize information. This system categorizes items based on multiple, independent attributes called facets. Each added facet acts as new filtering layer.
Concept: Faceted navigation falls under the umbrella of parametric search techniques. Instead of relying solely on keyword matching in a traditional search, it allows users to filter based on pre-defined parameters (facets) associated with the asset. This enables a more nuanced, smarter search experience.
Technology: Faceted navigation relies on efficient data structures and algorithms to handle large datasets and filter combinations. In essence, the system needs to quickly identify items that match all selected facet criteria. Search engines like Solr or Elastic Search are popular choices for implementing faceted navigation due to their ability to handle complex filtering operations.
Benefits: The technical aspects of faceted navigation translate into several benefits:
Drill-down refinement: Users can progressively narrow down results by applying multiple facet filters, leading to a precise subset of assets.
Faceted exploration: Faceted browsing allows users to explore different categories and discover new assets they might not have found through a simple keyword search.
Improved efficiency: By filtering irrelevant options, faceted navigation reduces the number of results a user needs to sift through, saving them time and effort.
Faceted navigation is applied for 4 types of attributes (as of May 8th, 2024)
Date (Uploaded and Created date)
Metadata Single-select
Metadata Multi-select
Metadata Boolean
→ Faceted navigation is only available in the "Assets" view (ie. not in the Folder view)
→ Faceted navigation section is built as a side bar at the left side of the UI, replacing entirely the filter bar (shown below, that will then be hidden) in the Assets view.
This type of navigation is activated at a {token} level, and is a global setting for all users, that can be found at the DAM-> Library -> Interface customization page.
Within a metadata, the operator between 2 values or more is set to OR
Between 2 metadata or more, the operator is set to AND
In details:
Within a metadata
-> For multi-select, we provide 2 operators: AND and OR
Let assume we are calling query HAS (equal to IS but for multi-select we call it HAS just for gramatical purpose)
We will have 2 options
Metadata HAS a AND b -> expected results are files which have both values a and b, file that contains only a or b won't be returned
Metadata HAS a OR b -> expected results are files which has either a or b, file that contains both of the values will be returned also
➡️ This option cover the 1st option -> within a multi-select metadata, we will choose HAS-OR operator to cover more file results
-> For single-select, it will always be OR when users click in more than one answers as one file can not contain more than one value.
For 2 metadata or more
For faceted search, it is an AND operator between multiple metadata as the answers will be more specific and this is the main idea for faceted navigation: trimming down the most correct answers
a. Multi-select metadata "colour" with value: “red”, “white”, “black”
When "red" and "white" are searched for -> expected answers are:
Files that contain value red only
Files that contain value white only
Files that contain both value red and white
b. Single-select metadata "size" with value: “35”, “36”, “37”
When “35” and “36” are searched for -> expected answers are:
files that contain 35 value only
files that contain 36 value only
c. Now, combining the two previous examples
When “red”, “white”, “35”, “36” are searched for -> expected answers have to use AND operator to encompass the 2 metadata constraints, so answers will be assets that contain:
red and 35,
red and 36,
white and 35,
white and 36,
red and white and 35,
red and white and 36
→ Refining searches further is leading to more accurate and relevant results.