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James Goodwin

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In this table you can see which fields apply to each content type in Fuse:

Content type

Content title

Content description

Content body

Content transcript

tags

Videos

Yes

Yes

N/A

Yes*

Yes

Articles

Yes

Yes

Yes

N/A

Yes

Questions

Yes

Yes

N/A

N/A

Yes

Links

Yes

Yes

N/A

N/A

Yes

Uploaded files

Yes

Yes

Yes**

Yes**

Yes

Topics

Yes

Yes

N/A

N/A

Yes

Learning Plans

Yes

Yes

N/A

N/A

N/A

Communities

Yes

Yes

N/A

N/A

N/A

Events

Yes

Yes

N/A

N/A

Yes

Event occurrences

Yes

Yes

N/A

N/A

N/A

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Legend:

  • N/A means that the content type does not have that particular field. For example, Body is not applicable for videos as they do not have a body, they have a transcript instead. 

  • ** = The body or transcript of some uploaded file types is searchable. For example, the body of uploaded Word, PDF, and PPTX files can be searched by Fuse. 

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  • Listening to customers' feedback

  • Reviewing specific issues where search terms are not providing optimal results

  • Reviewing bugs and issues raised through our Support portal

  • Analysing search queries and the position of results users click on (Engine scoring)

Before any changes are released, engine scoring is performed to ensure that those changes do not have any a negative impact on search relevancy. 

Relevancy of fields in an item of content

Search calculates a relevancy score for each item of content in the search results. Matches found in each field contribute to the item's score.  Some  Some fields have a greater impact on the score than others. Content fields: 

Title

Matches in the title field score most highly and if there is an exact match between the user's search query and the content title, that score is boosted.

 For example, if the user searched for

 

Description

Matches in the description field contribute to the score. The more terms that match, the greater the score from the description field. 

Body/Transcript

Matches in the content body or transcript also contribute to the score. If the user searched for ‘onboarding plan’ and the term ‘plan’ appears 10 times in document A and only twice in document B, then document A will score higher.

Tags

Matches against content tags also contribute toward the content score. Tags are only considered if they match the terms in the user’s search query exactly. For example, if the user searches for ‘onboarding plan’, and there is a single tag called ‘onboarding plan’, then it would contribute to the score. Two tags

‘Onboarding’ and ‘Plan’

‘onboarding’ and ‘plan’ would not contribute to the content’s score.