Fuse’s search engine seeks to provide provides the user with results that closely match the query they have entered. The search engine It looks for words or phrases the user has searched for , in fields within the an item of content, such as the content’s title, description, body, transcript or and tags. In the example below, you can see that this the article has a title, body, description and tags. So if If the user searched searches for ‘works of charles dickens’, the search engine will look for these words within this in the title, body, description and tags tag fields in the article.
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Some fields are only applicable to certain content types. See the table below to see which fields are applicable to each content type. |
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Notes:
Fuse can only search through a transcript if it exists in the video.
Video transcripts are searchable regardless of whether they wereare automatically generated or manually generated.
Automated transcription only occurs if this feature is enabled in your organisation’s Fuse instance. If it is not enabled, contact your Customer Success Consultant (CSC).
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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. :
Title |
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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. | |
Description |
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Matches in the description field contribute to the score. The more terms that match, the greater the score from the description field. | |
Body/Transcript |
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Matches in the content body or transcript also contribute to the score. If the user searched for ‘ |
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onboarding plan’ and the term ‘plan’ appears 10 times in document A and only twice in document B, then document A will score higher. |
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Tags |
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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 |
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plan’, and there is a single tag called ‘onboarding |
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plan’, then it would contribute to the score. Two tags |
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‘onboarding’ and ‘plan’ would not contribute to the content’s score. |