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Relevancy Ranking
dtSearch can sort and instantly re-sort search results by:
- number of hits, file name, file date
- metadata like subject or email recipient or sender
Natural language algorithms provide automatic term weighting, following a "plain English" or unstructured indexed search request.
- Term weighting sorts by relevancy using the frequency and density of hits in your files.
- For example, in the search request Sam memo 1999 CorpX takeover, if 1999 appeared in 3,000 files, and Sam appeared in only two files, then Sam would get a much higher relevancy rating, taking you straight to the most "relevant" files.
A positional scoring option works with dtSearch's natural language relevancy ranking to rank documents more highly when hits are near the top of a file, or otherwise clustered in a file.
Variable term weighting can also apply to natural language and other queries.
- Positive term weighting can place extra emphasis on one or more words: soup:8 or recipe:3
- Negative term weighting can assign negative emphasis to one or more words: red or green or yellow:-7
- Variable term weighting can also apply to metadata: (description:5 contains (apple and pear)) or (author:2 contains smith)
Developers, for more API-driven sorting and relevancy-ranking options, see Faceted Search and Data Classification