Relevance is a term that defines how search engines measure how closely related the content of a web page is aligned to match the context of a search query.
The gimmick of relevance is that search engines like Solr and Elasticsearch are just sophisticated text-matching systems. They can tell you when a search word matches a word in a document. Once a match has been found, the search engine can use the frequency of that word to give search results a relevance score.
Outside of this core “engine”, a lot of search relevancy is related to the development, which is necessary to correct the text to provide a fuzzy comparison or to correctly increase the correct factors. Relevance developers focus on the following areas as a “first line of defense”:
Text Analysis. The act of “normalizing” text from both a search query and a search result allows fuzzy matching.
Query Time Weights and Boosts. Reweighting the importance of various fields based on search requirements.
Phrase / Position Matching. Requiring or boosting on the appearance of the entire query or parts of a query as a phrase or based on the position of the words.
