So first we are getting a list of termVectors, which contain all tokens, then we create a map<token, frequency in the document>.
then the method createQueue will determine a score by deleting, stopWords and word which occurs not enough, compute idf, then idf * doc_frequency of a given token which is equals to its token, then we keeping the 25 best one, but after that how does it work? How is it compare to the whole index? I read http://cephas.net/blog/2008/03/30/how-morelikethis-works-in-lucene/ but that didn't explain it, or I miss the point.
how does More_like_this elasticsearch work (into the whole index)
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It creates a
TermQuery
out of each of those terms, and chucks them all into a simpleBooleanQuery
, boosting each term by the previously calculated tfidf score (boostFactor * myScore / bestScore
, where boostFactor can be set by the user).Here is the source (version 5.0):