Mlt QueryΒΆ

More like this query find documents that are “like” provided text by running it against one or more fields.

    "more_like_this" : {
        "fields" : ["name.first", "name.last"],
        "like_text" : "text like this one",
        "min_term_freq" : 1,
        "max_query_terms" : 12
more_like_this can be shortened to mlt.

The more_like_this top level parameters include:

Parameter Description
fields A list of the fields to run the more like this query against. Defaults to the _all field.
like_text The text to find documents like it, required.
percent_terms_to_match The percentage of terms to match on (float value). Defaults to 0.3 (30 percent).
min_term_freq The frequency below which terms will be ignored in the source doc. The default frequency is 2.
max_query_terms The maximum number of query terms that will be included in any generated query. Defaults to 25.
stop_words An array of stop words. Any word in this set is considered “uninteresting” and ignored. Even if your Analyzer allows stopwords, you might want to tell the MoreLikeThis code to ignore them, as for the purposes of document similarity it seems reasonable to assume that “a stop word is never interesting”.
min_doc_freq The frequency at which words will be ignored which do not occur in at least this many docs. Defaults to 5.
max_doc_freq The maximum frequency in which words may still appear. Words that appear in more than this many docs will be ignored. Defaults to unbounded.
min_word_len The minimum word length below which words will be ignored. Defaults to 0.
max_word_len The maximum word length above which words will be ignored. Defaults to unbounded (0).
boost_terms Sets the boost factor to use when boosting terms. Defaults to 1.
boost Sets the boost value of the query. Defaults to 1.0.
analyzer The analyzer that will be used to analyze the text. Defaults to the analyzer associated with the field.