OpenSearch/docs/reference/query-dsl/queries/mlt-query.asciidoc

[[query-dsl-mlt-query]]
=== More Like This Query

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

[source,js]
--------------------------------------------------
{
    "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`.

Under the hood, `more_like_this` simply creates multiple `should` clauses in a `bool` query of
interesting terms extracted from some provided text. The interesting terms are
selected with respect to their tf-idf scores. These are controlled by
`min_term_freq`, `min_doc_freq`, and `max_doc_freq`. The number of interesting
terms is controlled by `max_query_terms`. While the minimum number of clauses
that must be satisfied is controlled by `percent_terms_to_match`. The terms
are extracted from `like_text` which is analyzed by the analyzer associated
with the field, unless specified by `analyzer`. There are other parameters,
such as `min_word_length`, `max_word_length` or `stop_words`, to control what
terms should be considered as interesting. In order to give more weight to
more interesting terms, each boolean clause associated with a term could be
boosted by the term tf-idf score times some boosting factor `boost_terms`.

The `more_like_this` top level parameters include:

[cols="<,<",options="header",]
|=======================================================================
|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_length` |The minimum word length below which words will be
ignored. Defaults to `0`.(Old name "min_word_len" is deprecated)

|`max_word_length` |The maximum word length above which words will be
ignored. Defaults to unbounded (`0`). (Old name "max_word_len" is deprecated)

|`boost_terms` |Sets the boost factor to use when boosting terms.
Defaults to deactivated (`0`). Any other value activates boosting with given
boost factor.

|`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.
|=======================================================================