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

[[query-dsl-mlt-query]]
=== More like this query
++++
<titleabbrev>More like this</titleabbrev>
++++

The More Like This Query finds documents that are "like" a given
set of documents. In order to do so, MLT selects a set of representative terms
of these input documents, forms a query using these terms, executes the query
and returns the results. The user controls the input documents, how the terms
should be selected and how the query is formed.

The simplest use case consists of asking for documents that are similar to a
provided piece of text. Here, we are asking for all movies that have some text
similar to "Once upon a time" in their "title" and in their "description"
fields, limiting the number of selected terms to 12.

[source,console]
--------------------------------------------------
GET /_search
{
  "query": {
    "more_like_this" : {
      "fields" : ["title", "description"],
      "like" : "Once upon a time",
      "min_term_freq" : 1,
      "max_query_terms" : 12
    }
  }
}
--------------------------------------------------

A more complicated use case consists of mixing texts with documents already
existing in the index. In this case, the syntax to specify a document is
similar to the one used in the <<docs-multi-get,Multi GET API>>.

[source,console]
--------------------------------------------------
GET /_search
{
  "query": {
    "more_like_this": {
      "fields": [ "title", "description" ],
      "like": [
        {
          "_index": "imdb",
          "_id": "1"
        },
        {
          "_index": "imdb",
          "_id": "2"
        },
        "and potentially some more text here as well"
      ],
      "min_term_freq": 1,
      "max_query_terms": 12
    }
  }
}
--------------------------------------------------

Finally, users can mix some texts, a chosen set of documents but also provide
documents not necessarily present in the index. To provide documents not
present in the index, the syntax is similar to <<docs-termvectors-artificial-doc,artificial documents>>.

[source,console]
--------------------------------------------------
GET /_search
{
  "query": {
    "more_like_this": {
      "fields": [ "name.first", "name.last" ],
      "like": [
        {
          "_index": "marvel",
          "doc": {
            "name": {
              "first": "Ben",
              "last": "Grimm"
            },
            "_doc": "You got no idea what I'd... what I'd give to be invisible."
          }
        },
        {
          "_index": "marvel",
          "_id": "2"
        }
      ],
      "min_term_freq": 1,
      "max_query_terms": 12
    }
  }
}
--------------------------------------------------

==== How it Works

Suppose we wanted to find all documents similar to a given input document.
Obviously, the input document itself should be its best match for that type of
query. And the reason would be mostly, according to
link:https://lucene.apache.org/core/4_9_0/core/org/apache/lucene/search/similarities/TFIDFSimilarity.html[Lucene scoring formula],
due to the terms with the highest tf-idf. Therefore, the terms of the input
document that have the highest tf-idf are good representatives of that
document, and could be used within a disjunctive query (or `OR`) to retrieve similar
documents. The MLT query simply extracts the text from the input document,
analyzes it, usually using the same analyzer at the field, then selects the
top K terms with highest tf-idf to form a disjunctive query of these terms.

IMPORTANT: The fields on which to perform MLT must be indexed and of type
`text` or `keyword``. Additionally, when using `like` with documents, either
`_source` must be enabled or the fields must be `stored` or store
`term_vector`. In order to speed up analysis, it could help to store term
vectors at index time.

For example, if we wish to perform MLT on the "title" and "tags.raw" fields,
we can explicitly store their `term_vector` at index time. We can still
perform MLT on the "description" and "tags" fields, as `_source` is enabled by
default, but there will be no speed up on analysis for these fields.

[source,console]
--------------------------------------------------
PUT /imdb
{
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "term_vector": "yes"
      },
      "description": {
        "type": "text"
      },
      "tags": {
        "type": "text",
        "fields": {
          "raw": {
            "type": "text",
            "analyzer": "keyword",
            "term_vector": "yes"
          }
        }
      }
    }
  }
}
--------------------------------------------------

==== Parameters

The only required parameter is `like`, all other parameters have sensible
defaults. There are three types of parameters: one to specify the document
input, the other one for term selection and for query formation.

[discrete]
==== Document Input Parameters

[horizontal]
`like`::
The only *required* parameter of the MLT query is `like` and follows a
versatile syntax, in which the user can specify free form text and/or a single
or multiple documents (see examples above). The syntax to specify documents is
similar to the one used by the <<docs-multi-get,Multi GET API>>. When
specifying documents, the text is fetched from `fields` unless overridden in
each document request. The text is analyzed by the analyzer at the field, but
could also be overridden. The syntax to override the analyzer at the field
follows a similar syntax to the `per_field_analyzer` parameter of the
<<docs-termvectors-per-field-analyzer,Term Vectors API>>.
Additionally, to provide documents not necessarily present in the index,
<<docs-termvectors-artificial-doc,artificial documents>> are also supported.

`unlike`::
The `unlike` parameter is used in conjunction with `like` in order not to
select terms found in a chosen set of documents. In other words, we could ask
for documents `like: "Apple"`, but `unlike: "cake crumble tree"`. The syntax
is the same as `like`.

`fields`::
A list of fields to fetch and analyze the text from.

[discrete]
[[mlt-query-term-selection]]
==== Term Selection Parameters

[horizontal]
`max_query_terms`::
The maximum number of query terms that will be selected. Increasing this value
gives greater accuracy at the expense of query execution speed. Defaults to
`25`.

`min_term_freq`::
The minimum term frequency below which the terms will be ignored from the
input document. Defaults to `2`.

`min_doc_freq`::
The minimum document frequency below which the terms will be ignored from the
input document. Defaults to `5`.

`max_doc_freq`::
The maximum document frequency above which the terms will be ignored from the
input document. This could be useful in order to ignore highly frequent words
such as stop words. Defaults to unbounded (`Integer.MAX_VALUE`, which is `2^31-1`
or 2147483647).

`min_word_length`::
The minimum word length below which the terms will be ignored. The old name
`min_word_len` is deprecated. Defaults to `0`.

`max_word_length`::
The maximum word length above which the terms will be ignored. The old name
`max_word_len` is deprecated. Defaults to unbounded (`0`).

`stop_words`::
An array of stop words. Any word in this set is considered "uninteresting" and
ignored. If the analyzer allows for stop words, you might want to tell MLT to
explicitly ignore them, as for the purposes of document similarity it seems
reasonable to assume that "a stop word is never interesting".

`analyzer`::
The analyzer that is used to analyze the free form text. Defaults to the
analyzer associated with the first field in `fields`.

[discrete]
==== Query Formation Parameters

[horizontal]
`minimum_should_match`::
After the disjunctive query has been formed, this parameter controls the
number of terms that must match.
The syntax is the same as the <<query-dsl-minimum-should-match,minimum should match>>.
(Defaults to `"30%"`).

`fail_on_unsupported_field`::
Controls whether the query should fail (throw an exception) if any of the 
specified fields are not of the supported types
(`text` or `keyword`). Set this to `false` to ignore the field and continue
processing. Defaults to `true`.

`boost_terms`::
Each term in the formed query could be further boosted by their tf-idf score.
This sets the boost factor to use when using this feature. Defaults to
deactivated (`0`). Any other positive value activates terms boosting with the
given boost factor.

`include`::
Specifies whether the input documents should also be included in the search
results returned. Defaults to `false`.

`boost`::
Sets the boost value of the whole query. Defaults to `1.0`.

==== Alternative
To take more control over the construction of a query for similar documents it is worth considering writing custom client code to assemble selected terms from an example document into a Boolean query with the desired settings. The logic in `more_like_this` that selects "interesting" words from a piece of text is also accessible via the <<docs-termvectors,TermVectors API>>. For example, using the termvectors API it would be possible to present users with a selection of topical keywords found in a document's text, allowing them to select words of interest to drill down on, rather than using the more "black-box" approach of matching used by `more_like_this`.