128 lines
5.1 KiB
Plaintext
128 lines
5.1 KiB
Plaintext
[[query-dsl-mlt-query]]
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=== More Like This Query
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More like this query find documents that are "like" provided text by
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running it against one or more fields.
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[source,js]
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--------------------------------------------------
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{
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"more_like_this" : {
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"fields" : ["name.first", "name.last"],
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"like_text" : "text like this one",
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"min_term_freq" : 1,
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"max_query_terms" : 12
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}
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}
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--------------------------------------------------
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Additionally, More Like This can find documents that are "like" a set of
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chosen documents. The syntax to specify one or more documents is similar to
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the <<docs-multi-get,Multi GET API>>, and supports the `ids` or `docs` array.
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If only one document is specified, the query behaves the same as the
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<<search-more-like-this,More Like This API>>.
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[source,js]
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--------------------------------------------------
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{
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"more_like_this" : {
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"fields" : ["name.first", "name.last"],
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"docs" : [
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{
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"_index" : "test",
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"_type" : "type",
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"_id" : "1"
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},
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{
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"_index" : "test",
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"_type" : "type",
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"_id" : "2"
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}
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],
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"ids" : ["3", "4"],
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"min_term_freq" : 1,
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"max_query_terms" : 12
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}
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}
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--------------------------------------------------
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`more_like_this` can be shortened to `mlt`.
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Under the hood, `more_like_this` simply creates multiple `should` clauses in a `bool` query of
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interesting terms extracted from some provided text. The interesting terms are
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selected with respect to their tf-idf scores. These are controlled by
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`min_term_freq`, `min_doc_freq`, and `max_doc_freq`. The number of interesting
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terms is controlled by `max_query_terms`. While the minimum number of clauses
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that must be satisfied is controlled by `percent_terms_to_match`. The terms
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are extracted from `like_text` which is analyzed by the analyzer associated
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with the field, unless specified by `analyzer`. There are other parameters,
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such as `min_word_length`, `max_word_length` or `stop_words`, to control what
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terms should be considered as interesting. In order to give more weight to
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more interesting terms, each boolean clause associated with a term could be
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boosted by the term tf-idf score times some boosting factor `boost_terms`.
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When a search for multiple `docs` is issued, More Like This generates a
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`more_like_this` query per document field in `fields`. These `fields` are
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specified as a top level parameter or within each `doc`.
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IMPORTANT: The fields must be indexed and of type `string`. Additionally, when
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using `ids` or `docs`, the fields must be either `stored`, store `term_vector`
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or `_source` must be enabled.
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The `more_like_this` top level parameters include:
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[cols="<,<",options="header",]
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|=======================================================================
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|Parameter |Description
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|`fields` |A list of the fields to run the more like this query against.
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Defaults to the `_all` field for `like_text` and to all possible fields
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for `ids` or `docs`.
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|`like_text` |The text to find documents like it, *required* if `ids` or `docs` are
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not specified.
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|`ids` or `docs` |A list of documents following the same syntax as the
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<<docs-multi-get,Multi GET API>>. The text is fetched from `fields`
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unless specified otherwise in each `doc`.
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|`include` |When using `ids` or `docs`, specifies whether the documents should be
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included from the search. Defaults to `false`.
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|`percent_terms_to_match` |From the generated query, the percentage of terms
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that must match (float value between 0 and 1). Defaults to `0.3` (30 percent).
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|`min_term_freq` |The frequency below which terms will be ignored in the
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source doc. The default frequency is `2`.
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|`max_query_terms` |The maximum number of query terms that will be
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included in any generated query. Defaults to `25`.
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|`stop_words` |An array of stop words. Any word in this set is
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considered "uninteresting" and ignored. Even if your Analyzer allows
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stopwords, you might want to tell the MoreLikeThis code to ignore them,
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as for the purposes of document similarity it seems reasonable to assume
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that "a stop word is never interesting".
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|`min_doc_freq` |The frequency at which words will be ignored which do
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not occur in at least this many docs. Defaults to `5`.
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|`max_doc_freq` |The maximum frequency in which words may still appear.
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Words that appear in more than this many docs will be ignored. Defaults
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to unbounded.
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|`min_word_length` |The minimum word length below which words will be
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ignored. Defaults to `0`.(Old name "min_word_len" is deprecated)
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|`max_word_length` |The maximum word length above which words will be
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ignored. Defaults to unbounded (`0`). (Old name "max_word_len" is deprecated)
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|`boost_terms` |Sets the boost factor to use when boosting terms.
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Defaults to deactivated (`0`). Any other value activates boosting with given
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boost factor.
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|`boost` |Sets the boost value of the query. Defaults to `1.0`.
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|`analyzer` |The analyzer that will be used to analyze the `like text`.
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Defaults to the analyzer associated with the first field in `fields`.
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|=======================================================================
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