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More Like This Queryedit
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
}
}
Additionally, More Like This can find documents that are "like" a set of
chosen documents. The syntax to specify one or more documents is similar to
the Multi GET API, and supports the ids or docs array.
If only one document is specified, the query behaves the same as the
More Like This API.
{
"more_like_this" : {
"fields" : ["name.first", "name.last"],
"docs" : [
{
"_index" : "test",
"_type" : "type",
"_id" : "1"
},
{
"_index" : "test",
"_type" : "type",
"_id" : "2"
}
],
"ids" : ["3", "4"],
"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.
When a search for multiple docs is issued, More Like This generates a
more_like_this query per document field in fields. These fields are
specified as a top level parameter or within each doc.
The fields must be indexed and of type string. Additionally, when
using ids or docs, the fields must be either stored, store term_vector
or _source must be enabled.
The more_like_this top level parameters include:
| Parameter | Description |
|---|---|
|
A list of the fields to run the more like this query against.
Defaults to the |
|
The text to find documents like it, required if |
|
A list of documents following the same syntax as the
Multi GET API. The text is fetched from |
|
When using |
|
When using |
|
From the generated query, the percentage of terms
that must match (float value between 0 and 1). Defaults to |
|
The frequency below which terms will be ignored in the
source doc. The default frequency is |
|
The maximum number of query terms that will be
included in any generated query. Defaults to |
|
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". |
|
The frequency at which words will be ignored which do
not occur in at least this many docs. Defaults to |
|
The maximum frequency in which words may still appear. Words that appear in more than this many docs will be ignored. Defaults to unbounded. |
|
The minimum word length below which words will be
ignored. Defaults to |
|
The maximum word length above which words will be
ignored. Defaults to unbounded ( |
|
Sets the boost factor to use when boosting terms.
Defaults to deactivated ( |
|
Sets the boost value of the query. Defaults to |
|
The analyzer that will be used to analyze the |