Elasticsearch mapping文档相似性算法-阿里云开发者社区

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Elasticsearch mapping文档相似性算法

简介:

Elasticsearch allows you to configure a scoring algorithm or similarity per field. The similaritysetting provides a simple way of choosing a similarity algorithm other than the default TF/IDF, such as BM25.

Similarities are mostly useful for text fields, but can also apply to other field types.

Custom similarities can be configured by tuning the parameters of the built-in similarities. For more details about this expert options, see the similarity module.

The only similarities which can be used out of the box, without any further configuration are:

BM25
The Okapi BM25 algorithm. The algorithm used by default in Elasticsearch and Lucene. See Pluggable Similarity Algorithms for more information.
classic
The TF/IDF algorithm which used to be the default in Elasticsearch and Lucene. See Lucene’s Practical Scoring Function for more information.

The similarity can be set on the field level when a field is first created, as follows:

PUT my_index
{
  "mappings": {
    "my_type": {
      "properties": {
        "default_field": { 
          "type": "text"
        },
        "classic_field": {
          "type": "text",
          "similarity": "classic" 
        }
      }
    }
  }
}

The default_field uses the BM25 similarity.

The classic_field uses the classic similarity (ie TF/IDF).

 

参考:https://www.elastic.co/guide/en/elasticsearch/reference/current/similarity.html















本文转自张昺华-sky博客园博客,原文链接:http://www.cnblogs.com/bonelee/p/6472719.html,如需转载请自行联系原作者


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