One of the nice features of BM25 is that, unlike TF/IDF, it has two parameters that allow it to be tuned:

The practicalities of tuning BM25 are another matter. The default values for k1 and b should be suitable for most document collections, but the optimal values really depend on the collection. Finding good values for your collection is a matter of adjusting, checking, and adjusting again.

The similarity algorithm can be set on a per-field basis. It’s just a matter of specifying the chosen algorithm in the field’s mapping:

PUT /my_index
{
  "mappings": {
    "doc": {
      "properties": {
        "title": {
          "type":       "string",
          "similarity": "BM25" 

        },
        "body": {
          "type":       "string",
          "similarity": "default" 

        }
      }
  }
}

Currently, it is not possible to change the similarity mapping for an existing field. You would need to reindex your data in order to do that.

Configuring BM25

Configuring a similarity is much like configuring an analyzer. Custom similarities can be specified when creating an index. For instance:

PUT /my_index
{
  "settings": {
    "similarity": {
      "my_bm25": { 

        "type": "BM25",
        "b":    0 

      }
    }
  },
  "mappings": {
    "doc": {
      "properties": {
        "title": {
          "type":       "string",
          "similarity": "my_bm25" 

        },
        "body": {
          "type":       "string",
          "similarity": "BM25" 

        }
      }
    }
  }
}


参考：https://www.elastic.co/guide/en/elasticsearch/guide/current/changing-similarities.html

本文转自张昺华-sky博客园博客，原文链接：http://www.cnblogs.com/bonelee/p/6472828.html，如需转载请自行联系原作者