一尘不染

Elasticsearch中的加权随机抽样

elasticsearch

我需要从ElasticSearch指数获得了随机抽样,即发出查询检索从加权概率定索引一些文档Wj/ΣWi(这里Wj是行的权重j,并Wj/ΣWi在此查询所有文件的权重的总和)。

当前,我有以下查询:

GET products/_search?pretty=true

{"size":5,
  "query": {
    "function_score": {
      "query": {
        "bool":{
          "must": {
            "term":
              {"category_id": "5df3ab90-6e93-0133-7197-04383561729e"}
          }
        }
      },
      "functions":
        [{"random_score":{}}]
    }
  },
  "sort": [{"_score":{"order":"desc"}}]
}

它从选定类别中随机返回5个项目。每个项目都有一个字段weight。所以,我可能必须使用

"script_score": {
  "script": "weight = data['weight'].value / SUM; if (_score.doubleValue() > weight) {return 1;} else {return 0;}"
}

作为描述在这里

我有以下问题:

  • 正确的方法是什么?
  • 我需要启用动态脚本吗?
  • 如何计算查询的总和?

非常感谢你的帮助!


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2020-06-22

共1个答案

一尘不染

万一它对任何人都有帮助,这就是我最近实施加权改组的方式。

在此示例中,我们对公司进行了洗牌。每个公司都有一个介于0到100之间的“
company_score”。通过这种简单的加权改组,得分为100的公司出现在首页的可能性是得分为20的公司的5倍。

json_body = {
    "sort": ["_score"],
    "query": {
        "function_score": {
            "query": main_query,  # put your main query here
            "functions": [
                {
                    "random_score": {},
                },
                {
                    "field_value_factor": {
                        "field": "company_score",
                        "modifier": "none",
                        "missing": 0,
                    }
                }
            ],
            # How to combine the result of the two functions 'random_score' and 'field_value_factor'.
            # This way, on average the combined _score of a company having score 100 will be 5 times as much
            # as the combined _score of a company having score 20, and thus will be 5 times more likely
            # to appear on first page.
            "score_mode": "multiply",
            # How to combine the result of function_score with the original _score from the query.
            # We overwrite it as our combined _score (random x company_score) is all we need.
            "boost_mode": "replace",
        }
    }
}
2020-06-22