白话Elasticsearch26-深度探秘搜索技术之function_score自定义相关度分数算法

2021-08-17 14:51:14 浏览数 (1)

概述

继续跟中华石杉老师学习ES,第26篇

课程地址: https://www.roncoo.com/view/55


官方说明

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-function-score-query.html

简单来说: 自定义一个function_score函数,自己将某个field的值,跟es内置算出来的分数进行运算,然后由自己指定的field来进行分数的增强


例子

需求: 看帖子的人越多,那么帖子的分数就越高

先给所有的帖子数据增加follower数量 , 将对帖子搜索得到的分数,跟follower_num进行运算,由follower_num在一定程度上增强帖子的分数 看帖子的人越多,那么帖子的分数就越高

代码语言:javascript复制
POST /forum/article/_bulk
{ "update": { "_id": "1"} }
{ "doc" : {"follower_num" : 5} }
{ "update": { "_id": "2"} }
{ "doc" : {"follower_num" : 10} }
{ "update": { "_id": "3"} }
{ "doc" : {"follower_num" : 25} }
{ "update": { "_id": "4"} }
{ "doc" : {"follower_num" : 3} }
{ "update": { "_id": "5"} }
{ "doc" : {"follower_num" : 60} }

DSL

代码语言:javascript复制
GET /forum/article/_search
{
  "query": {
    "function_score": {
      "query": {
        "multi_match": {
          "query": "java spark",
          "fields": ["tile", "content"]
        }
      },
      "field_value_factor": {
        "field": "follower_num",
        "modifier": "log1p",
        "factor": 0.5
      },
      "boost_mode": "sum",
      "max_boost": 5
    }
  }
}
  • 如果只有field,那么会将每个doc的分数都乘以follower_num,如果有的doc follower是0,那么分数就会变为0,效果很不好。
  • 因此一般会加个log1p函数,公式会变为,new_score = old_score * log(1 number_of_votes),这样出来的分数会比较合理 。
  • 再加个factor,可以进一步影响分数,new_score = old_score * log(1 factor * number_of_votes)
  • boost_mode,可以决定分数与指定字段的值如何计算 : multiply,replace, sum,min,max,avg
  • max_boost,限制计算出来的分数不要超过max_boost指定的值

返回结果:

代码语言:javascript复制
{
  "took": 87,
  "timed_out": false,
  "_shards": {
    "total": 1,
    "successful": 1,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 2,
    "max_score": 3.8050528,
    "hits": [
      {
        "_index": "forum",
        "_type": "article",
        "_id": "5",
        "_score": 3.8050528,
        "_source": {
          "articleID": "DHJK-B-1395-#Ky5",
          "userID": 3,
          "hidden": false,
          "postDate": "2019-05-01",
          "tag": [
            "elasticsearch"
          ],
          "tag_cnt": 1,
          "view_cnt": 10,
          "title": "this is spark blog",
          "content": "spark is best big data solution based on scala ,an programming language similar to java spark",
          "sub_title": "haha, hello world",
          "author_first_name": "Tonny",
          "author_last_name": "Peter Smith",
          "new_author_last_name": "Peter Smith",
          "new_author_first_name": "Tonny",
          "follower_num": 60
        }
      },
      {
        "_index": "forum",
        "_type": "article",
        "_id": "2",
        "_score": 1.7247463,
        "_source": {
          "articleID": "KDKE-B-9947-#kL5",
          "userID": 1,
          "hidden": false,
          "postDate": "2017-01-02",
          "tag": [
            "java"
          ],
          "tag_cnt": 1,
          "view_cnt": 50,
          "title": "this is java blog",
          "content": "i think java is the best programming language",
          "sub_title": "learned a lot of course",
          "author_first_name": "Smith",
          "author_last_name": "Williams",
          "new_author_last_name": "Williams",
          "new_author_first_name": "Smith",
          "follower_num": 10
        }
      }
    ]
  }
}

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