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自定义结果中文档的分数

您可以自定义结果中各个文档的 分数。通过调整分数的计算方式，您可以确保最相关的文档在搜索结果中排名更高。要了解有关自定义分数的不同方法，请参阅对结果中的文档进行评分。此页面演示如何：

  • 修改结果中文档的分数，以提升或隐藏结果。

  • 在聚合管道的后续阶段中，将 $search 查询分数标准化为 0 至 1 范围。

修改结果中文档的评分

MongoDB Search 查询会根据每个返回文档的相关性为其分配分数。结果集中包含的文档会按从最高分到最低分的顺序返回。要了解更多信息，请参阅对结果中的文档进行评分。

您可以使用以下选项与所有操作符修改默认评分行为。有关详细信息和示例，请单击以下任一选项：

本节展示了如何为搜索字段添加权重，以提升或隐藏结果或结果类别中的文档。具体来说，它演示了如何将一个或多个值分配给字段，以返回得分增加或减少的结果。

示例索引

您可以设置一个启用了 动态映射的索引，以为集合中的所有字段建立索引。或者，在要查询的字段上使用静态映射，并按此字段对结果进行排序。要了解有关创建 MongoDB Search 索引的更多信息，请参阅 管理 MongoDB Search 索引。

示例查询

这些示例查询演示了如何在结果中提升或隐藏文档。它们使用复合操作符将两个或多个操作符组合成一个查询。

提升结果

使用复合运算符将两个或多个操作符组合成一个查询。使用分数选项更改MongoDB Search 返回的相关性分数。

使用 sample_mflix.movies 命名空间中的 title 和 year 字段来提升 MongoDB Search 返回的包含术语 snow 的电影标题的相关性分数。如果您在 movies 集合上设置索引，则可以运行以下查询。

隐藏结果

使用复合运算符将两个或多个操作符组合成一个查询。使用分数选项更改MongoDB Search 返回的相关性分数。

以下示例查询使用 title、plot 和 genres 字段在 sample_mflix.movies 命名空间中执行以下搜索：

  • 搜索所有包含单词 ghost 的电影，但将喜剧电影的分数降低到 50%。

  • 搜索包含单词 ghost 的所有电影，但将具有指定 ObjectIds 的电影的分数降低 50%。

对分数进行规范化

您可以在聚合管道的后续阶段将$search查询分数标准化为0到1的范围。 您可以在$search阶段之后按以下顺序使用以下阶段来标准化分数：

  • $addFields

    {
      "$addFields": {
      "score": {
         "$meta": "searchScore"
      }
     }
    }

  • $setWindowFields

    {
      "$setWindowFields": {
        "output": {
          "maxScore": {
            "$max": "$score"
          }
        }
      }
    }

  • $addFields

    {
      "$addFields": {
        "normalizedScore": {
          "$divide": [
            "$score", "$maxScore"
          ]
        }
      }
    }
1db.movies.aggregate([{
2    "$search": {
3      "text": {
4        "query": "Helsinki",
5        "path": "plot"
6      }
7    }
8  },
9  {
10    "$limit": 5
11  },
12  {
13    "$project": {
14      "_id": 0,
15      "title": 1,
16      "score": 1,
17      "maxScore": 1,
18      "normalizedScore": 1
19    }
20  },
21  {
22    "$addFields": {
23      "score": {
24        "$meta": "searchScore"
25      }
26    }
27  },
28  {
29    "$setWindowFields": {
30      "output": {
31        "maxScore": {
32          "$max": "$score"
33        }
34      }
35    }
36  },
37  {
38    "$addFields": {
39      "normalizedScore": {
40        "$divide": [
41          "$score", "$maxScore"
42        ]
43      }
44    }
45}])
1[
2    {
3      title: 'Drifting Clouds',
4      score: 4.5660295486450195,
5      maxScore: 4.5660295486450195,
6      normalizedScore: 1
7    },
8    {
9      title: 'Sairaan kaunis maailma',
10      score: 4.041563034057617,
11      maxScore: 4.5660295486450195,
12      normalizedScore: 0.8851372929150143
13    },
14    {
15      title: 'Bad Luck Love',
16      score: 3.6251673698425293,
17      maxScore: 4.5660295486450195,
18      normalizedScore: 0.79394303764817
19    },
20    {
21      title: 'Bad Luck Love',
22      score: 3.6251673698425293,
23      maxScore: 4.5660295486450195,
24      normalizedScore: 0.79394303764817
25    },
26    {
27      title: 'Forbidden Fruit',
28      score: 3.6251673698425293,
29      maxScore: 4.5660295486450195,
30      normalizedScore: 0.79394303764817
31    }
32]
1db.movies.aggregate([{
2    "$search": {
3      "text": {
4        "path": "title",
5        "query": "men",
6        "score": {
7          "function":{
8            "multiply":[
9              {
10                "path": {
11                  "value": "imdb.rating",
12                  "undefined": 2
13                }
14              },
15              {
16                "score": "relevance"
17              }
18            ]
19          }
20        }
21      }
22    }
23  },
24  {
25    "$limit": 5
26  },
27  {
28    "$addFields": {
29      "score": {
30        "$meta": "searchScore"
31      }
32    }
33  },
34  {
35    "$setWindowFields": {
36      "output": {
37        "maxScore": {
38          "$max": "$score"
39        }
40      }
41    }
42  },
43  {
44    "$addFields": {
45      "normalizedScore": {
46        "$divide": [
47          "$score", "$maxScore"
48        ]
49      }
50    }
51  },
52  {
53    "$project": {
54      "_id": 0,
55      "title": 1,
56      "score": 1,
57      "maxScore": 1,
58      "normalizedScore": 1
59    }
60}])
1[
2    {
3      title: 'Men...',
4      score: 23.431293487548828,
5      maxScore: 23.431293487548828,
6      normalizedScore: 1
7    },
8    {
9      title: '12 Angry Men',
10      score: 22.080968856811523,
11      maxScore: 23.431293487548828,
12      normalizedScore: 0.9423708882544255
13    },
14    {
15      title: 'X-Men',
16      score: 21.34803581237793,
17      maxScore: 23.431293487548828,
18      normalizedScore: 0.911090795039637
19    },
20    {
21      title: 'X-Men',
22      score: 21.34803581237793,
23      maxScore: 23.431293487548828,
24      normalizedScore: 0.911090795039637
25    },
26    {
27      title: 'Matchstick Men',
28      score: 21.05954933166504,
29      maxScore: 23.431293487548828,
30      normalizedScore: 0.8987787781692841
31    }
32]
1db.movies.aggregate([{
2    "$search": {
3      "text": {
4        "path": "title",
5        "query": "shop",
6        "score": {
7          "function":{
8            "gauss": {
9              "path": {
10                "value": "imdb.rating",
11                "undefined": 4.6
12              },
13              "origin": 9.5,
14              "scale": 5,
15              "offset": 0,
16              "decay": 0.5
17            }
18          }
19        }
20      }
21    }
22  },
23  {
24    "$limit": 5
25  },
26  {
27    "$addFields": {
28      "score": {
29        "$meta": "searchScore"
30      }
31    }
32  },
33  {
34    "$setWindowFields": {
35      "output": {
36        "maxScore": {
37          "$max": "$score"
38        }
39      }
40    }
41  },
42  {
43    "$addFields": {
44      "normalizedScore": {
45        "$divide": [
46          "$score", "$maxScore"
47        ]
48      }
49    }
50  },
51  {
52    "$project": {
53      "_id": 0,
54      "title": 1,
55      "score": 1,
56      "maxScore": 1,
57      "normalizedScore": 1
58    }
59}])
1[
2    {
3      title: 'The Shop Around the Corner',
4      score: 0.9471074342727661,
5      maxScore: 0.9471074342727661,
6      normalizedScore: 1
7    },
8    {
9      title: 'Exit Through the Gift Shop',
10      score: 0.9471074342727661,
11      maxScore: 0.9471074342727661,
12      normalizedScore: 1
13    },
14    {
15      title: 'The Shop on Main Street',
16      score: 0.9395227432250977,
17      maxScore: 0.9471074342727661,
18      normalizedScore: 0.9919917310611205
19    },
20    {
21      title: 'Chop Shop',
22      score: 0.8849083781242371,
23      maxScore: 0.9471074342727661,
24      normalizedScore: 0.9343273488331464
25    },
26    {
27      title: 'Little Shop of Horrors',
28      score: 0.8290896415710449,
29      maxScore: 0.9471074342727661,
30      normalizedScore: 0.8753913353110349
31    }
32]
1db.movies.aggregate([{
2    "$search": {
3      "text": {
4        "path": "title",
5        "query": "men",
6        "score": {
7          "function":{
8            "path": {
9              "value": "imdb.rating",
10              "undefined": 4.6
11            }
12          }
13        }
14      }
15    }
16  },
17  {
18    "$limit": 5
19  },
20  {
21    "$addFields": {
22      "score": {
23        "$meta": "searchScore"
24      }
25    }
26  },
27  {
28    "$setWindowFields": {
29      "output": {
30        "maxScore": {
31          "$max": "$score"
32        }
33      }
34    }
35  },
36  {
37    "$addFields": {
38      "normalizedScore": {
39        "$divide": [
40          "$score", "$maxScore"
41        ]
42      }
43    }
44  },
45  {
46    "$project": {
47      "_id": 0,
48      "title": 1,
49      "score": 1,
50      "maxScore": 1,
51      "normalizedScore": 1
52    }
53}])
1[
2    {
3      title: '12 Angry Men',
4      score: 8.899999618530273,
5      maxScore: 8.899999618530273,
6      normalizedScore: 1
7    },
8    {
9      title: 'The Men Who Built America',
10      score: 8.600000381469727,
11      maxScore: 8.899999618530273,
12      normalizedScore: 0.9662922191102197
13    },
14    {
15      title: 'No Country for Old Men',
16      score: 8.100000381469727,
17      maxScore: 8.899999618530273,
18      normalizedScore: 0.9101124414213563
19    },
20    {
21      title: 'X-Men: Days of Future Past',
22      score: 8.100000381469727,
23      maxScore: 8.899999618530273,
24      normalizedScore: 0.9101124414213563
25    },
26    {
27      title: 'The Best of Men',
28      score: 8.100000381469727,
29      maxScore: 8.899999618530273,
30      normalizedScore: 0.9101124414213563
31    }
32]
1db.movies.aggregate([{
2    "$search": {
3        "text": {
4          "path": "title",
5          "query": "men",
6          "score": {
7            "function": {
8              "log": {
9                "path": {
10                "value": "imdb.rating",
11                "undefined": 10
12                }
13              }
14            }
15          }
16        }
17      }
18    },
19    {
20      "$limit": 5
21    },
22    {
23      "$addFields": {
24        "score": {
25          "$meta": "searchScore"
26        }
27      }
28    },
29    {
30      "$setWindowFields": {
31        "output": {
32          "maxScore": {
33            "$max": "$score"
34          }
35        }
36      }
37    },
38    {
39      "$addFields": {
40        "normalizedScore": {
41          "$divide": [
42              "$score", "$maxScore"
43        ]
44        }
45      }
46    },
47    {
48      "$project": {
49        "_id": 0,
50        "title": 1,
51        "score": 1,
52        "maxScore": 1,
53        "normalizedScore": 1
54      }
55    }
56])
1[
2    {
3        title: '12 Angry Men',
4        score: 0.9493899941444397,
5        maxScore: 0.9493899941444397,
6        normalizedScore: 1
7    },
8    {
9        title: 'The Men Who Built America',
10        score: 0.9344984292984009,
11        maxScore: 0.9493899941444397,
12        normalizedScore: 0.9843145968064908
13    },
14    {
15        title: 'No Country for Old Men',
16        score: 0.9084849953651428,
17        maxScore: 0.9493899941444397,
18        normalizedScore: 0.9569144408182233
19    },
20    {
21        title: 'X-Men: Days of Future Past',
22        score: 0.9084849953651428,
23        maxScore: 0.9493899941444397,
24        normalizedScore: 0.9569144408182233
25    },
26    {
27        title: 'The Best of Men',
28        score: 0.9084849953651428,
29        maxScore: 0.9493899941444397,
30        normalizedScore: 0.9569144408182233
31    }
32]

MongoDB搜索结果包含以下分数：

  • The modified score for the $search query in the score field from the $addFields stage.

  • The maximum score assigned to the documents in the results in the maxScore field from the $setWindowFields stage.

  • The normalized score in the normalizedScore field from the $addFields stage. MongoDB computes this score by dividing the modified score in $score by the maximum score in $maxScore using $divide.

继续学习

To learn more about compound queries using MongoDB Search, take Unit 9 of the Intro To MongoDB Course on MongoDB University. The 1.5-hour unit includes an overview of MongoDB Search. The unit also covers creating MongoDB Search indexes, running $search queries using compound operators, and grouping results using facet (MongoDB Search Operator).

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