MongoDB全文搜索分数“；分数是什么意思；

我正在为我的学校做一个MongoDB项目。我有一个句子集合，我做了一个普通的文本搜索，以找到集合中最相似的句子，这是基于评分的

我运行此查询

当我查询句子时，看看这些结果

"that kicking a dog causes it pain"
----Matched With
"that kicking a dog causes it pain – is not very controversial."
----Give a Result of:
*score: 2.4*


"This sentence have nothing to do with any other"
----Matched With
"Who is the “He” in this sentence?"
----Give a result of:
*Score: 1.0* 

分数值是多少？这是什么意思？ 如果我想显示相似性仅为70%及以上的结果，该怎么办

---

如何解释评分结果以便显示相似性百分比，我使用C#来实现这一点，但不必担心实现。我不介意使用伪代码解决方案

文本搜索为索引字段中包含搜索词的每个文档分配分数。分数决定文档与给定搜索查询的相关性

对于文档中的每个索引字段，MongoDB将匹配数乘以权重，并对结果求和。然后，使用这个总和，MongoDB计算文档的分数

索引字段的默认权重为1

---

使用MongoDB文本索引时，它会为每个匹配文档生成一个分数。此分数表示搜索字符串与文档的匹配程度。分数越高，越有可能与搜索到的文本相似。分数由以下公式计算：

Step 1: Let the search text = S
Step 2: Break S into tokens (If you are not doing a Phrase search). Let's say T1, T2..Tn. Apply Stemming to each token
Step 3: For every search token, calculate score per index field of text index as follows:
       
score = (weight * data.freq * coeff * adjustment);
       
Where :
weight = user Defined Weight for any field. Default is 1 when no weight is specified
data.freq = how frequently the search token appeared in the text
coeff = ​(0.5 * data.count / numTokens) + 0.5
data.count = Number of matching token
numTokens = Total number of tokens in the text
adjustment = 1 (By default).If the search token is exactly equal to the document field then adjustment = 1.1
Step 4: Final score of document is calculated by adding all tokens scores per text index field
Total Score = score(T1) + score(T2) + .....score(Tn)

因此，如上所述，分数受以下因素影响：

与实际搜索文本匹配的术语数量越多，则得分越高

文档字段中的标记数

搜索的文本是否与文档字段完全匹配

以下是其中一个文档的派生：

Search String = This sentence have nothing to do with any other
Document = Who is the “He” in this sentence?

Score Calculation:
Step 1: Tokenize search string.Apply Stemming and remove stop words.
    Token 1: "sentence"
    Token 2: "nothing"
Step 2: For every search token obtained in Step 1, do steps 3-11:
        
      Step 3: Take Sample Document and Remove Stop Words
            Input Document:  Who is the “He” in this sentence?
            Document after stop word removal: "sentence"
      Step 4: Apply Stemming 
        Document in Step 3: "sentence"
        After Stemming : "sentence"
      Step 5: Calculate data.count per search token 
              data.count(sentence)= 1
              data.count(nothing)= 1
      Step 6: Calculate total number of token in document
              numTokens = 1
      Step 7: Calculate coefficient per search token
              coeff = ​(0.5 * data.count / numTokens) + 0.5
              coeff(sentence) =​ 0.5*(1/1) + 0.5 = 1.0
              coeff(nothing) =​ 0.5*(1/1) + 0.5 = 1.0    
      Step 8: Calculate adjustment per search token (Adjustment is 1 by default. If the search text match exactly with the raw document only then adjustment = 1.1)
              adjustment(sentence) = 1
              adjustment(nothing) =​ 1
      Step 9: weight of field (1 is default weight)
              weight = 1
      Step 10: Calculate frequency of search token in document (data.freq)
           For ever ith occurrence, the data frequency = 1/(2^i). All occurrences are summed.
            a. Data.freq(sentence)= 1/(2^0) = 1
            b. Data.freq(nothing)= 0
      Step 11: Calculate score per search token per field:
         score = (weight * data.freq * coeff * adjustment);
         score(sentence) = (1 * 1 * 1.0 * 1.0) = 1.0
         score(nothing) = (1 * 0 * 1.0 * 1.0) = 0
Step 12: Add individual score for every token of search string to get total score
Total score = score(sentence) + score(nothing) = 1.0 + 0.0 = 1.0 

用同样的方法，你可以导出另一个

有关更详细的MongoDB分析，请检查：

---

70%的相似性意味着什么？你想用什么样的分数来衡量相似性？我实际上在尝试制作一个剽窃软件，你可以上传你的文档，然后将每个句子与一组句子进行比较。所以，当最高分数的句子相似程度达到70%或以上时，就有可能发生剽窃。@NasriYatim你找到了吗？嗨，Nasri，我也是MongoDB的新手，对我来说，我需要从名称字段中搜索名称“Raja Sekar”，我已经为其编制了索引。但我的条件是搜索词应该匹配75%的相似记录。你能在这方面帮助我吗？不要抄袭，用例子来解释会很有帮助。

Search String = This sentence have nothing to do with any other
Document = Who is the “He” in this sentence?

Score Calculation:
Step 1: Tokenize search string.Apply Stemming and remove stop words.
    Token 1: "sentence"
    Token 2: "nothing"
Step 2: For every search token obtained in Step 1, do steps 3-11:
        
      Step 3: Take Sample Document and Remove Stop Words
            Input Document:  Who is the “He” in this sentence?
            Document after stop word removal: "sentence"
      Step 4: Apply Stemming 
        Document in Step 3: "sentence"
        After Stemming : "sentence"
      Step 5: Calculate data.count per search token 
              data.count(sentence)= 1
              data.count(nothing)= 1
      Step 6: Calculate total number of token in document
              numTokens = 1
      Step 7: Calculate coefficient per search token
              coeff = ​(0.5 * data.count / numTokens) + 0.5
              coeff(sentence) =​ 0.5*(1/1) + 0.5 = 1.0
              coeff(nothing) =​ 0.5*(1/1) + 0.5 = 1.0    
      Step 8: Calculate adjustment per search token (Adjustment is 1 by default. If the search text match exactly with the raw document only then adjustment = 1.1)
              adjustment(sentence) = 1
              adjustment(nothing) =​ 1
      Step 9: weight of field (1 is default weight)
              weight = 1
      Step 10: Calculate frequency of search token in document (data.freq)
           For ever ith occurrence, the data frequency = 1/(2^i). All occurrences are summed.
            a. Data.freq(sentence)= 1/(2^0) = 1
            b. Data.freq(nothing)= 0
      Step 11: Calculate score per search token per field:
         score = (weight * data.freq * coeff * adjustment);
         score(sentence) = (1 * 1 * 1.0 * 1.0) = 1.0
         score(nothing) = (1 * 0 * 1.0 * 1.0) = 0
Step 12: Add individual score for every token of search string to get total score
Total score = score(sentence) + score(nothing) = 1.0 + 0.0 = 1.0