Elasticsearch 的相关度评分（relevance score）算法采用的是 term frequency/inverse document frequency 算法，简称为 TF/IDF 算法。
算法介绍
relevance score 算法，简单来说就是，就是计算出一个索引中的文本，与搜索文本，它们之间的关联匹配程度。
TF/IDF 算法，分为两个部分，IF 和 IDF
Term Frequency(TF): 搜索文本中的各个词条在 field 文本中出现了多少次，出现的次数越多，就越相关
例如：
搜索请求：hello world
doc1: hello you, and world is very good
doc2: hello, how are you
那么此时根据 TF 算法，doc1 的相关度要比 doc2 的要高
Inverse Document Frequency(IDF): 搜索文本中的各个词条在整个索引的所有文档中出现的次数，出现的次数越多，就越不相关。
搜索请求：hello world
doc1: hello, today is very good.
doc2: hi world, how are you.
比如在 index 中有 1 万条 document, hello 这个单词在所有的 document 中，一共出现了 1000 次，world 这个单词在所有的 document 中一共出现 100 次。那么根据 IDF 算法此时 doc2 的相关度要比 doc1 要高。
对于 ES 还有一个 Field-length norm
field-length norm 就是 field 长度越长，相关度就越弱
搜索请求：hello world
doc1: {“title”: “hello article”, “content”: “1 万个单词 ”}
doc2: {“title”: “my article”, “content”: “1 万个单词，hi world”}
此时 hello world 在整个 index 中出现的次数是一样多的。但是根据 Field-length norm 此时 doc1 比 doc2 相关度要高。因为 title 字段更短。
_score 是如何被计算出来的
GET /test_index/_search?explain=true
{
"query": {
"match": {"test_field": "hello"}
}
}
{
"took" : 9,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 2,
"relation" : "eq"
},
"max_score" : 0.20521778,
"hits" : [
{"_shard" : "[test_index][0]",
"_node" : "P-b-TEvyQOylMyEcMEhApQ",
"_index" : "test_index",
"_type" : "_doc",
"_id" : "2",
"_score" : 0.20521778,
"_source" : {"test_field" : "hello, how are you"},
"_explanation" : {
"value" : 0.20521778,
"description" : "weight(test_field:hello in 0) [PerFieldSimilarity], result of:",
"details" : [
{
"value" : 0.20521778,
"description" : "score(freq=1.0), product of:",
"details" : [
{
"value" : 2.2,
"description" : "boost",
"details" : []},
{
"value" : 0.18232156,
"description" : "idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:",
"details" : [
{
"value" : 2,
"description" : "n, number of documents containing term",
"details" : []},
{
"value" : 2,
"description" : "N, total number of documents with field",
"details" : []}
]
},
{
"value" : 0.5116279,
"description" : "tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:",
"details" : [
{
"value" : 1.0,
"description" : "freq, occurrences of term within document",
"details" : []},
{
"value" : 1.2,
"description" : "k1, term saturation parameter",
"details" : []},
{
"value" : 0.75,
"description" : "b, length normalization parameter",
"details" : []},
{
"value" : 4.0,
"description" : "dl, length of field",
"details" : []},
{
"value" : 5.5,
"description" : "avgdl, average length of field",
"details" : []}
]
}
]
}
]
}
},
{"_shard" : "[test_index][0]",
"_node" : "P-b-TEvyQOylMyEcMEhApQ",
"_index" : "test_index",
"_type" : "_doc",
"_id" : "1",
"_score" : 0.16402164,
"_source" : {"test_field" : "hello you, and world is very good"},
"_explanation" : {
"value" : 0.16402164,
"description" : "weight(test_field:hello in 0) [PerFieldSimilarity], result of:",
"details" : [
{
"value" : 0.16402164,
"description" : "score(freq=1.0), product of:",
"details" : [
{
"value" : 2.2,
"description" : "boost",
"details" : []},
{
"value" : 0.18232156,
"description" : "idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:",
"details" : [
{
"value" : 2,
"description" : "n, number of documents containing term",
"details" : []},
{
"value" : 2,
"description" : "N, total number of documents with field",
"details" : []}
]
},
{
"value" : 0.40892193,
"description" : "tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:",
"details" : [
{
"value" : 1.0,
"description" : "freq, occurrences of term within document",
"details" : []},
{
"value" : 1.2,
"description" : "k1, term saturation parameter",
"details" : []},
{
"value" : 0.75,
"description" : "b, length normalization parameter",
"details" : []},
{
"value" : 7.0,
"description" : "dl, length of field",
"details" : []},
{
"value" : 5.5,
"description" : "avgdl, average length of field",
"details" : []}
]
}
]
}
]
}
}
]
}
}匹配的文档有两个，下面直接用一个文档来分析出 ES 各个算法的公式。
从上面可以看出第一个文档的相关度分数是 0.20521778
{"_shard" : "[test_index][0]",
"_node" : "P-b-TEvyQOylMyEcMEhApQ",
"_index" : "test_index",
"_type" : "_doc",
"_id" : "2",
"_score" : 0.20521778,
"_source" : {"test_field" : "hello, how are you"},
"_explanation" : {
"value" : 0.20521778,
"description" : "weight(test_field:hello in 0) [PerFieldSimilarity], result of:",
"details" : [
{
"value" : 0.20521778,
"description" : "score(freq=1.0), product of:",
"details" : [
{
"value" : 2.2,
"description" : "boost",
"details" : []},
{
"value" : 0.18232156,
"description" : "idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) from:",
"details" : [
{
"value" : 2,
"description" : "n, number of documents containing term",
"details" : []},
{
"value" : 2,
"description" : "N, total number of documents with field",
"details" : []}
]
},
{
"value" : 0.5116279,
"description" : "tf, computed as freq / (freq + k1 * (1 - b + b * dl / avgdl)) from:",
"details" : [
{
"value" : 1.0,
"description" : "freq, occurrences of term within document",
"details" : []},
{
"value" : 1.2,
"description" : "k1, term saturation parameter",
"details" : []},
{
"value" : 0.75,
"description" : "b, length normalization parameter",
"details" : []},
{
"value" : 4.0,
"description" : "dl, length of field",
"details" : []},
{
"value" : 5.5,
"description" : "avgdl, average length of field",
"details" : []}
]
}
]
}
]
}
}
通过观察我们可以知道
_score = boost * idf * tf
此时 boost = 2.2, idf = 0.18232156, tf = 0.5116279
idf = log(1 + (N - n + 0.5) / (n + 0.5))
此时 n = 2 (n, number of documents containing term), N = 2(N, total number of documents with field)
tf = freq / (freq + k1 * (1 - b + b * dl / avgdl))
此时 freq = 1(freq, occurrences of term within document), k1 = 1.2(k1, term saturation parameter), b = 0.75(b, length normalization parameter), d1 = 4 (dl, length of field), avgdl = 5.5(avgdl, average length of field)