背景
难道不好奇怎么评分的吗！？
直接先上例子
参考
官网
注意 在有分片的情况下 需要用routing来限制在一个shard里再搞，不然都搞不清楚什么情况
# 按照routing来索引
PUT /hjx_test_index/doc/1?routing=text1&refresh=true
{ "text" : "quick brown fox 11" }
PUT /hjx_test_index/doc/2?routing=text1&refresh=true
{ "text" : "bad fox" }
PUT /hjx_test_index/doc/3?routing=text1&refresh=true
{ "text" : "some test more some 35"
}
GET /hjx_test_index/doc/_search?explain=true&routing=text1
{
"query": {
"term": {
"text": "fox"
}
}
}
GET /hjx_test_index/doc/_search?routing=text1
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 1.2310667,
"hits": [
{
"_shard": "[hjx_test_index][4]",
"_node": "JUltA86XS-WNJ3baNQWlcg",
"_index": "hjx_test_index",
"_type": "doc",
"_id": "2",
"_score": 1.2310667,
"_routing": "text1",
"_source": {
"text": "bad fox"
},
"_explanation": {
"value": 1.2310667,
"description": "weight(text:fox in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 1.2310667,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 1.0296195,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
"details": [
{
"value": 2,
"description": "docFreq",
"details": []
},
{
"value": 6,
"description": "docCount",
"details": []
}
]
},
{
"value": 1.1956521,
"description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 3.3333333,
"description": "avgFieldLength",
"details": []
},
{
"value": 2,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
}
},
{
"_shard": "[hjx_test_index][4]",
"_node": "JUltA86XS-WNJ3baNQWlcg",
"_index": "hjx_test_index",
"_type": "doc",
"_id": "1",
"_score": 0.9517491,
"_routing": "text1",
"_source": {
"text": "quick brown fox 11"
},
"_explanation": {
"value": 0.9517491,
"description": "weight(text:fox in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 0.9517491,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 1.0296195,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
"details": [
{
"value": 2,
"description": "docFreq",
"details": []
},
{
"value": 6,
"description": "docCount",
"details": []
}
]
},
{
"value": 0.92436975,
"description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 3.3333333,
"description": "avgFieldLength",
"details": []
},
{
"value": 4,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
}
}
]
}
}
别慌，咱们来看看咋肥事
先看看官网的计算公式。最新的公式可能有所变化，但是我们可以记住的，不变的是，
最终是三个因素的乘积（tf * idf * fieldNorm）
上面这个是在搜索词只有一个的情况下的，如果搜索词有多个呢？
看下面这个结果
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 3,
"max_score": 1.5924733,
"hits": [
{
"_shard": "[hjx_test_index][4]",
"_node": "JUltA86XS-WNJ3baNQWlcg",
"_index": "hjx_test_index",
"_type": "doc",
"_id": "1",
"_score": 1.5924733,
"_routing": "text1",
"_source": {
"text": "quick brown fox 11"
},
"_explanation": {
"value": 1.5924734,
"description": "sum of:",
"details": [
{
"value": 0.9517491,
"description": "weight(text:quick in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 0.9517491,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 1.0296195,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
"details": [
{
"value": 2,
"description": "docFreq",
"details": []
},
{
"value": 6,
"description": "docCount",
"details": []
}
]
},
{
"value": 0.92436975,
"description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 3.3333333,
"description": "avgFieldLength",
"details": []
},
{
"value": 4,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
},
{
"value": 0.6407243,
"description": "weight(text:fox in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 0.6407243,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 0.6931472,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
"details": [
{
"value": 3,
"description": "docFreq",
"details": []
},
{
"value": 6,
"description": "docCount",
"details": []
}
]
},
{
"value": 0.92436975,
"description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 3.3333333,
"description": "avgFieldLength",
"details": []
},
{
"value": 4,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
}
]
}
},
{
"_shard": "[hjx_test_index][4]",
"_node": "JUltA86XS-WNJ3baNQWlcg",
"_index": "hjx_test_index",
"_type": "doc",
"_id": "3",
"_score": 1.4302213,
"_routing": "text1",
"_source": {
"text": "quick fox more some 35"
},
"_explanation": {
"value": 1.4302213,
"description": "sum of:",
"details": [
{
"value": 0.8547784,
"description": "weight(text:quick in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 0.8547784,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 1.0296195,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
"details": [
{
"value": 2,
"description": "docFreq",
"details": []
},
{
"value": 6,
"description": "docCount",
"details": []
}
]
},
{
"value": 0.8301887,
"description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 3.3333333,
"description": "avgFieldLength",
"details": []
},
{
"value": 5,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
},
{
"value": 0.57544297,
"description": "weight(text:fox in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 0.57544297,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 0.6931472,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
"details": [
{
"value": 3,
"description": "docFreq",
"details": []
},
{
"value": 6,
"description": "docCount",
"details": []
}
]
},
{
"value": 0.8301887,
"description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 3.3333333,
"description": "avgFieldLength",
"details": []
},
{
"value": 5,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
}
]
}
},
{
"_shard": "[hjx_test_index][4]",
"_node": "JUltA86XS-WNJ3baNQWlcg",
"_index": "hjx_test_index",
"_type": "doc",
"_id": "2",
"_score": 0.8287629,
"_routing": "text1",
"_source": {
"text": "bad fox"
},
"_explanation": {
"value": 0.8287629,
"description": "sum of:",
"details": [
{
"value": 0.8287629,
"description": "weight(text:fox in 0) [PerFieldSimilarity], result of:",
"details": [
{
"value": 0.8287629,
"description": "score(doc=0,freq=1.0 = termFreq=1.0\n), product of:",
"details": [
{
"value": 0.6931472,
"description": "idf, computed as log(1 + (docCount - docFreq + 0.5) / (docFreq + 0.5)) from:",
"details": [
{
"value": 3,
"description": "docFreq",
"details": []
},
{
"value": 6,
"description": "docCount",
"details": []
}
]
},
{
"value": 1.1956521,
"description": "tfNorm, computed as (freq * (k1 + 1)) / (freq + k1 * (1 - b + b * fieldLength / avgFieldLength)) from:",
"details": [
{
"value": 1,
"description": "termFreq=1.0",
"details": []
},
{
"value": 1.2,
"description": "parameter k1",
"details": []
},
{
"value": 0.75,
"description": "parameter b",
"details": []
},
{
"value": 3.3333333,
"description": "avgFieldLength",
"details": []
},
{
"value": 2,
"description": "fieldLength",
"details": []
}
]
}
]
}
]
}
]
}
}
]
}
}
可见是把两个词的得分加一起
但是官网写的是
文档向量
😂
有点无法理解，这怎么就文档向量了？！
看下
lucene是怎么说的
？
Lucene scoring uses a combination of the Vector Space Model (VSM) of Information Retrieval and the Boolean model to determine how relevant a given Document is to a User’s query. In general, the idea behind the VSM is the more times a query term appears in a document relative to the number of times the term appears in all the documents in the collection, the more relevant that document is to the query. It uses the Boolean model to first narrow down the documents that need to be scored based on the use of boolean logic in the Query specification. Lucene also adds some capabilities and refinements onto this model to support boolean and fuzzy searching, but it essentially remains a VSM based system at the heart. For some valuable references on VSM and IR in general refer to the Lucene Wiki IR references.
Lucene使用的是VSM和boolean模型的结合来决定query和文档的近似度。
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