Elasticsearch的相關度評分(relevance score)算法採用的是term frequency/inverse document frequency算法,簡稱爲TF/IDF算法。node
例如:
搜索請求:hello world
doc1: hello you, and world is very good
doc2: hello, how are you
那麼此時根據TF算法,doc1的相關度要比doc2的要高算法
搜索請求: hello world
doc1: hello, today is very good.
doc2: hi world, how are you.
好比在index中有1萬條document, hello這個單詞在全部的document中,一共出現了1000次,world這個單詞在全部的document中一共出現100次。那麼根據IDF算法此時doc2的相關度要比doc1要高。code
field-length norm就是field長度越長,相關度就越弱
搜索請求:hello world
doc1: {"title": "hello article", "content": "1萬個單詞"}
doc2: {"title": "my article", "content": "1萬個單詞, hi world"}
此時hello world在整個index中出現的次數是同樣多的。可是根據Field-length norm此時doc1比doc2相關度要高。由於title字段更短。orm
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" : [ ] } ] } ] } ] } } ] } }
匹配的文檔有兩個,下面直接用一個文檔來分析出ES各個算法的公式。
從上面能夠看出第一個文檔的相關度分數是0.20521778索引
{ "_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" : [ ] } ] } ] } ] } }
經過觀察咱們能夠知道 _score = boost * idf * tf 此時boost = 2.2, idf = 0.18232156, tf = 0.5116279 idf = log(1 + (N - n + 0.5) / (n + 0.5)) 此時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)) 此時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)