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IDFTerm 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 worlddoc1: hello you, and world is very gooddoc2: 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 worlddoc1: 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 normfield-length norm就是field长度越长相关度就越弱搜索请求hello worlddoc1: {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?explaintrue { 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(freq1.0), 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(freq1.0), 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(freq1.0), 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)