ES实战-相关性搜索

ES打分机制
1.TF-IDF 词频-逆文档频率
2.Okapi BM25
3.随机性分歧- DFR相似度
4.基于信息 - IB相似度
5.LM Dirichlet 相似度
6.LM Jelinek Mercer相似度
解释一个查询的结果集

curl -XPOST 'localhost:9200/get-together/_search?pretty' -H 'Content-Type: application/json' -d '
{"query": {"match": {"description": "elasticsearch"}},"explain": true
}'

使用function_score来定制得分

#在description字段中匹配到hadoop
#和logstash的文档,分数就乘以2或3
POST /get-together/_search
{"query": {"function_score": {"query": {"match": {"description": "elasticsearch"}},"functions": [{"weight": 2,"filter": {"term": {"description": "hadoop"}}},{"weight": 3,"filter": {"term": {"description": "logstash"}}}]}}
}

合并得分
1.每个单独函数得分合并 score_mode
multiply,sum,avg,first,max,min
2.函数得分和原始得分合并 boost_mode
sum,avg,max,min,replace