召回率定义
比如你搜索一个java spark，总共有100个doc，能返回多少个doc作为结果，就是召回率，recall
精准度定义
比如你搜索一个java spark，能不能尽可能让包含java spark，或者是java和spark离的很近的doc，排在最前面，precision
直接用match_phrase短语搜索，会导致必须所有term都在doc field中出现，而且距离在slop限定范围内，才能匹配上
match phrase，proximity match，要求doc必须包含所有的term，才能作为结果返回；如果某一个doc可能就是有某个term没有包含，那么就无法作为结果返回
java spark --> hello world java --> 就不能返回了
java spark --> hello world, java spark --> 才可以返回
近似匹配的时候，召回率比较低，精准度太高了
但是有时可能我们希望的是匹配到几个term中的部分，就可以作为结果出来，这样可以提高召回率。同时我们也希望用上match_phrase根据距离提升分数的功能，让几个term距离越近分数就越高，优先返回
就是优先满足召回率，意思，java spark，包含java的也返回，包含spark的也返回，包含java和spark的也返回；同时兼顾精准度，就是包含java和spark，同时java和spark离的越近的doc排在最前面
此时可以用bool组合match query和match_phrase query一起，来实现上述效果
实验举例
| GET /forum/article/_search { "query": { "bool": { "must": { "match": { "title": { "query": "java spark" --> java或spark或java spark，java和spark靠前，但是没法区分java和spark的距离，也许java和spark靠的很近，但是没法排在最前面 } } }, "should": { "match_phrase": { --> 在slop以内，如果java spark能匹配上一个doc，那么就会对doc贡献自己的relevance score，如果java和spark靠的越近，那么就分数越高 "title": { "query": "java spark", "slop": 50 } } } } } }
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单独查询的实验一：
单独match保证招呼率，不保证精准度
| GET /forum/article/_search { "query": { "bool": { "must": [ { "match": { "content": "java spark" } } ] } } } 结果： { "took": 1, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 2, "max_score": 0.68640786, "hits": [ { "_index": "forum", "_type": "article", "_id": "2", "_score": 0.68640786, "_source": { "articleID": "KDKE-B-9947-#kL5", "userID": 1, "hidden": false, "postDate": "2017-01-02", "tag": [ "java" ], "tag_cnt": 1, "view_cnt": 50, "title": "this is java blog", "content": "i think java is the best programming language", "sub_title": "learned a lot of course", "author_first_name": "Smith", "author_last_name": "Williams" } }, { "_index": "forum", "_type": "article", "_id": "5", "_score": 0.68324494, "_source": { "articleID": "DHJK-B-1395-#Ky5", "userID": 3, "hidden": false, "postDate": "2017-03-01", "tag": [ "elasticsearch" ], "tag_cnt": 1, "view_cnt": 10, "title": "this is spark blog", "content": "spark is best big data solution based on scala ,an programming language similar to java spark", "sub_title": "haha, hello world", "author_first_name": "Tonny", "author_last_name": "Peter Smith" } } ] } } |
保证招呼率和精准度实验二：
| GET /forum/article/_search { "query": { "bool": { "must": [ { "match": { "content": "java spark" } } ], "should": [ { "match_phrase": { "content": { "query": "java spark", "slop": 50 } } } ] } } } 结果： { "took": 2, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 2, "max_score": 1.258609, "hits": [ { "_index": "forum", "_type": "article", "_id": "5", "_score": 1.258609, "_source": { "articleID": "DHJK-B-1395-#Ky5", "userID": 3, "hidden": false, "postDate": "2017-03-01", "tag": [ "elasticsearch" ], "tag_cnt": 1, "view_cnt": 10, "title": "this is spark blog", "content": "spark is best big data solution based on scala ,an programming language similar to java spark", "sub_title": "haha, hello world", "author_first_name": "Tonny", "author_last_name": "Peter Smith" } }, { "_index": "forum", "_type": "article", "_id": "2", "_score": 0.68640786, "_source": { "articleID": "KDKE-B-9947-#kL5", "userID": 1, "hidden": false, "postDate": "2017-01-02", "tag": [ "java" ], "tag_cnt": 1, "view_cnt": 50, "title": "this is java blog", "content": "i think java is the best programming language", "sub_title": "learned a lot of course", "author_first_name": "Smith", "author_last_name": "Williams" } } ] } } |