lucene评分机制研究(debug信息)

{
"id": 4111005,
"bookType": 2,
"title": "故宫",
"userId": "xxx",
"userName": "xxx",
"routeDays": 1,
"startTime": 1310313600000,
"cTime": 1407243889812,
"uTime": 1407244293220,
"avgPrice": 0,
"actorType": 0,
"tripType": 0,
"platform": 0,
"quality": 5365590,
"travelMonths": 64,
"sourceId": -1,
"labelName": "",
"imageUrl": "xxx",
"travelRouteTabbed": "故宫",
"travelRoute": "故宫",
"destCitiesTabbed": "北京",
"destCitiesGrouped": "北京",
"destCities": "北京",
"destProvinces": "",
"destNations": "中国",
"destNationsText": "中国",
"destContinents": "亚洲",
"destContinentsText": "亚洲",
"poiIdTabbed": "710603",
"distIds": "299914\t300667",
"provinceName": "",
"nationName": "",
"continentName": "",
"disallowed": "",
"viewCount": 36,
"quoteCount": 0,
"downloadCount": 0,
"ugcType": 2,
"ugcTime": 1407244293220,
"destWeights": "{\"中国\":1.0,\"北京\":1.0,\"china\":1.0}",
"fingerPrint": "153a926f3b27c63889b5dc3e9b69f9ba",
"_version_": 1497957597913809000,
"score": 145735744,
"payload": "score:145735744,matchscore:14.573575,destscore:0.0"
}

1 &＃39;4111005&＃39;&＃61;>&＃39;//ID号2 14.557129 &＃61; (MATCH) sum of://最后得分 &＃61; 11.167477&＃xff08;相似性评分&＃xff09; &＃43; 3.3896518&＃xff08;boost query打分&＃xff09;3 11.167477 &＃61; (MATCH) max of://相似性评分(max of &＃xff1a;11.167477&＃xff08;命中标题得分&＃xff09;&＃xff0c;0.40179574&＃xff08;命中途径得分&＃xff09;取其中最大的&＃xff1f;)
// DisjunctionMaxScore源码中对于score的计算为 scoreMax &＃43; (scoreSum - scoreMin) * tieBreakerMultipliter
// tieBreadkerMultipiler传值为0,所以最后就只取最大的匹配值

###################################### 命中标题部分 #####################################4 11.167477 &＃61; (MATCH) weight(title:故宫^3.0 in 212390) [DefaultSimilarity], result of://命中标题得分明细5 11.167477 &＃61; score(doc&＃61;212390,freq&＃61;1.0 &＃61; termFreq&＃61;1.0)// queryWeight * fieldWeight6 , product of://score &＃61; queryWeight * fieldWeight &＃61; boost * idf * queryNorm * tf * idf * fieldNorm// &＃61; tf * idf^2 * boost * fieldNorm * queryNorm//score &＃43; score &＃61; (tf * idf^2 * boost * fieldNorm &＃43; tf * idf^2 *boost *fieldNorm) * queryNorm 与评分公式一致7 0.99955523 &＃61; queryWeight, product of://queryWeight 为什么不等于boost * idf &＃xff1f;压缩误差&＃xff1f;8 3.0 &＃61; boost //qf&＃61;title^3 相当于termQuery.setBoost(3.0) 公式&＃xff1a;boost(t.field in d)9 11.172446 &＃61; idf(docFreq&＃61;44, maxDocs&＃61;1177741)//逆向文本频率idf&＃xff08;docFreq&＃xff1a;该term出现的doc数量44&＃xff0c;maxDocs&＃xff1a;所有doc数量1177741&＃xff09;
10 0.029822035 &＃61; queryNorm //查询权重&＃xff0c;不影响排序&＃xff0c;多个查询比较使用 公式queryNorm(q)部分
11 11.172446 &＃61; fieldWeight in 212390, product of://fieldWeight
12 1.0 &＃61; tf(freq&＃61;1.0), with freq of: //该term的词频,对应公式tf(t in d)部分
13 1.0 &＃61; termFreq&＃61;1.0 //该term在本doc中出现的次数
14 11.172446 &＃61; idf(docFreq&＃61;44, maxDocs&＃61;1177741) //逆向文本频率idf(t)部分
15 1.0 &＃61; fieldNorm(doc&＃61;212390)//公式中lengthNorm部分&＃61;state.getboost()*(1/maths.sqrt(numTerms)) numTerms为对应field的term数量 state.getboost()创建索引指定field权重&＃xff0c;例中title就是故宫&＃xff0c;分词切成一个term&＃xff0c;故numTerms&＃61;1

#################################### 命中途径部分 #######################################
16 0.40179574 &＃61; (MATCH) weight(travelRouteTabbed:故宫^0.5 in 212390) [DefaultSimilarity], result of://命中途径得分
17 0.40179574 &＃61; score(doc&＃61;212390,freq&＃61;1.0 &＃61; termFreq&＃61;1.0)
18　, product of:
19 0.07740273 &＃61; queryWeight, product of:
20 0.5 &＃61; boost //qf&＃61;travelRouteTabbed:故宫^0.5
21 5.190976 &＃61; idf(docFreq&＃61;17820, maxDocs&＃61;1177741) //逆向文本频率idf&＃xff08;docFreq&＃xff1a;该term出现的doc数量17820&＃xff0c;maxDocs&＃xff1a;所有doc数量1177741&＃xff09;
22 0.029822035 &＃61; queryNorm //查询权重&＃xff0c;不影响排序&＃xff0c;多个查询比较使用 公式queryNorm(q)部分
23 5.190976 &＃61; fieldWeight in 212390, product of:
24 1.0 &＃61; tf(freq&＃61;1.0), with freq of:
25 1.0 &＃61; termFreq&＃61;1.0
26 5.190976 &＃61; idf(docFreq&＃61;17820, maxDocs&＃61;1177741)
27 1.0 &＃61; fieldNorm(doc&＃61;212390)##################################### boost funtion部分 #################################
// 就debug信息来看 没有打印出公式中的coord部分&＃xff0c;而是使用lucene原有的公式加上了bf的分数&＃xff0c;bf最后的得分依照product of来看应该是
// sum * boost * queryNorm来得出&＃xff0c;那么bf所需要定制的分值就需要考虑乘以queryNorm之后的分数再去影响lucene自身的score&＃xff0c;是不是把queryNorm设置为1比较好
// 因为queryNorm对排序的结果没有影响&＃xff0c;最终的结果只会被所有命中term的累加和分数再加上bf分数影响&＃xff0c;那么coord去哪里呢&＃xff1f;
28 3.3896518 &＃61; (MATCH) FunctionQuery(sum(rord(routeDays),2000000.0/(1.0*float(rord(quality))&＃43;20000.0))), product of://boost query打分&＃xff09;
29 113.66266 &＃61; sum(rord(routeDays)&＃61;76,2000000.0/(1.0*float(rord(quality)&＃61;33103)&＃43;20000.0))//bf值的设定
30 1.0 &＃61; boost//默认权重是1
31 0.029822035 &＃61; queryNorm&＃39;