了解Solr中的评分

我是Solr的新手，并尝试索引一些文档，其中每个文档都是json。有一些文件的得分应该很高，但得分非常低。我查询的字段类型是text_general。需要对tfNorm，字段长度等字段有所了解了解Solr中的评分

附加是调试查询的结果。

"718152d81b4db95f":"\n1.0891073 = sum of:\n 0.5578956 = weight(channel_genre:sports in 53) [SchemaSimilarity], result of:\n 0.5578956 = score(doc=53,freq=11.0 = termFreq=11.0\n), product of:\n  0.29769886 = idf(docFreq=223, docCount=300)\n  1.8740268 = tfNorm, computed from:\n  11.0 = termFreq=11.0\n  1.2 = parameter k1\n  0.75 = parameter b\n  142.80667 = avgFieldLength\n  256.0 = fieldLength\n 0.53121173 = weight(channel_genre:kids in 53) [SchemaSimilarity], result of:\n 0.53121173 = score(doc=53,freq=12.0 = termFreq=12.0\n), product of:\n  0.27996004 = idf(docFreq=227, docCount=300)\n  1.8974556 = tfNorm, computed from:\n  12.0 = termFreq=12.0\n  1.2 = parameter k1\n  0.75 = parameter b\n  142.80667 = avgFieldLength\n  256.0 = fieldLength\n", 
    "7071fa048f60603":"\n1.0834496 = sum of:\n 0.5491592 = weight(channel_genre:sports in 75) [SchemaSimilarity], result of:\n 0.5491592 = score(doc=75,freq=23.0 = termFreq=23.0\n), product of:\n  0.29769886 = idf(docFreq=223, docCount=300)\n  1.8446804 = tfNorm, computed from:\n  23.0 = termFreq=23.0\n  1.2 = parameter k1\n  0.75 = parameter b\n  142.80667 = avgFieldLength\n  655.36 = fieldLength\n 0.53429043 = weight(channel_genre:kids in 75) [SchemaSimilarity], result of:\n 0.53429043 = score(doc=75,freq=29.0 = termFreq=29.0\n), product of:\n  0.27996004 = idf(docFreq=227, docCount=300)\n  1.9084525 = tfNorm, computed from:\n  29.0 = termFreq=29.0\n  1.2 = parameter k1\n  0.75 = parameter b\n  142.80667 = avgFieldLength\n  655.36 = fieldLength\n", 
    "17e4a205707dc974":"\n1.0824875 = sum of:\n 0.62048614 = weight(channel_genre:sports in 64) [SchemaSimilarity], result of:\n 0.62048614 = score(doc=64,freq=24.0 = termFreq=24.0\n), product of:\n  0.29769886 = idf(docFreq=223, docCount=300)\n  2.0842745 = tfNorm, computed from:\n  24.0 = termFreq=24.0\n  1.2 = parameter k1\n  0.75 = parameter b\n  142.80667 = avgFieldLength\n  163.84 = fieldLength\n 0.46200132 = weight(channel_genre:kids in 64) [SchemaSimilarity], result of:\n 0.46200132 = score(doc=64,freq=4.0 = termFreq=4.0\n), product of:\n  0.27996004 = idf(docFreq=227, docCount=300)\n  1.6502403 = tfNorm, computed from:\n  4.0 = termFreq=4.0\n  1.2 = parameter k1\n  0.75 = parameter b\n  142.80667 = avgFieldLength\n  163.84 = fieldLength\n", 
    "1a48c3a658cc07af":"\n1.0820175 = sum of:\n 0.58498204 = weight(channel_genre:sports in 59) [SchemaSimilarity], result of:\n 0.58498204 = score(doc=59,freq=16.0 = termFreq=16.0\n), product of:\n  0.29769886 = idf(docFreq=223, docCount=300)\n  1.9650128 = tfNorm, computed from:\n  16.0 = termFreq=16.0\n  1.2 = parameter k1\n  0.75 = parameter b\n  142.80667 = avgFieldLength\n  256.0 = fieldLength\n 0.49703547 = weight(channel_genre:kids in 59) [SchemaSimilarity], result of:\n 0.49703547 = score(doc=59,freq=8.0 = termFreq=8.0\n), product of:\n  0.27996004 = idf(docFreq=227, docCount=300)\n  1.7753801 = tfNorm, computed from:\n  8.0 = termFreq=8.0\n  1.2 = parameter k1\n  0.75 = parameter b\n  142.80667 = avgFieldLength\n  256.0 = fieldLength\n", 
    "e073dacae12f494b":"\n1.0804946 = sum of:\n 0.5613358 = weight(channel_genre:sports in 17) [SchemaSimilarity], result of:\n 0.5613358 = score(doc=17,freq=19.0 = termFreq=19.0\n), product of:\n  0.29769886 = idf(docFreq=223, docCount=300)\n  1.8855827 = tfNorm, computed from:\n  19.0 = termFreq=19.0\n  1.2 = parameter k1\n  0.75 = parameter b\n  142.80667 = avgFieldLength\n  455.1111 = fieldLength\n 0.51915884 = weight(channel_genre:kids in 17) [SchemaSimilarity], result of:\n 0.51915884 = score(doc=17,freq=17.0 = termFreq=17.0\n), product of:\n  0.27996004 = idf(docFreq=227, docCount=300)\n  1.8544034 = tfNorm, computed from:\n  17.0 = termFreq=17.0\n  1.2 = parameter k1\n  0.75 = parameter b\n  142.80667 = avgFieldLength\n  455.1111 = fieldLength\n", 
    "c69628bbb1d9f3ca":"\n1.0785265 = sum of:\n 0.55884564 = weight(channel_genre:sports in 96) [SchemaSimilarity], result of:\n 0.55884564 = score(doc=96,freq=14.0 = termFreq=14.0\n), product of:\n  0.29769886 = idf(docFreq=223, docCount=300)\n  1.877218 = tfNorm, computed from:\n  14.0 = termFreq=14.0\n  1.2 = parameter k1\n  0.75 = parameter b\n  142.80667 = avgFieldLength\n  334.36734 = fieldLength\n 0.51968086 = weight(channel_genre:kids in 96) [SchemaSimilarity], result of:\n 0.51968086 = score(doc=96,freq=13.0 = termFreq=13.0\n), product of:\n  0.27996004 = idf(docFreq=227, docCount=300)\n  1.8562679 = tfNorm, computed from:\n  13.0 = termFreq=13.0\n  1.2 = parameter k1\n  0.75 = parameter b\n  142.80667 = avgFieldLength\n  334.36734 = fieldLength\n",

据我所提交的“c69628bbb1d9f3ca”分数查询应该比其他documents.What我很想念这里了解高。请解释。

来源

2017-01-23 annu

从调试中查询channel_genre字段。对于字段c69628bbb1d9f3ca该分数受术语数量和字段长度的影响，但分数仅在结果中略有不同。

词频是一个长期的频率出现在现场的措施，更多的比赛，更重要的结果
字段长度 - 短的领域不太可能包含命中因此获得提振。

您是否正在使用标准查询解析器？

也许你可以解释你为什么认为结果不正确？

如果要禁用长度标准化，还可以考虑omitNorms =“true”。

来源

2017-01-23 08:47:49

在Solr的查询是 - channel_genre：“体育” AND channel_genre：“孩子”，即返回的文档数（谁看孩子和体育预先显性用户数）：150 最大分值：1.2256454 我专门增加了100名经常观看Kids和Sports的用户来验证他们是否进入前100名。但是有6名用户降到100以下，“c69628bbb1d9f3ca”就是这样一个用户。只是想了解场地的长度是否对比分产生巨大影响。 – annu

鉴于你发布的领域的分数接近，我会说它在这种情况下。 –

顺便说一句，你考虑尝试omitNorms =“true”在你的领域，（应该禁用长度标准化） –

了解Solr中的评分

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