我是Solr的新手并试图索引一些文档，其中每个文档都是json。有些文档的分数应该很高但分数很低。我查询的字段类型是text_general。 需要对tfNorm，field-length等字段有所了解。
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",
根据我提交的查询得分＆＃34; c69628bbb1d9f3ca＆＃34;应该高于其他文件。我在这里想念的是什么。请解释一下。
答案 0 :(得分：0)
在调试中，您要查询 channel_genre 字段。对于字段 c69628bbb1d9f3ca ，分数受术语数量和字段长度的影响，但结果中的分数仅略有不同。
您使用的是标准查询解析器吗？
也许您可以解释为什么您认为结果不正确？
另请考虑omitNorms =＆＃34; true＆＃34;如果你想禁用长度标准化。