瞭解Solr中的評分

我是Solr的新手，並嘗試索引一些文檔，其中每個文檔都是json。有一些文件的得分應該很高，但得分非常低。我查詢的字段類型是text_general。需要對tfNorm，字段長度等字段有所瞭解瞭解Solr中的評分

附加是調試查詢的結果。

"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",

據我所提交的「c69628bbb1d9f3ca」分數查詢應該比其他documents.What我很想念這裏瞭解高。請解釋。

來源

2017-01-23 annu

從調試中查詢channel_genre字段。對於字段c69628bbb1d9f3ca該分數受術語數量和字段長度的影響，但分數僅在結果中略有不同。

詞頻是一個長期的頻率出現在現場的措施，更多的比賽，更重要的結果
字段長度 - 短的領域不太可能包含命中因此獲得提振。

您是否正在使用標準查詢解析器？

也許你可以解釋你爲什麼認爲結果不正確？

如果要禁用長度標準化，還可以考慮omitNorms =「true」。

來源

2017-01-23 08:47:49

在Solr的查詢是 - channel_genre：「體育」 AND channel_genre：「孩子」，即返回的文檔數（誰看孩子和體育預先顯性用戶數）：150 最大分值：1.2256454 我專門增加了100名經常觀看Kids和Sports的用戶來驗證他們是否進入前100名。但是有6名用戶降到100以下，「c69628bbb1d9f3ca」就是這樣一個用戶。只是想了解場地的長度是否對比分產生巨大影響。 – annu

鑑於你發佈的領域的分數接近，我會說它在這種情況下。 –

順便說一句，你考慮嘗試omitNorms =「true」在你的領域，（應該禁用長度標準化） –

瞭解Solr中的評分

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