Solrでの得点の理解

Question

私はSolrには新しく、各文書がjsonであるいくつかの文書の索引付けを試みています。スコアが高いはずですがスコアが非常に低いドキュメントがいくつかあります。私が照会しているフィールドのタイプはtext_generalです。 tfNorm、フィールド長などのフィールドの理解が必要です。

添付されているのはデバッグクエリの結果です。

"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", 

は、私が「c69628bbb1d9f3ca」のスコアを提出したクエリによると、私は理解することが、ここで行方不明です他のdocuments.Whatよりも高くする必要があります。説明してください。

Answer 1

channel_genreフィールドをデバッグしています。フィールドc69628bbb1d9f3caの場合、スコアは用語の数とフィールドの長さの影響を受けますが、スコアはわずかに結果に違いがあります。

• 用語頻度は、用語がフィールドに表示される頻度の尺度、より多くの試合、多くの重要な結果
• フィールドの長さである - 短いフィールドにはヒットが含まれているので、ブーストを取得する可能性が低いです。

標準のクエリパーサーを使用していますか？

結果が間違っていると思われる理由を説明できますか？

また、長さの正規化を無効にする場合は、omitNorms = "true"としてください。