Understanding Scoring in Solr

Question

Ich bin neu in Solr und versuche, einige Dokumente zu indizieren, wo jedes Dokument ein JSON ist. Es gibt einige Dokumente, deren Bewertung hoch sein sollte, aber eine sehr niedrige Punktzahl haben. Der Feldtyp, für den ich abfrage, ist text_general. Benötigen Sie ein Verständnis der Felder wie tfNorm, Feldlänge usw.

Attached ist das Ergebnis der Debug-Abfrage.

"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", 

Nach der Abfrage, die ich die Partitur von „c69628bbb1d9f3ca“ eingereicht haben, sollte höher sein als der andere documents.What sein, die ich hier zu verstehen, bin fehlt. Bitte erkläre.

Answer 1

Von der Fehlersuche abfragst du das Feld channel_genre. Für das Feld c69628bbb1d9f3ca wird die Punktzahl durch die Anzahl der Terme und die Feldlänge beeinflusst, aber die Ergebnisse unterscheiden sich nur marginal in den Ergebnissen.

• Begriff Frequenz an, wie oft ein Begriff erscheint im Feld, mehr Spiele, desto wichtiger ist das Ergebnis
• Feldlänge - kürzere Felder sind weniger wahrscheinlich Treffer enthalten, um einen Schub zu bekommen.

Verwenden Sie den Standardabfrageparser?

Vielleicht können Sie erklären, warum Sie die Ergebnisse für falsch halten?

Beachten Sie auch omitNorms = "true", wenn Sie die Längennormalisierung deaktivieren möchten.