I am also busy with getting this clear. Here are my notes so far (by copying
and writing myself):
queryWeight = the impact of the query against the field
implementation: boost(query)*idf*queryNorm
boost(query) = boost of the field at query-time
Implication: hits in fields with higher boost get a higher score
Rationale: a term in field A could be more relevant than the same term
in field B
idf = inverse document frequency = measure of how often the term appears
across the index for this field
implementation: log(numDocs/(docFreq+1))+1
Implication: the greater the occurrence of a term in different
documents, the lower its score
Rationale: common terms are less important than uncommon ones
numDocs = the total number of documents in the index, not including those
that are marked as deleted but have not yet been purged. This is a constant
(the same value for all documents in the index).
docFreq = the number of documents in the index which contain the term in
this field. This is a constant (the same value for all documents in the index
containing this field)
queryNorm = normalization factor so that queries can be compared
implementation: 1/sqrt(sumOfSquaredWeights)
Implication: doesn't impact the relevancy of this result
Rationale: queryNorm is not related to the relevance of the document,
but rather tries to make scores between different queries comparable. This
value is equal for all results of the query
fieldWeight = the score of a term matching the field
implementation: tf*idf*fieldNorm
tf = term frequency in a field = measure of how often a term appears in the
field
implementation: sqrt(freq)
Implication: the more frequent a term occurs in a field, the greater
its score
Rationale: fields which contains more of a term are generally more
relevant
freq = termFreq = amount of times the term occurs in the field for this
document
fieldNorm = impact of a hit in this field
implementation: lengthNorm*boost(index)
lengthNorm = measure of the importance of a term according to the total
number of terms in the field
implementation: 1/sqrt(numTerms)
Implication: a term matched in fields with less terms have a higher
score
Rationale: a term in a field with less terms is more important than one
with more
numTerms = amount of terms in a field
boost (index) = boost of the field at index-time
Implication: hits in fields with higher boost get a higher score
Rationale: a term in field A could be more relevant than the same term
in field B
maxDocs = the number of documents in the index, including those that are
marked as deleted but have not yet been purged. This is a constant (the same
value for all documents in the index)
Implication: (probably) doesn't play a role in the scoring calculation
coord = number of terms in the query that were found in the document
(omitted if equal to 1)
implementation: overlap/maxOverlap
Implication: of the terms in the query, a document that contains more
terms will have a higher score
Rationale: documents that match the most optional terms score highest
overlap = the number of query terms matched in the document
maxOverlap = the total number of terms in the query
FunctionQuery = could be any kind of custom ranking function, which outcome
is added to, or multiplied with the default rank score.
Implication: various
Look at the EXPLAIN information to see how the final score is calculated.
Tom
-----Original Message-----
From: Sangeetha [mailto:sangeetha...@gmail.com]
Sent: Thursday 13 December 2012 08:33
To: solr-user@lucene.apache.org
Subject: score calculation
I want to know how score is calculated?
what is fieldweight, fieldNorm, queryWeight and queryNorm. And what is the
formula to get the final score using fieldweight, fieldNorm, queryWeight
,queryNorm, idf and tf.
Can anyone explain or provide some links?
Thanks,
Sangeetha
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