Hi Steve, Thank you for your suggestions. Payloads might indeed help me to overcome the precision loss problem that I am experiencing right now. I don't think it will help me with the combining of Lucene scores with external scores however.
Is there anyone who has a suggestion how to deal with that? Dennis On Thu, 2010-01-28 at 13:52 -0500, Steven A Rowe wrote: > Hi Dennis, > > You should check out payloads (arbitrary per-index-term byte[] arrays), which > can be used to encode values which are then incorporated into documents' > scores, by overriding Similarity.scorePayload(): > > <http://lucene.apache.org/java/3_0_0/api/core/org/apache/lucene/search/Similarity.html#scorePayload%28int,%20java.lang.String,%20int,%20int,%20byte[],%20int,%20int%29> > > The Lucene in Action 2 MEAP has a nice introduction to using payloads to > influence scoring, in section 6.5. > > See also this (slightly out-of-date*) blog post "Getting Started with > Payloads" by Grant Ingersoll at Lucid Imagination: > > <http://www.lucidimagination.com/blog/2009/08/05/getting-started-with-payloads/> > > *Note that since this blog post was written, BoostingTermQuery was renamed to > PayloadTermQuery (in Lucene 2.9.0+ ; see > http://issues.apache.org/jira/browse/LUCENE-1827 ; wow - this issue isn't > mentioned in CHANGES.txt???): > > <http://lucene.apache.org/java/3_0_0/api/core/org/apache/lucene/search/payloads/PayloadTermQuery.html> > > Steve > > On 01/28/2010 at 6:01 AM, Dennis Hendriksen wrote: > > I'm struggling to create a performant query in Lucene 3.0.0 in which I > > want to combine 'regular' scoring with scores derived from external > > sources. > > > > For each document a fixed set of scores is calculated in the range [0.0, > > 1.0>. These scores represent the confidences that a document falls into > > categories. So for example document #1 has a score of 0.3 for cat=boys, > > 0.2 for cat=girls, 0.1 for cat=toys, 0.05 for cat=animals. > > > > The 'regular' scoring is calculated using a BooleanQuery with TermQuerys > > similar to: -type:H +(title:dna body:dna^1.5) > > > > In the current naive approach I'm combining the scores as following: - > > for each document store the three best categories in the following > > fields: > > name=cat1st value=boys fieldboost=0.3 > > name=cat2nd value=girls fieldboost=0.2 > > name=cat3rd value=toys fieldboost=0.1 > > Search-time use the following query if you're interested in 'girls': > > -type:H +(title:dna body:dna^1.5) cat1st:girls cat2nd:girls cat3rd:girls > > or if you're interested in 'boys': > > -type:H +(title:dna body:dna^1.5) cat1st:boys cat2nd:boys cat3rd:boys > > > > Disadvantages of the current approach: > > - loss of precision encoding/decoding boosts (performance is important, > > so this might be acceptable) > > - using TermQuery for the cat fields doesn't make a lot of sense since > > the external scores are multiplied by the idf of 'boys'/'girls' and > > the querynorm > > - the resulting score from the cat field is added to the other query > > score instead of multiplied > > > > Just to give you an idea: the index I'm using is growing in time and > > contains about 50 million documents > > > > Do you have an idea how I can improve my query and still keep high > > performance? Or should I combine the scores in the Collector (but this > > doesn't seem the right place to retrieve the category scores from the > > index)? Is it possible to use a different float->byte encoder per field > > to reduce the lack of precision? > > > > Thanks for your time, > > Dennis > > --------------------------------------------------------------------- To unsubscribe, e-mail: java-user-unsubscr...@lucene.apache.org For additional commands, e-mail: java-user-h...@lucene.apache.org
