I think it really depends on the particular use case. Sometime the absolute score is a good feature, sometimes no.
If you are using the default bm25, I think that increasing the number of terms in the query will increase the average doc. score in the results. So maybe I would normalize the score at least considering the number of terms in the query. Using the rank has been proposed in academia [1] and it seems to improve quality of the results. [1] http://hpc.isti.cnr.it/~claudio/web/archives/20150416b/index.html From: solr-user@lucene.apache.org At: 01/29/18 14:19:41To: solr-user@lucene.apache.org Subject: Re: LTR original score feature >It seems to me that the original score feature is not useful because it is not normalized across all queries and therefore cannot be used to compare relevance in different queries. I don't agree with this statement and it's not what Alessandro was suggesting ("When you put the original score together with the rest of features, it may be of potential usage."). The magnitude of the score could very well contain useful information in certain contexts. The simplest way to determine whether or not the score is useful is to just train and test the model with and without the feature included and see which one performs better. On Thu, Jan 25, 2018 at 3:41 PM, Brian Yee <b...@wayfair.com> wrote: > Thanks for the reply Alessandro. I'm starting to agree with you but I > wanted to see if others agree. It seems to me that the original score > feature is not useful because it is not normalized across all queries and > therefore cannot be used to compare relevance in different queries. > > -----Original Message----- > From: alessandro.benedetti [mailto:a.benede...@sease.io] > Sent: Wednesday, January 24, 2018 10:22 AM > To: solr-user@lucene.apache.org > Subject: Re: LTR original score feature > > This is actually an interesting point. > The original Solr score alone will mean nothing, the ranking position of > the document would be a more relevant feature at that stage. > > When you put the original score together with the rest of features, it may > be of potential usage ( number of query terms, tf for a specific field, idf > for another field ...). > Also because some training algorithms will group the training samples by > query. > > personally I start to believe it would be better to decompose the original > score into finer grain features and then rely on LTR to weight them ( as > the original score is effectively already mixing up finer grain features > following a standard formula). > > > > > > ----- > --------------- > Alessandro Benedetti > Search Consultant, R&D Software Engineer, Director Sease Ltd. - > www.sease.io > -- > Sent from: http://lucene.472066.n3.nabble.com/Solr-User-f472068.html >
