Thanks, Alessandro, for your reply. Indeed, LTR looks like what I need.
However, all of the LRT examples that I have found use a single collection as a data source. My data spans across two collections. Does LTR support this somehow or should I 'denormalise' the data and merge both collections? My concern is that the denormalisation will lead to a significant increase in size on the drive. Best, Gintas On Tue, Jan 30, 2018 at 2:30 PM, Alessandro Benedetti <a.benede...@sease.io> wrote: > Hi Ginsul, > let's try to wrap it up : > > 1) you have an item win N binary features ( given the fact that you > represent the document with a list of feature Ids ( and no values) I would > assume that it means that when the feature is in the list, it has a value > of > 1 for the item > > 2) you want to score (or maybe re-rank ? ) your documents giving the score > you defined > > You could solve this problem with a number of possible customizations. > Starting from an easy one, you could try to use the LTR re-ranker[1] . > > Specifically you can define your set of feature( and that should be > possible > using the component out of the box) and then a linear model ( and you > already have the weights for the features so you don't need to train it). > > This can be close to what you want but you may want to customize a bit ( > given the fact that you may want to average the weight). > For example you could define an extension of the linear model that does the > average of the score ect ect... > > > [1] https://lucene.apache.org/solr/guide/6_6/learning-to-rank.html > > > > > ----- > --------------- > 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 >
