Thanks for your help Alessandro! Ryan
On Wed, 21 Jun 2017 at 19:25 alessandro.benedetti <a.benede...@sease.io> wrote: > Hi Ryan, > first thing to know is that Learning To Rank is about relevancy and > specifically it is about to improve your relevancy function. > Deciding if to use or not LTR has nothing to do with your index size or > update frequency ( although LTR brings some performance consideration you > will need to evaluate) . > > Functionally, the moment you realize you want LTR is when you start tuning > your relevancy. > Normally the first approach is the manual one, you identify a set of > features, interesting for your use case and you tune a boosting function to > improve your search experience. > > e.g. > you decide to weight more the title field than the content and then > boosting > recent documents. > > What happens next is : > "How much should I weight more the title ?" > "How much should I boost recent documents ?" > > Normally you just check some golden queries and you try to manually > optimise > these boosting factors by hand. > > LTR answers to this requirements. > To make it simple LTR will bring you a model that will tell you the best > weighting factors given your domain ( and past experience) to get the most > relevant results for all the queries ( this is the ideal, of course it is > quite complicated and it depends of a lot of factors) > > Of course it doesn't work like magic and you will need to extensively > design > your features ( features engineering), build a valid training set ( > explicit > or implicit), decide the model that best suites your needs ( linear model > or > Tree based ?) and a lot of corollary configurations. > > hope this helps! > > > > > > ----- > --------------- > Alessandro Benedetti > Search Consultant, R&D Software Engineer, Director > Sease Ltd. - www.sease.io > -- > View this message in context: > http://lucene.472066.n3.nabble.com/When-to-use-LTR-tp4342130p4342140.html > Sent from the Solr - User mailing list archive at Nabble.com. >
