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.
