So Prateek : "You're right it doesn't have to be that accurate to the query time but our requirement is having a more solid control over our outputs from Solr like if we have 4 features then we can adjust the weights giving something like (40,20,20,20) to each feature such that the sum total of features for a document is 100 this is only possible if we could scale the feature outputs between 0-1." You are talking about weights so I assume you are using a linear Learning To Rank model. Which library are you using to train your model? Is this library allowing you to limit the summation of the linear weights and normalise the training set per feature ?
At query time LTR will just apply the model weights to the query time feature vector. It makes sense to normalise each query time feature using the training time values. They should be close enough to the training set values ( if not the model is going to perform poor anyway and you need to curate a little bit better the training phase). Remember the model is used to give an order to the results, not to make an accurate regression prediction. "Secondly, I also have a doubt regarding the scaling function like why it is not considering only the documents filtered out by the FQ filter and considering all the documents which match the query." At the moment I would not focus on that scenario, I am not very convinced LTR SolrFeature is compatible to that complex function query, and I am not very convinced is going to be performance friendly anyway. i would need to investigate that properly. Regards ----- --------------- 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
