Hi Alessandro, "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? "
Yes, we're planning to use Linear LTR model and we're not using any library for this. Basically, we currently have an idea like what feature is more important to us than others so we will be adjusting the weights accordingly like (40,20,20,20) here the first feature is most important to us currently. "At the moment I would not focus on that scenario, I am not very convinced LTR SolrFeature is compatible with that complex function query, and I am not very convinced is going to be performance friendly anyway. I would need to investigate that properly. " You're completely right it is not going to be performance friendly anyway but I'm pretty sure that payload_score calculation considers all the documents which match the query not the small subset of documents which resulted from FQ_filters. I have even verified it using the debug=True like: The result found after applying that FQ_filter were 365 but below in the debugging part the docfreq used in the payload_score calculation was 3360(which is the total no. of documents that match the query). It was hard for me to get my head around that code maybe you can help. And it was worth a shot I think. Thanks Again. Regards, Prateek -- Sent from: http://lucene.472066.n3.nabble.com/Solr-User-f472068.html
