Thanks very much for integrating machine learning to Solr. https://github.com/apache/lucene-solr/blob/f62874e47a0c790b9e396f58ef6f14ea04e2280b/solr/contrib/ltr/README.md
In the Assemble training data part: the third column indicates the relative importance or relevance of that doc Could you please give more info about how to give a score based on what user clicks? I have read https://static.aminer.org/pdf/PDF/000/472/865/optimizing_search_engines_using_clickthrough_data.pdf http://www.cs.cornell.edu/people/tj/publications/joachims_etal_05a.pdf http://alexbenedetti.blogspot.com/2016/07/solr-is-learning-to-rank-better-part-1.html But still have no clue how to translate the partial pairwise feedback to the importance or relevance of that doc. >From a user's perspective, the steps such as setup the feature and model in Solr is simple, but collecting the feedback data and train/update the model is much more complex. It would be great Solr can provide some detailed instruction or sample code about how to translate the partial pairwise feedback and use it to train and update model. Thanks again for your help. -- View this message in context: http://lucene.472066.n3.nabble.com/How-to-train-the-model-using-user-clicks-when-use-ltr-learning-to-rank-module-tp4312462.html Sent from the Solr - User mailing list archive at Nabble.com.