http://www.dcc.fc.up.pt/~pribeiro/aulas/na1516/slides/na1516-slides-ir.pdf
see the relevant sections for good info.... On 1/5/2017 3:02 AM, Jeffery Yuan wrote: > 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.
