How to train the model using user clicks when use Solr ltr(learning to rank) module

Question

In https://issues.apache.org/jira/browse/SOLR-8542, Solr integrates learning-to-rank function.

I tried to integrate it into our product. But I am having difficult figuring out how to translate the partial pairwise feedback to the importance or relevance of that doc. 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

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 yet.

Could anyone please give detailed instruction and sample code about how to translate the partial pairwise feedback, five it a score and use it to train and update model?

Thanks a lot.

Answer 1

I opened https://issues.apache.org/jira/browse/SOLR-9929, and they added more doc at https://github.com/bloomberg/lucene-solr/blob/master-ltr/solr/contrib/ltr/example/README.md