Documentation and sample code about how to train the model using user clicks when use ltr module

Thanks very much for integrating machine learning to Solr.
https://issues.apache.org/jira/browse/SOLR-8542

I tried to integrate it. But have 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
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 yet.

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. Without it, we can't really use the learning-to-rank function in Solr.

It would be great if Solr can provide some detailed instruction and sample code about how to translate the partial pairwise feedback and use it to train and update model.

Thanks