jefferyyuan created SOLR-9929:
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Summary: Documentation and smaple code about how to train the
model using user clicks when use ltr module
Key: SOLR-9929
URL: https://issues.apache.org/jira/browse/SOLR-9929
Project: Solr
Issue Type: Improvement
Security Level: Public (Default Security Level. Issues are Public)
Reporter: jefferyyuan
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
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