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https://issues.apache.org/jira/browse/SOLR-9929?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Christine Poerschke updated SOLR-9929:
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Fix Version/s: 6.4
master (7.0)
> Documentation and sample 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
> Assignee: Christine Poerschke
> Labels: learning-to-rank, machine_learning, solr
> Fix For: master (7.0), 6.4
>
>
> 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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