[jira] [Resolved] (SOLR-9929) Documentation and sample code about how to train the model using user clicks when use ltr module

["Christine Poerschke (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SOLR-9929?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Christine Poerschke resolved SOLR-9929.
---------------------------------------
    Resolution: Fixed

Thanks [~yuanyun.cn] and [~diegoceccarelli]!

> 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: Task
>      Security Level: Public(Default Security Level. Issues are Public) 
>            Reporter: jefferyyuan
>            Assignee: Christine Poerschke
>            Priority: Minor
>              Labels: learning-to-rank, machine_learning, solr
>             Fix For: master (7.0), 6.4
>
>         Attachments: 0001-Improve-Learning-to-Rank-example-Readme.patch
>
>
> 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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