[jira] [Updated] (SOLR-9929) Documentation and sample code about how to train the model using user clicks when use ltr module
[ https://issues.apache.org/jira/browse/SOLR-9929?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Christine Poerschke updated SOLR-9929: -- Priority: Minor (was: Major) > 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org
[jira] [Updated] (SOLR-9929) Documentation and sample code about how to train the model using user clicks when use ltr module
[ https://issues.apache.org/jira/browse/SOLR-9929?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Christine Poerschke updated SOLR-9929: -- Issue Type: Task (was: Improvement) > 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 > 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org
[jira] [Updated] (SOLR-9929) Documentation and sample code about how to train the model using user clicks when use ltr module
[ https://issues.apache.org/jira/browse/SOLR-9929?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Diego Ceccarelli updated SOLR-9929: --- Attachment: 0001-Improve-Learning-to-Rank-example-Readme.patch Improve Learning to Rank example readme > 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 > > 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org
[jira] [Updated] (SOLR-9929) Documentation and sample code about how to train the model using user clicks when use ltr module
[ https://issues.apache.org/jira/browse/SOLR-9929?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Christine Poerschke updated SOLR-9929: -- 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org
[jira] [Updated] (SOLR-9929) Documentation and sample code about how to train the model using user clicks when use ltr module
[ https://issues.apache.org/jira/browse/SOLR-9929?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] jefferyyuan updated SOLR-9929: -- Summary: Documentation and sample code about how to train the model using user clicks when use ltr module (was: Documentation and smaple code about how to train the model using user clicks when use ltr module) > 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 > Labels: learning-to-rank, machine_learning, solr > > 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: dev-unsubscr...@lucene.apache.org For additional commands, e-mail: dev-h...@lucene.apache.org
