Hi,
NLP4L[1] has not only Learning-to-Rank module but also a module which calculates
click model and converts it into pointwise annotation data.
NLP4L has a comprehensive manual[2], but you may want to read "Click Log
Analysis"
section[3] first to see if it suits your requirements.
Hope this helps. Thanks!
Koji
--
T: @kojisays
[1] https://github.com/NLP4L/nlp4l
[2] https://github.com/NLP4L/manuals
[3] https://github.com/NLP4L/manuals/blob/master/ltr/ltr_import.md
On 2017/01/05 17:02, Jeffery Yuan wrote:
Thanks very much for integrating machine learning to Solr.
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 how to translate the partial pairwise feedback to the
importance or relevance of that doc.
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.
It would be great Solr can provide some detailed instruction or sample code
about how to translate the partial pairwise feedback and use it to train and
update model.
Thanks again for your help.
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