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
Hi Jeffery,
Just noticed your comment to my blog, I will try to respond asap.
Related your doubt, I second Diego's readme.
If you have other user signals as well ( apart from clicks) it may be
interesting to use them as well.
Users signals such as : "Add To Favorites" , "Add to the basket" ,
ah. very nice Diego. Thanks.
On 1/6/2017 1:52 PM, Diego Ceccarelli (BLOOMBERG/ LONDON) wrote:
Hi Jeffery,
I submitted a patch to the README of the learning to rank example folder,
trying to explain better how to produce a training set given a log with
interaction data.
Patch is available
Hi Jeffery,
I submitted a patch to the README of the learning to rank example folder,
trying to explain better how to produce a training set given a log with
interaction data.
Patch is available here: https://issues.apache.org/jira/browse/SOLR-9929
And you can see the new version of the
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?
Hi Jeffery,
Give your questions more detail and there may be more feedback; just a
suggestion.
Thanks , Will Martin.
I checked the pdf it's great. but seems not very useful for my question: How
to train the model using user clicks when use ltr(learning to rank) module.
I know the concept after reading these papers. But still not sure how to
code them.
--
View this message in context:
http://www.dcc.fc.up.pt/~pribeiro/aulas/na1516/slides/na1516-slides-ir.pdf
see the relevant sections for good info
On 1/5/2017 3:02 AM, Jeffery Yuan wrote:
> Thanks very much for integrating machine learning to Solr.
>
