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https://issues.apache.org/jira/browse/SOLR-8183?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ajinkya Kale updated SOLR-8183:
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Description:
Add a Learning to Rank or Machine Learned Ranking framework to Solr. The
framework should be able to
1. Generate user-result-click tracking logs
2. Learning/Training component which is based on something like Ranklib
(http://sourceforge.net/p/lemur/wiki/RankLib/)
3. Push the model learnt in step 2 to Solr through the existing rerank framework
I have a very crude experimental code for step 2 and 3 working (thanks to the
help from Joel Bernstein).
For step 1 I was wondering if Solr has an open source version of
https://docs.lucidworks.com/display/lweug/Click+Scoring+Relevance+Framework
If there existing something like this then it wont be hard to close the loop
and have an nice feedback loop implementation on LTR framework.
I feel there should be a big push on the overall algorithmic front which will
help LTR framework.
Something like the Fair Pairs algorithm switch in Solr to turn on/off to
generate unbiased clicked data will be pretty hand for optimizing the ranking
through LTR.
http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36363.pdf
http://www.cs.cornell.edu/people/tj/publications/radlinski_joachims_06a.pdf
Also better support for experimenting with multi-arm bandits in search rankings
will be pretty handy too! https://www.cs.cornell.edu/~rdk/papers/icml08.pdf
This calls for a better architecture to
1. Collect user behavior data
2. Learn a ML-model based on User data
3. Experiment / collect unbiased datasets from deployed models
was:
Add a Learning to Rank or Machine Learned Ranking framework to Solr. The
framework should be able to
1. Generate user-result-click tracking logs
2. Learning/Training component which is based on something like Ranklib
(http://sourceforge.net/p/lemur/wiki/RankLib/)
3. Push the model learnt in step 2 to Solr through the existing rerank framework
I have a very crude experimental code for step 2 and 3 working (thanks to the
help from Joel Bernstein).
For step 1 I was wondering if Solr has an open source version of
https://docs.lucidworks.com/display/lweug/Click+Scoring+Relevance+Framework
If there existing something like this then it wont be hard to close the loop
and have an nice feedback loop implementation on LTR framework.
> Add Learning to Rank framework to Solr
> --------------------------------------
>
> Key: SOLR-8183
> URL: https://issues.apache.org/jira/browse/SOLR-8183
> Project: Solr
> Issue Type: New Feature
> Reporter: Ajinkya Kale
>
> Add a Learning to Rank or Machine Learned Ranking framework to Solr. The
> framework should be able to
> 1. Generate user-result-click tracking logs
> 2. Learning/Training component which is based on something like Ranklib
> (http://sourceforge.net/p/lemur/wiki/RankLib/)
> 3. Push the model learnt in step 2 to Solr through the existing rerank
> framework
> I have a very crude experimental code for step 2 and 3 working (thanks to the
> help from Joel Bernstein).
> For step 1 I was wondering if Solr has an open source version of
> https://docs.lucidworks.com/display/lweug/Click+Scoring+Relevance+Framework
> If there existing something like this then it wont be hard to close the loop
> and have an nice feedback loop implementation on LTR framework.
> I feel there should be a big push on the overall algorithmic front which will
> help LTR framework.
> Something like the Fair Pairs algorithm switch in Solr to turn on/off to
> generate unbiased clicked data will be pretty hand for optimizing the ranking
> through LTR.
> http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36363.pdf
> http://www.cs.cornell.edu/people/tj/publications/radlinski_joachims_06a.pdf
> Also better support for experimenting with multi-arm bandits in search
> rankings will be pretty handy too!
> https://www.cs.cornell.edu/~rdk/papers/icml08.pdf
> This calls for a better architecture to
> 1. Collect user behavior data
> 2. Learn a ML-model based on User data
> 3. Experiment / collect unbiased datasets from deployed models
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