GitHub user mnilsson23 opened a pull request:
https://github.com/apache/lucene-solr/pull/40
SOLR-8542: Integrate Learning to Rank into Solr
Solr Learning to Rank (LTR) provides a way for you to extract features
directly inside Solr for use in training a machine learned model. You
can then deploy that model to Solr and use it to rerank your top X
search results. This concept was previously presented by the authors at
Lucene/Solr Revolution 2015.
See the
[README](https://github.com/bloomberg/lucene-solr/tree/master-ltr-plugin-release/solr/contrib/ltr)
for more information on how to get started.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/bloomberg/lucene-solr
master-ltr-plugin-release
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/lucene-solr/pull/40.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #40
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commit 073de9b2719abe91e106119b23b977e521e8b32f
Author: Diego Ceccarelli <dceccarel...@bloomberg.net>
Date: 2016-01-13T22:29:17Z
SOLR-8542: Integrate Learning to Rank into Solr
Solr Learning to Rank (LTR) provides a way for you to extract features
directly inside Solr for use in training a machine learned model. You
can then deploy that model to Solr and use it to rerank your top X
search results. This concept was previously presented by the authors at
Lucene/Solr Revolution 2015
commit b2bbe8c13122280ee5a76149bfb55fd1b7324279
Author: Michael Nilsson <mnilsso...@bloomberg.net>
Date: 2016-05-25T22:13:05Z
Learning to Rank plugin updates
- Updated our documentation about the training phase and how to train a
real model for those that are not familiar with this process. We provided a
step by step example building a rankSVM model externally, and supplied a sample
script which does this using liblinear.
- Formatted the code based on the lucene eclipse style
- Updated the hashCode and equals functions of the ModelQuery as
[~Alessandro.Benedetti] pointed out
- Renamed ModelMetadata, the class you would subclass to add a new model
for scoring docs, to LTRScoringAlgorithm
- Cleaned up the LTRScoringAlgorithm to no longer have a type parameter
- Added IntelliJ support. Thank you [~Alessandro.Benedetti] for adding it
- Renamed mstore and fstore endpoints to feature-store and model-store as
per [~Upayavira]'s suggestion
- Added support for default efi parameters using the same Solr standard in
solrconfig. When defining a feature in the config, put $\{isFromManchester:0\}
to get 0 as a default, and you won't have to specify it in the request's efi
params. Thanks for the enhancement suggestion [~Alessandro.Benedetti]
- Removed the fv=true param requirement for extracting features.
- You do not have to provide a "dummy model" first for extracting features,
so you can request the transformer without the need of an rq ranking query.
Inside the transformer you can provide a store=myFeatureStore param, and it
will extract all features from that feature store directly. You can also
provide local efi params if needed when extracting without an rq.
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