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
----
commit 073de9b2719abe91e106119b23b977e521e8b32f
Author: Diego Ceccarelli <[email protected]>
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 <[email protected]>
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.
----
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
