Package org.apache.solr.ltr
This package contains the main logic for performing the reranking using a Learning to Rank model.
A model will be applied on each document through a LTRScoringQuery, a
subclass of Query. As a normal query,
the learned model will produce a new score
for each document reranked.
A LTRScoringQuery is created by providing an instance of
LTRScoringModel. An instance of
LTRScoringModel
defines how to combine the features in order to create a new
score for a document. A new Learning to Rank model is plugged
into the framework by extending LTRScoringModel,
(see for example MultipleAdditiveTreesModel and LinearModel).
The LTRScoringQuery will take care of computing the values of
all the features (see Feature) and then will delegate the final score
generation to the LTRScoringModel, by calling the method
score(float[] modelFeatureValuesNormalized).
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Class Summary Class Description CSVFeatureLogger A feature logger that logs in csv format.DocInfo FeatureLogger FeatureLogger can be registered in a model and provide a strategy for logging the feature values.LTRRescorer Implements the rescoring logic.LTRScoringQuery The ranking query that is run, reranking results using the LTRScoringModel algorithmLTRScoringQuery.FeatureInfo LTRThreadModule The LTRThreadModule is optionally used by theLTRQParserPluginandLTRFeatureLoggerTransformerFactoryclasses to parallelize the creation ofFeature.FeatureWeightobjects.SolrQueryRequestContextUtils -
Enum Summary Enum Description FeatureLogger.FeatureFormat