Package org.apache.solr.ltr
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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ClassDescriptionA feature logger that logs in csv format.FeatureLogger can be registered in a model and provide a strategy for logging the feature values.Implements the rescoring logic.The ranking query that is run, reranking results using the LTRScoringModel algorithmThe LTRThreadModule is optionally used by the
LTRQParserPluginandLTRFeatureLoggerTransformerFactoryclasses to parallelize the creation ofFeature.FeatureWeightobjects.