Package org.apache.solr.ltr

This package contains the main logic for performing the reranking using a Learning to Rank model.

See: Description

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 algorithm
LTRScoringQuery.FeatureInfo
LTRThreadModule	The LTRThreadModule is optionally used by the LTRQParserPlugin and LTRFeatureLoggerTransformerFactory classes to parallelize the creation of Feature.FeatureWeight objects.
SolrQueryRequestContextUtils

Enum Summary

Enum	Description
FeatureLogger.FeatureFormat

Package org.apache.solr.ltr Description

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).