All Classes and Interfaces
Class
Description
A scoring model whose initialization is completed via its
AdapterModel.init(SolrResourceLoader)
method.A feature logger that logs in csv format.
A scoring model that fetches the wrapped model from
SolrResourceLoader.A recipe for computing a feature.
A 'recipe' for computing a feature
A
FeatureScorer that contains a Scorer, which it delegates to where
appropriate.Default FeatureScorer class that returns the score passed in.
The class used to extract features for LTR feature logging.
FeatureLogger can be registered in a model and provide a strategy for logging the feature values.
This class is responsible for extracting features and using them to score the document.
This feature returns the length of a field (in terms) for the current document.
This feature returns the value of a field in the current document.
A Normalizer that normalizes a feature value to itself.
Interleaving considers two ranking models: modelA and modelB.
For a given query, each model returns its ranked list of documents La = (a1,a2,...) and Lb = (b1, b2, ...).
An Interleaving algorithm creates a unique ranked list I = (i1, i2, ...).
This list is created by interleaving elements from the two lists la and lb as described by the implementation algorithm.
Each element Ij is labelled TeamA if it is selected from La and TeamB if it is selected from Lb.
For a given query, each model returns its ranked list of documents La = (a1,a2,...) and Lb = (b1, b2, ...).
An Interleaving algorithm creates a unique ranked list I = (i1, i2, ...).
This list is created by interleaving elements from the two lists la and lb as described by the implementation algorithm.
Each element Ij is labelled TeamA if it is selected from La and TeamB if it is selected from Lb.
Deprecated.
A scoring model that computes scores using a dot product.
This transformer will take care to generate and append in the response the features declared in
the feature store of the current reranking model, or a specified feature store.
A learning to rank Query with Interleaving, will incapsulate two models, and delegate to it the
rescoring of the documents.
Implements the rescoring logic.
Plug into solr a rerank model.
A learning to rank Query, will incapsulate a learning to rank model, and delegate to it the
rescoring of the documents.
Implements the rescoring logic.
A scoring model computes scores that can be used to rerank documents.
The ranking query that is run, reranking results using the LTRScoringModel algorithm
The LTRThreadModule is optionally used by the
LTRQParserPlugin
and LTRFeatureLoggerTransformerFactory classes to parallelize the creation of Feature.FeatureWeight objects.Managed resource for a storing a feature.
Menaged resource for storing a model
A Normalizer to scale a feature value using a (min,max) range.
Contains the model and features declared.
The class used to extract more than one feature for LTR feature logging.
This class is responsible for extracting more than one feature and using them to score the
document.
A scoring model that computes scores based on the summation of multiple weighted trees.
A scoring model that computes document scores using a neural network.
A normalizer normalizes the value of a feature.
This feature returns the original score that the document had before performing the reranking.
The class used to extract a single feature for LTR feature logging.
This class is responsible for extracting a single feature and using it to score the document.
This feature allows you to reuse any Solr query as a feature.
Scorer for a SolrFeature
A Normalizer to scale a feature value around an average-and-standard-deviation distribution.
Interleaving was introduced the first time by Joachims in [1, 2].
Team Draft Interleaving is among the most successful and used interleaving approaches[3].
Team Draft Interleaving implements a method similar to the way in which captains select their players in team-matches.
Team Draft Interleaving produces a fair distribution of ranking models’ elements in the final interleaved list.
"Team draft interleaving" has also proved to overcome an issue of the "Balanced interleaving" approach, in determining the winning model[4].
Team Draft Interleaving is among the most successful and used interleaving approaches[3].
Team Draft Interleaving implements a method similar to the way in which captains select their players in team-matches.
Team Draft Interleaving produces a fair distribution of ranking models’ elements in the final interleaved list.
"Team draft interleaving" has also proved to overcome an issue of the "Balanced interleaving" approach, in determining the winning model[4].
This feature allows to return a constant given value for the current document.
A scoring model that wraps the other model.