solr/contrib/ltr/src/java/overview.html - lucene-solr - Git at Google

 <!--
  Licensed to the Apache Software Foundation (ASF) under one or more
  contributor license agreements.  See the NOTICE file distributed with
  this work for additional information regarding copyright ownership.
  The ASF licenses this file to You under the Apache License, Version 2.0
  (the "License"); you may not use this file except in compliance with
  the License.  You may obtain a copy of the License at

      http://www.apache.org/licenses/LICENSE-2.0

  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License.
 -->
 <html>
 <body>
 Apache Solr Search Server: Learning to Rank Contrib

 <p>
 This module contains a logic <strong>to plug machine learned ranking modules into Solr</strong>.
 </p>
 <p>
 In information retrieval systems, Learning to Rank is used to re-rank the top X
 retrieved documents using trained machine learning models. The hope is
 that sophisticated models can make more nuanced ranking decisions than standard ranking
 functions like TF-IDF or BM25.
 </p>
 <p>
 This module allows to plug a reranking model directly into Solr, enabling users
 to easily build their own learning to rank systems and access the rich
 matching features readily available in Solr. It also provides tools to perform
 feature engineering and feature extraction.
 </p>
 <h2> Code structure </h2>
 <p>
 A Learning to Rank model is plugged into the ranking through the {@link org.apache.solr.ltr.search.LTRQParserPlugin},
 a {@link org.apache.solr.search.QParserPlugin}. The plugin will
 read from the request the model (instance of {@link org.apache.solr.ltr.model.LTRScoringModel})
 used to perform the request plus other
 parameters. The plugin will generate a {@link org.apache.solr.ltr.search.LTRQuery LTRQuery}:
 a particular {@link org.apache.solr.search.RankQuery}
 that will encapsulate the given model and use it to
 rescore and rerank the document (by using an {@link org.apache.solr.ltr.LTRRescorer}).
 </p>
 <p>
 A model will be applied on each document through a {@link org.apache.solr.ltr.LTRScoringQuery}, a
 subclass of {@link org.apache.lucene.search.Query}. As a normal query,
 the learned model will produce a new score
 for each document reranked.
 </p>
 <p>
 A {@link org.apache.solr.ltr.LTRScoringQuery} is created by providing an instance of
 {@link org.apache.solr.ltr.model.LTRScoringModel}. An instance of
 {@link org.apache.solr.ltr.model.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 {@link org.apache.solr.ltr.model.LTRScoringModel},
 (see for example {@link org.apache.solr.ltr.model.MultipleAdditiveTreesModel} and {@link org.apache.solr.ltr.model.LinearModel}).
 </p>
 <p>
 The {@link org.apache.solr.ltr.LTRScoringQuery} will take care of computing the values of
 all the features (see {@link org.apache.solr.ltr.feature.Feature}) and then will delegate the final score
 generation to the {@link org.apache.solr.ltr.model.LTRScoringModel}, by calling the method
 {@link org.apache.solr.ltr.model.LTRScoringModel#score(float[] modelFeatureValuesNormalized)}.
 </p>
 <p>
 A {@link org.apache.solr.ltr.feature.Feature} will produce a particular value for each document, so
 it is modeled as a {@link org.apache.lucene.search.Query}. The package
 {@link org.apache.solr.ltr.feature} contains several examples
 of features. One benefit of extending the Query object is that we can reuse
 Query as a feature, see for example {@link org.apache.solr.ltr.feature.SolrFeature}.
 Features for a document can also be returned in the response by
 using the FeatureTransformer (a {@link org.apache.solr.response.transform.DocTransformer DocTransformer})
 provided by {@link org.apache.solr.ltr.response.transform.LTRFeatureLoggerTransformerFactory}.
 </p>
 <p>
 {@link org.apache.solr.ltr.store} contains all the logic to store all the features and the models.
 Models are registered into a unique {@link org.apache.solr.ltr.store.ModelStore ModelStore},
 and each model specifies a particular {@link org.apache.solr.ltr.store.FeatureStore FeatureStore} that
 will contain a particular subset of features.
 <p>
 </p>
 Features and models can be managed through a REST API, provided by the
 {@link org.apache.solr.rest.ManagedResource Managed Resources}
 {@link org.apache.solr.ltr.store.rest.ManagedFeatureStore ManagedFeatureStore}
 and {@link org.apache.solr.ltr.store.rest.ManagedModelStore ManagedModelStore}.
 </p>
 </body>
 </html>
	<!--
	Licensed to the Apache Software Foundation (ASF) under one or more
	contributor license agreements. See the NOTICE file distributed with
	this work for additional information regarding copyright ownership.
	The ASF licenses this file to You under the Apache License, Version 2.0
	(the "License"); you may not use this file except in compliance with
	the License. You may obtain a copy of the License at

	http://www.apache.org/licenses/LICENSE-2.0

	Unless required by applicable law or agreed to in writing, software
	distributed under the License is distributed on an "AS IS" BASIS,
	WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	See the License for the specific language governing permissions and
	limitations under the License.
	-->
	<html>
	<body>
	Apache Solr Search Server: Learning to Rank Contrib

	<p>
	This module contains a logic <strong>to plug machine learned ranking modules into Solr</strong>.
	</p>
	<p>
	In information retrieval systems, Learning to Rank is used to re-rank the top X
	retrieved documents using trained machine learning models. The hope is
	that sophisticated models can make more nuanced ranking decisions than standard ranking
	functions like TF-IDF or BM25.
	</p>
	<p>
	This module allows to plug a reranking model directly into Solr, enabling users
	to easily build their own learning to rank systems and access the rich
	matching features readily available in Solr. It also provides tools to perform
	feature engineering and feature extraction.
	</p>
	<h2> Code structure </h2>
	<p>
	A Learning to Rank model is plugged into the ranking through the {@link org.apache.solr.ltr.search.LTRQParserPlugin},
	a {@link org.apache.solr.search.QParserPlugin}. The plugin will
	read from the request the model (instance of {@link org.apache.solr.ltr.model.LTRScoringModel})
	used to perform the request plus other
	parameters. The plugin will generate a {@link org.apache.solr.ltr.search.LTRQuery LTRQuery}:
	a particular {@link org.apache.solr.search.RankQuery}
	that will encapsulate the given model and use it to
	rescore and rerank the document (by using an {@link org.apache.solr.ltr.LTRRescorer}).
	</p>
	<p>
	A model will be applied on each document through a {@link org.apache.solr.ltr.LTRScoringQuery}, a
	subclass of {@link org.apache.lucene.search.Query}. As a normal query,
	the learned model will produce a new score
	for each document reranked.
	</p>
	<p>
	A {@link org.apache.solr.ltr.LTRScoringQuery} is created by providing an instance of
	{@link org.apache.solr.ltr.model.LTRScoringModel}. An instance of
	{@link org.apache.solr.ltr.model.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 {@link org.apache.solr.ltr.model.LTRScoringModel},
	(see for example {@link org.apache.solr.ltr.model.MultipleAdditiveTreesModel} and {@link org.apache.solr.ltr.model.LinearModel}).
	</p>
	<p>
	The {@link org.apache.solr.ltr.LTRScoringQuery} will take care of computing the values of
	all the features (see {@link org.apache.solr.ltr.feature.Feature}) and then will delegate the final score
	generation to the {@link org.apache.solr.ltr.model.LTRScoringModel}, by calling the method
	{@link org.apache.solr.ltr.model.LTRScoringModel#score(float[] modelFeatureValuesNormalized)}.
	</p>
	<p>
	A {@link org.apache.solr.ltr.feature.Feature} will produce a particular value for each document, so
	it is modeled as a {@link org.apache.lucene.search.Query}. The package
	{@link org.apache.solr.ltr.feature} contains several examples
	of features. One benefit of extending the Query object is that we can reuse
	Query as a feature, see for example {@link org.apache.solr.ltr.feature.SolrFeature}.
	Features for a document can also be returned in the response by
	using the FeatureTransformer (a {@link org.apache.solr.response.transform.DocTransformer DocTransformer})
	provided by {@link org.apache.solr.ltr.response.transform.LTRFeatureLoggerTransformerFactory}.
	</p>
	<p>
	{@link org.apache.solr.ltr.store} contains all the logic to store all the features and the models.
	Models are registered into a unique {@link org.apache.solr.ltr.store.ModelStore ModelStore},
	and each model specifies a particular {@link org.apache.solr.ltr.store.FeatureStore FeatureStore} that
	will contain a particular subset of features.
	<p>
	</p>
	Features and models can be managed through a REST API, provided by the
	{@link org.apache.solr.rest.ManagedResource Managed Resources}
	{@link org.apache.solr.ltr.store.rest.ManagedFeatureStore ManagedFeatureStore}
	and {@link org.apache.solr.ltr.store.rest.ManagedModelStore ManagedModelStore}.
	</p>
	</body>
	</html>