# Package org.apache.lucene.search.similarities

This package contains the various ranking models that can be used in Lucene.

See: Description

Interface Summary
LMSimilarity.CollectionModel: A strategy for computing the collection language model.

Class Summary
AfterEffect: This class acts as the base class for the implementations of the first normalization of the informative content in the DFR framework.
AfterEffectB: Model of the information gain based on the ratio of two Bernoulli processes.
AfterEffectL: Model of the information gain based on Laplace's law of succession.
Axiomatic: Axiomatic approaches for IR.
AxiomaticF1EXP: F1EXP is defined as Sum(tf(term_doc_freq)*ln(docLen)*IDF(term)) where IDF(t) = pow((N+1)/df(t), k) N=total num of docs, df=doc freq
AxiomaticF1LOG: F1LOG is defined as Sum(tf(term_doc_freq)*ln(docLen)*IDF(term)) where IDF(t) = ln((N+1)/df(t)) N=total num of docs, df=doc freq
AxiomaticF2EXP: F2EXP is defined as Sum(tfln(term_doc_freq, docLen)*IDF(term)) where IDF(t) = pow((N+1)/df(t), k) N=total num of docs, df=doc freq
AxiomaticF2LOG: F2EXP is defined as Sum(tfln(term_doc_freq, docLen)*IDF(term)) where IDF(t) = ln((N+1)/df(t)) N=total num of docs, df=doc freq
AxiomaticF3EXP: F3EXP is defined as Sum(tf(term_doc_freq)*IDF(term)-gamma(docLen, queryLen)) where IDF(t) = pow((N+1)/df(t), k) N=total num of docs, df=doc freq gamma(docLen, queryLen) = (docLen-queryLen)*queryLen*s/avdl NOTE: the gamma function of this similarity creates negative scores
AxiomaticF3LOG: F3EXP is defined as Sum(tf(term_doc_freq)*IDF(term)-gamma(docLen, queryLen)) where IDF(t) = ln((N+1)/df(t)) N=total num of docs, df=doc freq gamma(docLen, queryLen) = (docLen-queryLen)*queryLen*s/avdl NOTE: the gamma function of this similarity creates negative scores
BasicModel: This class acts as the base class for the specific basic model implementations in the DFR framework.
BasicModelG: Geometric as limiting form of the Bose-Einstein model.
BasicModelIF: An approximation of the I(ne) model.
BasicModelIn: The basic tf-idf model of randomness.
BasicModelIne: Tf-idf model of randomness, based on a mixture of Poisson and inverse document frequency.
BasicStats: Stores all statistics commonly used ranking methods.
BM25Similarity: BM25 Similarity.
BooleanSimilarity: Simple similarity that gives terms a score that is equal to their query boost.
ClassicSimilarity: Expert: Historical scoring implementation.
DFISimilarity: Implements the Divergence from Independence (DFI) model based on Chi-square statistics (i.e., standardized Chi-squared distance from independence in term frequency tf).
DFRSimilarity: Implements the divergence from randomness (DFR) framework introduced in Gianni Amati and Cornelis Joost Van Rijsbergen.
Distribution: The probabilistic distribution used to model term occurrence in information-based models.
DistributionLL: Log-logistic distribution.
DistributionSPL: The smoothed power-law (SPL) distribution for the information-based framework that is described in the original paper.
IBSimilarity: Provides a framework for the family of information-based models, as described in Stéphane Clinchant and Eric Gaussier.
Independence: Computes the measure of divergence from independence for DFI scoring functions.
IndependenceChiSquared: Normalized chi-squared measure of distance from independence
IndependenceSaturated: Saturated measure of distance from independence
IndependenceStandardized: Standardized measure of distance from independence
Lambda: The lambda (λw) parameter in information-based models.
LambdaDF: Computes lambda as `docFreq+1 / numberOfDocuments+1`.
LambdaTTF: Computes lambda as `totalTermFreq+1 / numberOfDocuments+1`.
LMDirichletSimilarity: Bayesian smoothing using Dirichlet priors.
LMJelinekMercerSimilarity: Language model based on the Jelinek-Mercer smoothing method.
LMSimilarity: Abstract superclass for language modeling Similarities.
LMSimilarity.DefaultCollectionModel: Models `p(w|C)` as the number of occurrences of the term in the collection, divided by the total number of tokens `+ 1`.
LMSimilarity.LMStats: Stores the collection distribution of the current term.
MultiSimilarity: Implements the CombSUM method for combining evidence from multiple similarity values described in: Joseph A.
Normalization: This class acts as the base class for the implementations of the term frequency normalization methods in the DFR framework.
Normalization.NoNormalization: Implementation used when there is no normalization.
NormalizationH1: Normalization model that assumes a uniform distribution of the term frequency.
NormalizationH2: Normalization model in which the term frequency is inversely related to the length.
NormalizationH3: Dirichlet Priors normalization
NormalizationZ: Pareto-Zipf Normalization
PerFieldSimilarityWrapper: Provides the ability to use a different `Similarity` for different fields.
Similarity: Similarity defines the components of Lucene scoring.
Similarity.SimScorer: Stores the weight for a query across the indexed collection.
SimilarityBase: A subclass of `Similarity` that provides a simplified API for its descendants.
TFIDFSimilarity: Implementation of `Similarity` with the Vector Space Model.

## Package org.apache.lucene.search.similarities Description

This package contains the various ranking models that can be used in Lucene. The abstract class `Similarity` serves as the base for ranking functions. For searching, users can employ the models already implemented or create their own by extending one of the classes in this package.

## Table Of Contents

1. Summary of the Ranking Methods
2. Changing the Similarity

## Summary of the Ranking Methods

`BM25Similarity` is an optimized implementation of the successful Okapi BM25 model.

`ClassicSimilarity` is the original Lucene scoring function. It is based on the Vector Space Model. For more information, see `TFIDFSimilarity`.

`SimilarityBase` provides a basic implementation of the Similarity contract and exposes a highly simplified interface, which makes it an ideal starting point for new ranking functions. Lucene ships the following methods built on `SimilarityBase`:

- Amati and Rijsbergen's DFR framework;
- Clinchant and Gaussier's Information-based models for IR;
- The implementation of two language models from Zhai and Lafferty's paper.
- Divergence from independence models as described in "IRRA at TREC 2012" (Dinçer).

Since `SimilarityBase` is not optimized to the same extent as `ClassicSimilarity` and `BM25Similarity`, a difference in performance is to be expected when using the methods listed above. However, optimizations can always be implemented in subclasses; see below.

## Changing Similarity

Chances are the available Similarities are sufficient for all your searching needs. However, in some applications it may be necessary to customize your Similarity implementation. For instance, some applications do not need to distinguish between shorter and longer documents and could set BM25's `b` parameter to `0`.

To change `Similarity`, one must do so for both indexing and searching, and the changes must happen before either of these actions take place. Although in theory there is nothing stopping you from changing mid-stream, it just isn't well-defined what is going to happen.

To make this change, implement your own `Similarity` (likely you'll want to simply subclass `SimilarityBase`), and then register the new class by calling `IndexWriterConfig.setSimilarity(Similarity)` before indexing and `IndexSearcher.setSimilarity(Similarity)` before searching.

### Tuning BM25Similarity

`BM25Similarity` has two parameters that may be tuned:

- `k1`, which calibrates term frequency saturation and must be positive or null. A value of `0` makes term frequency completely ignored, making documents scored only based on the value of the `IDF` of the matched terms. Higher values of `k1` increase the impact of term frequency on the final score. Default value is `1.2`.
- `b`, which controls how much document length should normalize term frequency values and must be in `[0, 1]`. A value of `0` disables length normalization completely. Default value is `0.75`.

### Extending SimilarityBase

The easiest way to quickly implement a new ranking method is to extend `SimilarityBase`, which provides basic implementations for the low level . Subclasses are only required to implement the `SimilarityBase.score(BasicStats, double, double)` and `SimilarityBase.toString()` methods.

Another option is to extend one of the frameworks based on `SimilarityBase`. These Similarities are implemented modularly, e.g. `DFRSimilarity` delegates computation of the three parts of its formula to the classes `BasicModel`, `AfterEffect` and `Normalization`. Instead of subclassing the Similarity, one can simply introduce a new basic model and tell `DFRSimilarity` to use it.