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org.apache.lucene.search
abstract public class: Similarity [javadoc | source]
java.lang.Object
   org.apache.lucene.search.Similarity

All Implemented Interfaces:
    Serializable

Direct Known Subclasses:
    SimilarityDelegator, SimpleSimilarity, SweetSpotSimilarity, DefaultSimilarity

Expert: Scoring API.

Similarity defines the components of Lucene scoring. Overriding computation of these components is a convenient way to alter Lucene scoring.

Suggested reading: Introduction To Information Retrieval, Chapter 6.

The following describes how Lucene scoring evolves from underlying information retrieval models to (efficient) implementation. We first brief on VSM Score, then derive from it Lucene's Conceptual Scoring Formula, from which, finally, evolves Lucene's Practical Scoring Function (the latter is connected directly with Lucene classes and methods).

Lucene combines Boolean model (BM) of Information Retrieval with Vector Space Model (VSM) of Information Retrieval - documents "approved" by BM are scored by VSM.

In VSM, documents and queries are represented as weighted vectors in a multi-dimensional space, where each distinct index term is a dimension, and weights are Tf-idf values.

VSM does not require weights to be Tf-idf values, but Tf-idf values are believed to produce search results of high quality, and so Lucene is using Tf-idf. Tf and Idf are described in more detail below, but for now, for completion, let's just say that for given term t and document (or query) x, Tf(t,x) varies with the number of occurrences of term t in x (when one increases so does the other) and idf(t) similarly varies with the inverse of the number of index documents containing term t.

VSM score of document d for query q is the Cosine Similarity of the weighted query vectors V(q) and V(d):
 
cosine-similarity(q,d)   =  
V(q) · V(d)
–––––––––
|V(q)| |V(d)|
VSM Score

 
Where V(q) · V(d) is the dot product of the weighted vectors, and |V(q)| and |V(d)| are their Euclidean norms.

Note: the above equation can be viewed as the dot product of the normalized weighted vectors, in the sense that dividing V(q) by its euclidean norm is normalizing it to a unit vector.

Lucene refines VSM score for both search quality and usability:

Under the simplifying assumption of a single field in the index, we get Lucene's Conceptual scoring formula:
 
score(q,d)   =   coord-factor(q,d) ·   query-boost(q) ·  
V(q) · V(d)
–––––––––
|V(q)|
  ·   doc-len-norm(d)   ·   doc-boost(d)
Lucene Conceptual Scoring Formula

 

The conceptual formula is a simplification in the sense that (1) terms and documents are fielded and (2) boosts are usually per query term rather than per query.

We now describe how Lucene implements this conceptual scoring formula, and derive from it Lucene's Practical Scoring Function.

For efficient score computation some scoring components are computed and aggregated in advance:

Lucene's Practical Scoring Function is derived from the above. The color codes demonstrate how it relates to those of the conceptual formula:

score(q,d)   =   coord(q,d)  ·  queryNorm(q)  ·  ( tf(t in d)  ·  idf(t)2  ·  t.getBoost() ·  norm(t,d) )
t in q
Lucene Practical Scoring Function

where

  1. tf(t in d) correlates to the term's frequency, defined as the number of times term t appears in the currently scored document d. Documents that have more occurrences of a given term receive a higher score. Note that tf(t in q) is assumed to be 1 and therefore it does not appear in this equation, However if a query contains twice the same term, there will be two term-queries with that same term and hence the computation would still be correct (although not very efficient). The default computation for tf(t in d) in DefaultSimilarity is:
     
    tf(t in d)   =   frequency½

     
  2. idf(t) stands for Inverse Document Frequency. This value correlates to the inverse of docFreq (the number of documents in which the term t appears). This means rarer terms give higher contribution to the total score. idf(t) appears for t in both the query and the document, hence it is squared in the equation. The default computation for idf(t) in DefaultSimilarity is:
     
    idf(t)   =   1 + log (
    numDocs
    –––––––––
    docFreq+1
    )

     
  3. coord(q,d) is a score factor based on how many of the query terms are found in the specified document. Typically, a document that contains more of the query's terms will receive a higher score than another document with fewer query terms. This is a search time factor computed in coord(q,d) by the Similarity in effect at search time.
     
  4. queryNorm(q) is a normalizing factor used to make scores between queries comparable. This factor does not affect document ranking (since all ranked documents are multiplied by the same factor), but rather just attempts to make scores from different queries (or even different indexes) comparable. This is a search time factor computed by the Similarity in effect at search time. The default computation in DefaultSimilarity produces a Euclidean norm:
     
    queryNorm(q)   =   queryNorm(sumOfSquaredWeights)   =  
    1
    ––––––––––––––
    sumOfSquaredWeights½

     
    The sum of squared weights (of the query terms) is computed by the query org.apache.lucene.search.Weight object. For example, a boolean query computes this value as:
     
    sumOfSquaredWeights   =   q.getBoost() 2  ·  ( idf(t)  ·  t.getBoost() ) 2
    t in q

     
  5. t.getBoost() is a search time boost of term t in the query q as specified in the query text (see query syntax), or as set by application calls to setBoost() . Notice that there is really no direct API for accessing a boost of one term in a multi term query, but rather multi terms are represented in a query as multi TermQuery objects, and so the boost of a term in the query is accessible by calling the sub-query getBoost() .
     
  6. norm(t,d) encapsulates a few (indexing time) boost and length factors:
    • Document boost - set by calling doc.setBoost() before adding the document to the index.
    • Field boost - set by calling field.setBoost() before adding the field to a document.
    • lengthNorm(field) - computed when the document is added to the index in accordance with the number of tokens of this field in the document, so that shorter fields contribute more to the score. LengthNorm is computed by the Similarity class in effect at indexing.

    When a document is added to the index, all the above factors are multiplied. If the document has multiple fields with the same name, all their boosts are multiplied together:
     
    norm(t,d)   =   doc.getBoost()  ·  lengthNorm(field)  ·  f.getBoost ()
    field f in d named as t

     
    However the resulted norm value is encoded as a single byte before being stored. At search time, the norm byte value is read from the index directory and decoded back to a float norm value. This encoding/decoding, while reducing index size, comes with the price of precision loss - it is not guaranteed that decode(encode(x)) = x. For instance, decode(encode(0.89)) = 0.75.
     
    Compression of norm values to a single byte saves memory at search time, because once a field is referenced at search time, its norms - for all documents - are maintained in memory.
     
    The rationale supporting such lossy compression of norm values is that given the difficulty (and inaccuracy) of users to express their true information need by a query, only big differences matter.
     
    Last, note that search time is too late to modify this norm part of scoring, e.g. by using a different Similarity for search.
     

Field Summary
public static final  int NO_DOC_ID_PROVIDED     
Method from org.apache.lucene.search.Similarity Summary:
computeNorm,   coord,   decodeNorm,   encodeNorm,   getDefault,   getNormDecoder,   idf,   idfExplain,   idfExplain,   lengthNorm,   queryNorm,   scorePayload,   setDefault,   sloppyFreq,   tf,   tf
Methods from java.lang.Object:
clone,   equals,   finalize,   getClass,   hashCode,   notify,   notifyAll,   toString,   wait,   wait,   wait
Method from org.apache.lucene.search.Similarity Detail:
 public float computeNorm(String field,
    FieldInvertState state) 
    Compute the normalization value for a field, given the accumulated state of term processing for this field (see FieldInvertState ).

    Implementations should calculate a float value based on the field state and then return that value.

    For backward compatibility this method by default calls #lengthNorm(String, int) passing FieldInvertState#getLength() as the second argument, and then multiplies this value by FieldInvertState#getBoost() .

    WARNING: This API is new and experimental and may suddenly change.

 abstract public float coord(int overlap,
    int maxOverlap)
    Computes a score factor based on the fraction of all query terms that a document contains. This value is multiplied into scores.

    The presence of a large portion of the query terms indicates a better match with the query, so implementations of this method usually return larger values when the ratio between these parameters is large and smaller values when the ratio between them is small.

 public static float decodeNorm(byte b) 
    Decodes a normalization factor stored in an index.
 public static byte encodeNorm(float f) 
    Encodes a normalization factor for storage in an index.

    The encoding uses a three-bit mantissa, a five-bit exponent, and the zero-exponent point at 15, thus representing values from around 7x10^9 to 2x10^-9 with about one significant decimal digit of accuracy. Zero is also represented. Negative numbers are rounded up to zero. Values too large to represent are rounded down to the largest representable value. Positive values too small to represent are rounded up to the smallest positive representable value.

 public static Similarity getDefault() 
    Return the default Similarity implementation used by indexing and search code.

    This is initially an instance of DefaultSimilarity .

 public static float[] getNormDecoder() 
    Returns a table for decoding normalization bytes.
 abstract public float idf(int docFreq,
    int numDocs)
    Computes a score factor based on a term's document frequency (the number of documents which contain the term). This value is multiplied by the #tf(int) factor for each term in the query and these products are then summed to form the initial score for a document.

    Terms that occur in fewer documents are better indicators of topic, so implementations of this method usually return larger values for rare terms, and smaller values for common terms.

 public IDFExplanation idfExplain(Term term,
    Searcher searcher) throws IOException 
 public IDFExplanation idfExplain(Collection<Term> terms,
    Searcher searcher) throws IOException 
    Computes a score factor for a phrase.

    The default implementation sums the idf factor for each term in the phrase.

 abstract public float lengthNorm(String fieldName,
    int numTokens)
    Computes the normalization value for a field given the total number of terms contained in a field. These values, together with field boosts, are stored in an index and multipled into scores for hits on each field by the search code.

    Matches in longer fields are less precise, so implementations of this method usually return smaller values when numTokens is large, and larger values when numTokens is small.

    Note that the return values are computed under org.apache.lucene.index.IndexWriter#addDocument(org.apache.lucene.document.Document) and then stored using #encodeNorm(float) . Thus they have limited precision, and documents must be re-indexed if this method is altered.

 abstract public float queryNorm(float sumOfSquaredWeights)
    Computes the normalization value for a query given the sum of the squared weights of each of the query terms. This value is multiplied into the weight of each query term. While the classic query normalization factor is computed as 1/sqrt(sumOfSquaredWeights), other implementations might completely ignore sumOfSquaredWeights (ie return 1).

    This does not affect ranking, but the default implementation does make scores from different queries more comparable than they would be by eliminating the magnitude of the Query vector as a factor in the score.

 public float scorePayload(int docId,
    String fieldName,
    int start,
    int end,
    byte[] payload,
    int offset,
    int length) 
    Calculate a scoring factor based on the data in the payload. Overriding implementations are responsible for interpreting what is in the payload. Lucene makes no assumptions about what is in the byte array.

    The default implementation returns 1.

 public static  void setDefault(Similarity similarity) 
    Set the default Similarity implementation used by indexing and search code.
 abstract public float sloppyFreq(int distance)
    Computes the amount of a sloppy phrase match, based on an edit distance. This value is summed for each sloppy phrase match in a document to form the frequency that is passed to #tf(float) .

    A phrase match with a small edit distance to a document passage more closely matches the document, so implementations of this method usually return larger values when the edit distance is small and smaller values when it is large.

 public float tf(int freq) 
    Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the #idf(int, int) factor for each term in the query and these products are then summed to form the initial score for a document.

    Terms and phrases repeated in a document indicate the topic of the document, so implementations of this method usually return larger values when freq is large, and smaller values when freq is small.

    The default implementation calls #tf(float) .

 abstract public float tf(float freq)
    Computes a score factor based on a term or phrase's frequency in a document. This value is multiplied by the #idf(int, int) factor for each term in the query and these products are then summed to form the initial score for a document.

    Terms and phrases repeated in a document indicate the topic of the document, so implementations of this method usually return larger values when freq is large, and smaller values when freq is small.