## Theory Behind Relevance Scoringedit

Lucene (and thus Elasticsearch) uses the
*Boolean model*
to find matching documents, and a formula called the
*practical scoring function*
to calculate relevance. This formula borrows concepts from
*term frequency/inverse document frequency* and the
*vector space model*
but adds more-modern features like a coordination factor, field length
normalization, and term or query clause boosting.

Don’t be alarmed! These concepts are not as complicated as the names make them appear. While this section mentions algorithms, formulae, and mathematical models, it is intended for consumption by mere humans. Understanding the algorithms themselves is not as important as understanding the factors that influence the outcome.

### Boolean Modeledit

The *Boolean model* simply applies the `AND`

, `OR`

, and `NOT`

conditions
expressed in the query to find all the documents that match. A query for

full AND text AND search AND (elasticsearch OR lucene)

will include only documents that contain all of the terms `full`

, `text`

, and
`search`

, and either `elasticsearch`

or `lucene`

.

This process is simple and fast. It is used to exclude any documents that cannot possibly match the query.

### Term Frequency/Inverse Document Frequency (TF/IDF)edit

Once we have a list of matching documents, they need to be ranked by
relevance. Not all documents will contain all the terms, and some terms are
more important than others. The relevance score of the whole document
depends (in part) on the *weight* of each query term that appears in
that document.

The weight of a term is determined by three factors, which we already introduced in What Is Relevance?. The formulae are included for interest’s sake, but you are not required to remember them.

#### Term frequencyedit

How often does the term appear in this document? The more often, the
*higher* the weight. A field containing five mentions of the same term is
more likely to be relevant than a field containing just one mention.
The term frequency is calculated as follows:

tf(t in d) = √frequency

The term frequency ( |

If you don’t care about how often a term appears in a field, and all you care about is that the term is present, then you can disable term frequencies in the field mapping:

#### Inverse document frequencyedit

How often does the term appear in all documents in the collection? The more
often, the *lower* the weight. Common terms like `and`

or `the`

contribute
little to relevance, as they appear in most documents, while uncommon terms
like `elastic`

or `hippopotamus`

help us zoom in on the most interesting
documents. The inverse document frequency is calculated as follows:

idf(t) = 1 + log ( numDocs / (docFreq + 1))

#### Field-length normedit

How long is the field? The shorter the field, the *higher* the weight. If a
term appears in a short field, such as a `title`

field, it is more likely that
the content of that field is *about* the term than if the same term appears
in a much bigger `body`

field. The field length norm is calculated as follows:

norm(d) = 1 / √numTerms

While the field-length norm is important for full-text search, many other
fields don’t need norms. Norms consume approximately 1 byte per `string`

field
per document in the index, whether or not a document contains the field. Exact-value `not_analyzed`

string fields have norms disabled by default,
but you can use the field mapping to disable norms on `analyzed`

fields as
well:

PUT /my_index { "mappings": { "doc": { "properties": { "text": { "type": "string", "norms": { "enabled": false } } } } } }

This field will not take the field-length norm into account. A long field and a short field will be scored as if they were the same length. |

For use cases such as logging, norms are not useful. All you care about is whether a field contains a particular error code or a particular browser identifier. The length of the field does not affect the outcome. Disabling norms can save a significant amount of memory.

#### Putting it togetheredit

These three factors—term frequency, inverse document frequency, and field-length norm—are calculated and stored at index time. Together, they are
used to calculate the *weight* of a single term in a particular document.

When we refer to *documents* in the preceding formulae, we are actually talking about
a field within a document. Each field has its own inverted index and thus,
for TF/IDF purposes, the value of the field is the value of the document.

When we run a simple `term`

query with `explain`

set to `true`

(see
Understanding the Score), you will see that the only factors involved in calculating the
score are the ones explained in the preceding sections:

PUT /my_index/doc/1 { "text" : "quick brown fox" } GET /my_index/doc/_search?explain { "query": { "term": { "text": "fox" } } }

The (abbreviated) `explanation`

from the preceding request is as follows:

weight(text:fox in 0) [PerFieldSimilarity]: 0.15342641 result of: fieldWeight in 0 0.15342641 product of: tf(freq=1.0), with freq of 1: 1.0 idf(docFreq=1, maxDocs=1): 0.30685282 fieldNorm(doc=0): 0.5

The final | |

The term | |

The inverse document frequency of | |

The field-length normalization factor for this field. |

Of course, queries usually consist of more than one term, so we need a way of combining the weights of multiple terms. For this, we turn to the vector space model.

### Vector Space Modeledit

The *vector space model* provides a way of comparing a multiterm query
against a document. The output is a single score that represents how well the
document matches the query. In order to do this, the model represents both the document
and the query as *vectors*.

A vector is really just a one-dimensional array containing numbers, for example:

[1,2,5,22,3,8]

In the vector space model, each number in the vector is the *weight* of a term,
as calculated with term frequency/inverse document frequency.

While TF/IDF is the default way of calculating term weights for the vector space model, it is not the only way. Other models like Okapi-BM25 exist and are available in Elasticsearch. TF/IDF is the default because it is a simple, efficient algorithm that produces high-quality search results and has stood the test of time.

Imagine that we have a query for “happy hippopotamus.” A common word like
`happy`

will have a low weight, while an uncommon term like `hippopotamus`

will have a high weight. Let’s assume that `happy`

has a weight of 2 and
`hippopotamus`

has a weight of 5. We can plot this simple two-dimensional
vector—`[2,5]`

—as a line on a graph starting at point (0,0) and
ending at point (2,5), as shown in Figure 27, “A two-dimensional query vector for “happy hippopotamus” represented”.

Now, imagine we have three documents:

- I am
*happy*in summer. - After Christmas I’m a
*hippopotamus*. - The
*happy hippopotamus*helped Harry.

We can create a similar vector for each document, consisting of the weight of
each query term—`happy`

and `hippopotamus`

—that appears in the
document, and plot these vectors on the same graph, as shown in Figure 28, “Query and document vectors for “happy hippopotamus””:

- Document 1:
`(happy,____________)`

—`[2,0]`

- Document 2:
`( ___ ,hippopotamus)`

—`[0,5]`

- Document 3:
`(happy,hippopotamus)`

—`[2,5]`

The nice thing about vectors is that they can be compared. By measuring the angle between the query vector and the document vector, it is possible to assign a relevance score to each document. The angle between document 1 and the query is large, so it is of low relevance. Document 2 is closer to the query, meaning that it is reasonably relevant, and document 3 is a perfect match.

In practice, only two-dimensional vectors (queries with two terms) can be
plotted easily on a graph. Fortunately, *linear algebra*—the branch of
mathematics that deals with vectors—provides tools to compare the
angle between multidimensional vectors, which means that we can apply the
same principles explained above to queries that consist of many terms.

You can read more about how to compare two vectors by using *cosine similarity*.

Now that we have talked about the theoretical basis of scoring, we can move on to see how scoring is implemented in Lucene.