Information retrieval

**Information retrieval** is a field of computer science. It looks at how to get important data from a collection of information. Usually, it is either a full-text search, or it searches the metadata. Depending on the content, there may also be other indices. Information retrieval is about finding existing information. It is different from knowledge discovery in databases.

Information retrieval techniques are normally used in internet search engines, but also when looking for information on a subject in a library. They are also used when searching complex content such as images in a database. This kind of content is usually described using metadata.

## Problem description

When looking for information, the following  problems are commonly encountered:
1. The database contains an insufficient amount of information about the content of the documents it contains. A search may yield an incorrect answer, or no answer at all.
1. The search is done with terms and keywords that are vague, so that the results returned do not exactly match the needs of the request.
A system for information retrieval will attach a score to each document returned. This score reflects how well the document matches the query of the user. The documents with the best scores are shown to the user, who has the possibility to refine the query.

## Different models

There are different kinds of models that are used in information retrieval

### First dimension: the mathematical model

- Set-theoretic models represent documents as a set of words or features.
- Algebraic models use vectors, matrices and tuples.
- _Probabilistic models_ treat the process of document retrieval as a  probabilistic inference. Similarities are computed as probabilities  that a document is relevant for a given query. Probabilistic theorems  like the Bayes' theorem are often used in these models.
- _Feature-based retrieval models_ view documents as vectors of values of _feature functions_ (or just _features_) and seek the best way to combine these features into a single relevance score, typically by learning to rank methods. Feature functions are arbitrary functions of document and  query, and as such can easily incorporate almost any other retrieval  model as just a yet another feature.

### Second dimension: the properties of the model

- _Models without term-interdependencies_ treat different terms/words as independent. This fact is usually represented in vector space models by the orthogonality assumption of term vectors or in probabilistic models by an independency assumption for term variables.
- _Models with immanent term interdependencies_ allow a representation of interdependencies between terms. However the degree of the interdependency between two terms is defined by the model itself. It is usually directly or indirectly derived (e.g. by dimensional reduction) from the co-occurrence of those terms in the whole set of documents.
- _Models with transcendent term interdependencies_ allow a representation of interdependencies between terms, but they do not allege how the interdependency between two terms is defined. They rely an external source for the degree of interdependency between two terms. (For example a human or sophisticated algorithms.)

In addition, each model has parameters, which influence its performance. Some of the models make a number of assumption about the data; they were developed for a very special purpose. Using such a model for a different purpose may not yield good results.