# Changes: Information retrieval

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Information retrieval (IR) is the art and science of searching for information in documents, searching for documents themselves, searching for metadata which describe documents, or searching within databases, whether relational stand alone databases or hypertext networked databases such as the Internet or intranets, for text, sound, images or data. There is a common confusion, however, between data retrieval, document retrieval, information retrieval, and text retrieval, and each of these have their own bodies of literature, theory, praxis and technologies.

The term "information retrieval" was coined by Calvin Mooers in 1948-50.

IR is a broad interdisciplinary field, that draws on many other disciplines. Indeed, because it is so broad, it is normally poorly understood, being approached typically from only one perspective or another. It stands at the junction of many established fields, and draws upon cognitive psychology, information architecture, information design, human information behaviour, linguistics, semiotics, information science, computer science and librarianship.

Automated information retrieval (IR) systems were originally used to manage information explosion in scientific literature in the last few decades. Many universities and public libraries use IR systems to provide access to books, journals, and other documents. IR systems are often related to object and query. Queries are formal statements of information needs that are put to an IR system by the user. An object is an entity which keeps or stores information in a database. User queries are matched to documents stored in a database. A document is, therefore, a data object. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates.

In 1992 the Department of Defense, along with the National Institute of Standards and Technology (NIST), cosponsored the Text Retrieval Conference (TREC) as part of the TIPSTER text program. The aim of this was to look into the information retrieval community by supplying the infrastructure that was needed for such a huge evaluation of text retrieval methodologies.

Web search engines such as Google and Lycos are amongst the most visible applications of information retrieval research.

## Performance measures Edit

There are various ways to measure how well the retrieved information matches the intended information:

### Precision Edit

The proportion of relevant documents to all the documents retrieved:

P = (number of relevant documents retrieved) / (number of documents retrieved)

In binary classification, precision is analogous to positive predictive value. Precision can also be evaluated at a given cut-off rank, denoted P@n, instead of all retrieved documents.

### Recall Edit

The proportion of relevant documents that are retrieved, out of all relevant documents available:

R = (number of relevant documents retrieved) / (number of relevant documents)

In binary classification, recall is called sensitivity.

### F-measure Edit

The weighted harmonic mean of precision and recall, the traditional F-measure is:

$F = 2 \times \mathrm{precision} \times \mathrm{recall} / (\mathrm{precision} + \mathrm{recall}).\,$

This is also known as the $F_1$ measure, because recall and precision are evenly weighted.

The general formula is:

$F_N = (1 + N^2) \times \mathrm{precision} \times \mathrm{recall} / ((N^2 \times \mathrm{precision}) + \mathrm{recall}).\,$

Two other commonly used F measures are the $F_{0.5}$ measure, which weights precision twice as much as recall, and the $F_2$ measure, which weights recall twice as much as precision.

### Mean average precision Edit

Over a set of queries, find the mean of the average precisions, where Average Precision is the average of the precision after each relevant document is retrieved.

Where r is the rank, N the number retrieved, rel() a binary function on the relevance of a given rank, and P() precision at a given cut-off rank:

$\operatorname{Ave}P = \frac{\sum_{r=1}^N (P(r) \times \mathrm{rel}(r))}{\mbox{number of relevant documents}} \!$

This method emphasizes returning more relevant documents earlier.

## Model types Edit

For a successful IR, it is necessary to represent the documents in some way. There are a number of models for this purpose roughly dividable into three main groups:

## Major figures in information retrieval Edit

Awards in this field: Tony Kent Strix award

## ACM SIGIR Gerard Salton AwardEdit

1983 - Gerard Salton, Cornell University
"About the future of automatic information retrieval"
1988 - Karen Sparck Jones, University of Cambridge
"A look back and a look forward"
1991 - Cyril Cleverdon, Cranfield Institute of Technology
"The significance of the Cranfield tests on index languages"
1994 - William S. Cooper, University of California, Berkeley
"The formalism of probability theory in IR: a foundation or an encumbrance?"
1997 - Tefko Saracevic, Rutgers University
"Users lost: reflections on the past, future, and limits of information science"
2000 - Stephen E. Robertson, City University London
"On theoretical argument in information retrieval"
2003 - W. Bruce Croft, University of Massachusetts, Amherst
"Information retrieval and computer science: an evolving relationship"