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Chapter 1 + Section 2.1 Introduction

Information Retrieval Process

Predicting which documents are relevant is usaually dependent on a ranking algorithm.
The three classic models in information retreival are:
- Boolean Model: In the boolean model documents and queries are represented as sets of index terms, thus we say this model is a set theoretic model
- Vector Model: In the vector model documents and queries are represented as vectors in a t-dimensional space, thus we say that the model is algebraic.
- Probabilistic Model: The framework for modeling document and query representations is based on probability theory, and thus we sat that the model is prababilistic.

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+{{{#!latex2
\begin{definition}
An information retrieval model is a quadruple $D,Q,F,R(q_i , d_j))$ where 
\begin{numbering}
\item $D$ is a set composed of logical views (or representations) for the documents in the collection.
\item $Q$ is a set composed of logical views (or representations) for the user information needs. Such representations are called queries
\item $F$ is a framework for modeling document representations, queries and their relationships.
\item $R(q_i,d_j)$ is a ranging function wich associates a real number with a query $q_i \in Q$ and a document represenation $d_j \in D$. Such ranking defines an ordering among the documents with regard to the query $q_i$.
\end{numbering}
\end{definition}