An Information Retrieval System (IRS) is a software system that helps users to search, retrieve, and manage information from a large collection of data. It is designed to support users in finding relevant information in response to a query or a set of queries. The data in an IRS can be structured or unstructured, and may include text documents, multimedia content, images, videos, and other types of data.
An IRS typically involves three main components: a user interface, a search engine, and a database. The user interface allows users to enter queries and interact with the system. The search engine is responsible for processing the queries and retrieving relevant documents from the database. The database contains the indexed content and metadata associated with each document.
IRSs are widely used in various applications, including search engines, digital libraries, e-commerce, and social media platforms. They can help users to find information quickly and efficiently, and to manage the information they retrieve. Different types of IRSs can be designed to meet specific requirements, such as the size and complexity of the data, the nature of the queries, and the user preferences.
Recall : Recall measures the completeness of the retrieval, which is the proportion of relevant documents that are retrieved out of all the relevant documents in the database. In other words, recall measures the system's ability to retrieve all the relevant documents for a given query. A high recall rate means that the system retrieves most or all of the relevant documents for a given query.
Precision: Precision measures the relevance of the retrieval, which is the proportion of retrieved documents that are relevant to the user's query out of all the retrieved documents. In other words, precision measures the system's ability to retrieve only relevant documents for a given query. A high precision rate means that the system retrieves mostly relevant documents for a given query.
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Information Retrieval System | Objectives of IRS | Recall in IRS | Precision in IRS | Amit Sagu
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