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Various term weighting schemes are discussed next followed by descriptions of three IR modelsVector Space, Probabilistic, and Language models. Approaches to evaluating IR systems are presented. Relevance feedback techniques as a means to improving retrieval effectiveness are described.
Information retrieval (IR) in computing and information science is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing.
Designing and using a retrieval system involves four major activities: information analysis, information organization and search, query formulation, and information retrieval and dissemination.
Interaction Design (IxD) is the design of interactive products and services in which a designers focus goes beyond the item in development to include the way users will interact with it. Thus, close scrutiny of users needs, limitations and contexts, etc. empowers designers to customize output to suit precise demands.
Main components of an information retrieval system The set of documents that the system can retrieve information from. Source data and documents are processed to create an index, mapping terms and data to the documents that contain them often in a dedicated, optimized data structure.
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Classical IR Model This model is based on mathematical knowledge that was easily recognized and understood as well. Boolean, Vector and Probabilistic are the three classical IR models.

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