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Research that Adapts Information Retrieval Based on a User Profile

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As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find relevant documents to their particular needs. In this paper, we report on research that adapts information retrieval based on a user profile. Ontology models are widely used to represent user profiles in personalized web information retrieval. Many models have utilized only knowledge from either a global knowledge base or a user local information for representing user profiles. A personalized ontology model is used for knowledge representation and reasoning over user profiles. This model uses ontological user profiles based on both a world knowledge base and user local instance repositories. It is observed…show more content…
Many researchers have attempted to discover user background knowledge through global or local analysis to represent user profiles.

i) Motivation
The basic objective regarding this project is to achieve high performance in web information retrieval using a personalized ontology model. Most of the times when user searches for some information with some ideas in mind , It is always the case that he didn’t get the information exactly as he wants in first page . He has to go through different pages until he get the information exactly as per his concept. The basic idea is to create ontological user profiles from both a world knowledge base and user local instance repositories in order to have a fast information retrieval as per the concept model of the user. ii) Existing systems

Commonly used knowledge bases include generic ontologies, thesauruses, and online knowledge bases. The global analysis produce effective performance for user background knowledge extraction but it is limited by the quality of the used knowledge base. Local analysis investigates user local information or observes user behavior in user profiles. Analyzed query logs to discover user background knowledge is used. Users were provided with a set of documents and asked for relevance feedback. User background knowledge was then discovered from this feedback for user profiles. The discovered results may contain noisy and uncertain information.

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