Learning Implicit User Interests Using Ontology and Search History for Personalization

The key for providing a robust context for personalized information retrieval is to build a library which gathers the long term and the short term user’s interests and then using it in the retrieval process in order to deliver results that better meet the user’s information needs. In this paper, we present an enhanced approach for learning a semantic representation of the underlying user’s interests using the search history and a predefined ontology. The basic idea is to learn the user’s interests by collecting evidence from his search history and represent them conceptually using the concept hierarchy of the ontology. We also involve a dynamic method which tracks changes of the short term user’s interests using a correlation metric measure in order to learn and maintain the