Personalized and Effective Web Page Recommendation Search System based on Ontology and Semantic Enhancement

The project proposes a personalized-recommendation search system, a system that makes use of representations of items and user-profiles based on ontologies and semantic enhancement in order to provide semantic applications with personalized services. It provides a better personalized recommendation search system by integrating domain knowledge and web usage knowledge. It uses two methods ontology and semantic enhancement. Ontology approach is written in logical based language. Here the input data is weblogs that record user sessions on a daily basis. The user session include information about users, webpage navigation activities (web snippets). Based on these facts, we aim to discover domain knowledge from the titles of visited web pages at a website and represent the discovered knowledge in domain ontology to support effective recommendation systems. Metaphysics technique is semi-automated in order that the event efforts from the developers are reduced. Second, Semantic method is achieved by using two different methods. A domain based mostly that produces illation regarding users interests, and a Taxonomy based mostly similarity want to refine item-user matching algorithmic rule up overall results. Propose recommender system also creates User profiles based on user preferences or past search histories. Password security is provided by Double Caesar cipher encryption method. The Propose system also provides the location from where the user has searched. This Project provides better personalized recommendation search system which is secure and effective.