Improving Web Navigation Usability by Maintaining a Personalized Recommendation System

Abstract

Recommendation system can take the benefits of semantic reasoning potentiality which is used to overcome the common constraints of the current system and hence to improve the quality of recommendations. In this paper, we present a personalized recommendation system, a system that makes use of representations of items and user-profiles based on ontology to facilitate the semantic applications with personalized services. The recommender uses domain based inference method to mould the user’s interests in a more effective and accurate way to enhance personalization. Web Usage Mining plays a significant role in augmenting web personalization and recommendations. We propose an efficient personalized recommendation system based on domain ontology and Web Usage Mining. The primary approach is to extract features from web documents and to structure related concepts. Then construct ontology for the website using the characteristics extracted from the documents. According to the semantic similarity of web documents, cluster them into diverse semantic ideas where the diverse ideas imply different preferences. The proposed approach incorporates semantic knowledge into Web Usage Mining and personalization processes.