Personalized Recommendation for Online Retail Applications Based on Ontology Evolution

Abstract

The upcoming generation of World Wide Web is signified in semantic web technology that allows future applications to grasp and connect with numerous knowledge bases. Due to its exclusive function in modeling specific domain, Ontology has been playing an essential role in semantic web development. Recommender systems are an indispensable part of online site, which makes their use of high value in recommending items to users according to their interests. The semantic recommender systems recently aim to accomplish the website ontologies to generate semantic recommendations for users' profiles. Therefore, ontology-based semantic recommender systems are used to develop web recommendation. In this paper a recommendation system architecture based on ontology is proposed to give semantic recommendations for each user profile. The proposed system architecture applies two recommendation techniques, content-based filtering and collaborative filtering.