Ontology-based Web Recommendation from tags

With the advent of social networks and tagging systems, The Internet has recently witnessed a big leap in the use of Web Recommendation Systems WRS. Based on users' likings of items and their browsing history on the world wide web, these systems are able to predict and recommend items and future purchases to users. They are being used now in various domains, like news article recommendation, product recommendation, and make-friend recommendation. WRS are still limited by several problems, of which are sparsity, and the new user problem. They also fail to make full use and harness the power of domain knowledge and semantic web ontologies. In this article, we discuss how an ontology-based WRS can utilize relations and concepts in an ontology, along with user-provided tags, to provide top-n recommendations without the need for item clustering or user ratings. For this purpose, we also propose a dimensionality reduction method based on the domain ontology, to solve the sparsity problem.