With the ever increasing information overload on the internet, recommender systems have long become a necessity. The popularity of e-commerce sites is increasing by the day and an abundance of shopping sites are presenting users with an increasing number of choices. It has become a challenging task to meet expectations of customers to better understand their needs and provide them with information and suggestions of their interest. With the e-commerce field being fiercely competitive, businesses have started to feel the need of personalization which helps them in building customer loyalty [17]. Personalized recommendations can prove to be the most important aspect of the evolution of the recommender systems. Personalized recommendation services provide opportunities to promote new products, increase sales, click-through and conversion rates [18]. The use of semantic web technologies in recommender systems can effectively enhance the quality of recommendation. Semantic web has provided structured knowledge representation tools such as taxonomies, ontologies, powerful languages such as Resource Description Framework (RDF), Web Ontology Language (OWL), etc. which can be used to represent rich, complex knowledge about things and their relationships and query languages such as SPARQL, reasoning engines that can infer logical consequences from a set of assertions. Semantics enable machines to process natural languages in a manner close to human cognition and mimic human reasoning to a certain extent [12]. This can greatly help to generate personalized predictions in the recommender framework [6]
