Improving recommender systems with the help of Semantic Web
Subject Areas : General
rahele beheshti
1
()
mohammad ebrahim samie
2
()
ali hamze
3
()
Keywords: Referrer systems, Semantic Web, Ontology, DBpedia,
Abstract :
In order to provide for the necessities of life, human beings always use the advice and suggestions of others, which are provided orally or in writing, and take them into account in their decisions. Today, with the advancement of technology and the expansion of e-business in the context of Internet websites, a new chapter of digital life has begun with the help of advisory systems. The most important goal in these systems is to attract customers and gain their trust by offering the best and most appropriate offer to buy products, according to their interests and tastes in the midst of a multitude of choices. In this research, an attempt has been made to extract information related to the field of film with the help of connections in DBpedia's ontology. Then the structure of the recommender system is designed and implemented and with the help of the information available in the MovieLens database, the performance of the recommender system is evaluated. According to the evaluations, the proposed model is more efficient among other methods that somehow use the semantic web.
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