IMPROVE THE RECOMMENDER SYSTEM USING SEMANTIC WEB

To buy his/her necessities such as books, movies, CD, music, etc., one always trusts others’ oral and written consultations and offers and include them in his/her decisions. Nowadays, regarding the progress of technologies and development of e-business in websites, a new age of digital life has been commenced with the Recommender systems. The most important objectives of these systems include attracting the customers and their confidences by detecting their interests and tastes and recommending them the most appropriate offer. Using the relationships between entities in the DBpedia ontology, this study tries to investigate the application of and extracting the information in the movie area. In the next step, the structure of Recommender System is designed and its performance is evaluated using information extracted from "MovieLens" database. This study’s endeavor is to present a comprehensive overview of Recommender systems and a proposed method based on the benefits of semantic web databases, along with the implementation compared to existing methods. Our results indicate that the proposed method outperforms in terms of efficiency and performance.