List of Articles DBpedia

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  1 - IMPROVE THE RECOMMENDER SYSTEM USING SEMANTIC WEB
  mohammad ebrahim samie
  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 ne More

  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. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - Improving recommender systems with the help of Semantic Web
  rahele beheshti mohammad ebrahim samie ali hamze
  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-busine More

  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. Manuscript profile