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<record>
  <title>Semantically Enhanced Recommendation Systems</title>
  <journal>Journal of E-Technology</journal>
  <author>Omar NOUALI, Amokrane BELLOUI, Nadia Taboudjemat</author>
  <volume>1</volume>
  <issue>3</issue>
  <year>2010</year>
  <doi></doi>
  <url>http://www.dline.info/jet/fulltext/v1n3/3.pdf</url>
  <abstract>Collaborative filtering systems have some limitations such as cold-start problems for a new user, a new resource or both. In this paper, we show that using semantic information describing users and resources can reduce the problems and lead to a better precision, coverage and quality for the recommendation engine. Semantic web is the infrastructure used for managing such semantic descriptions. We have evaluated several strategies of filtering hybridization: weighting, adaptive change or switching and feature combination.</abstract>
</record>
