1 Ocak 2012 Dergi makalesi Açık Erişim

Yakut, Ibrahim; Polat, Huseyin

Dublin Core

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Yakut, Ibrahim</dc:creator>
  <dc:creator>Polat, Huseyin</dc:creator>
  <dc:date>2012-01-01</dc:date>
  <dc:description>Collaborative filtering (CF) systems use customers' preferences about various products to offer recommendations. Providing accurate and reliable predictions is vital for both e-commerce companies and their customers. To offer such referrals, CF systems should have sufficient data. When data collected for CF purposes held by a central server, it is an easy task to provide recommendations. However, customers' preferences represented as ratings might be partitioned between two vendors. To supply trustworthy and correct predictions, such companies might desire to collaborate. Due to privacy concerns, financial fears, and legal issues; however, the parties may not want to disclose their data to each other.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/85219</dc:identifier>
  <dc:identifier>oai:zenodo.org:85219</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>DATA &amp; KNOWLEDGE ENGINEERING 72 239-256</dc:source>
  <dc:title>Arbitrarily distributed data-based recommendations with privacy</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>