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<record>
  <title>Possibilistic Model for Relevance Feedback in Collaborative Information Retrieval</title>
  <journal>International Journal of Web Applications</journal>
  <author>Fatiha Naouar, Lobna Hlaoua, Mohamed Nazih Omri</author>
  <volume>4</volume>
  <issue>2</issue>
  <year>2012</year>
  <doi></doi>
  <url>http://dline.info/ijwa/fulltext/v4n2/3.pdf</url>
  <abstract>Web information is too heterogeneous that users have difficulties to retrieve their needed information: text, image orvideo. In this context, the collaborative work presents one solution proposed to solve this problem. Collaborative retrieval enables the retrieval historiesâ€™ sharing between users having the same profile across multiple tools such as annotations. We propose in this paper to improve collaborative retrieval performance, considering the annotations as a new source of information describing documents. In our contribution, we propose to apply the relevance feedback to extend the userâ€™s query. So we use a possibilistic approach to extract the relevant terms from annotations given in semi-structured documents returned by collaborative retrieval systems.</abstract>
</record>
