Possibilistic Model for Relevance Feedback in Collaborative Information Retrieval

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 histories’ 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 user’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.