Journal of Intelligent & Fuzzy Systems 30 (2016) 2119–2129 DOI:10.3233/IFS-151925 IOS Press 2119 Collaborative information retrieval model based on fuzzy confidence network Fatiha Naouar ∗ , Lobna Hlaoua and Mohamed Nazih Omri MARS Research Unit, Department of computer sciences Faculty of sciences of Monastir, University of Monastir, Monastir, Tunisia Abstract. The Web information is too heterogeneous that users have difficulties to retrieve their needed information: text, image or video. Indeed, 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. However the user has always problems in choosing the terms to form his query. This paper has proposed collaborative Information Retrieval Model based on Fuzzy Confidence Network. Our approach allows the detection of relevant annotations to a given evidence source. These annotations are next filtered to determine which are relevant to consider them as a new source of information that describes the document used to improve collaborative retrieval performance. We then measure the semantic relationships between terms which will be translated by the propagation of confidence. Experiments were conducted on different queries, showing very encouraging results that could reach an improvement rate. Keywords: Collaborative retrieval, propagation of confidence, annotation, filtering, relevance feedback 1. Introduction The development of the work mode of autonomy to cooperation has shown an improved performance of user research in several researches works [1, 8, 10, 23]. Indeed, the collaborative work can be carried out synchronously or asynchronously. The collabora- tive retrieval can reduce the retrieval time performed by the users of the same profile. The optimization of retrieval time may be caused by the formulation of collaborative queries through dialogue and mutual consultation of queries sent and the search results received by everyone. One of the most popular tools for sharing results and personal judgments is the annotations. Several problems exist with respect to Collaborative Retrieval (CR). There is, in particular, the problem of the relevance of information. Indeed, ∗ Corresponding author. Fatiha Naouar, MARS Research Unit, Department of computer sciences Faculty of sciences of Mona- stir, University of Monastir, Monastir 5000, Tunisia. Tel./Fax: +21620757720; E-mail: fatihanaouar@yahoo.fr. a user always finds problems in meeting his/her needs in relevant information. As in classical Information Retrieval, the Col- laborative Information Retrieval (hereafter CIR) is designed to return and display a set of documents to a user according to his need. On the other hand, the users of a retrieval system are not always specialists in this field; they can make a bad choice of terms to express the information they need. A reformulation of query has been necessary since the initial user query can return unsatisfactory results. It’s a question then of amending the original user query and this happens by adding meaningful terms to improve the initial returned result. Several approaches exist which use different techniques [3, 13], the most pertinent is the relevance feedback. In this context we suggest to improve the retrieval performance using the relevance feedback to extend the initial query. This technique consists of extracting terms from documents deemed relevant and con- sidered in a new extended query. In collaborative 1064-1246/16/$35.00 © 2016 – IOS Press and the authors. All rights reserved