Abstract. The 'Collaborative Tagging' is gaining popularity on Web 2.0, this new generation of Web which makes user reader/writer. The 'Tagging' is a mean for users to express themselves freely through additions of label called 'Tags' to shared resources. One of the problems encountered in current tagging systems is to define the most appropriate tag for a resource. Tags are typically listed in order of popularity, as del-icio-us. But the popularity of the tag does not always reflect its importance and representativeness for the resource to which it is associated. Starting from the assumptions that the same tag for a resource can take different meanings for different users, and a tag from a knowledgeable user would be more important than a tag from a novice user, we propose an approach for weighting resource's tags based on user profile. For this we define a user model for his integration in the tag weight calculation and a formula for this calculation, based on three factors namely the user, the degree of approximation between his interest centers and the resource field, expertise and personal assessment for tags associated to the resource. A resource descriptor containing the best tags is created.
To cite this version:Fouad Dahak, Mohand Boughanem, Amar Balla. A probabilistic model to exploit user expectations in XML information retrieval. Information Processing and Management, Elsevier, 2017, vol. 53 b s t r a c tThe main objective of this paper is to exploit a new source of evidence derived from the document hierarchical structure for XML information retrieval. We consider that the structure of XML document is an important source of prior knowledge, and the structural features of an element may influence the user to consider that element as relevant. We build a probabilistic model to estimate the probability that the structural characteristics of an element attract user to explore the content of this element and consider it as relevant. This probability reflects the context importance. We propose a simple, well-motivated probabilistic model to estimate the context importance. Finally, we demonstrate the effectiveness of the context importance through comprehensive experimental studies carried out on IEEE XML document collection. Experimental results show that the proposed approach outperforms models exploiting other sources of evidence.
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