Tags Weighting Based on User Profile

Abstract

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.

Tags and Information Recollection

“…), recommender systems [13]. Tagging has also been studied for tagging patterns [14], user profile generation [15], [16] and optimum tag selection (particularly with regard to folksonomies [17]). As we can see, tags are important clues for recollection, but to our knowledge in previous studies the link between recollection and tags has essentially been examined with regard to how systems exploit tags.…”

Matching Resources in Social Environment

“…To improve the web search, various approaches [4], [5], [13] explore the use of social annotations. In [4] two new algorithms are proposed: the first one calculates the similarity between social annotations and web queries whereas the second captures the popularity of web pages using social annotations.…”

“…A model in [5], based on social approval votes of documents, shows that social information on documents can improve research and the approval sources provide more details on user needs, particularly, when votes are provided by experts. To define user expertise level, a user model in [13] is integrated in calculating the tag weight. The evaluation is based on the closeness degree between user interest's and resource area, expertise and personal assessment for tags associated to the resource.…”