Dynamic content and user identification in social semantic tagging systems

Abstract

Collaboration relies on the flow of information among parties. To make informed decisions, shared knowledge should be managed in a way to be accessible for relevant group members to each of the contents. One of the main factors that contribute to the quality of contents recommendations to the relevant users is the quality of the users and contents identification. A variety of automatic and semi-automatic methods exist for extracting or assigning identity to online resources. Although users’ perception about contents and also their interests might change over time, most current approaches define a fixed identity for the objects and always recommend them to the users based on their fixed, defined identities because they use a source of knowledge that does not evolve. In our approach, we update identities of the users and Web contents dynamically based on the latest collective opinion of the evolving communities of users related to them in a social semantic tagging system.