Abstract—Social tagging applications allow users to annotate online resources, resulting in a complex network of interrelated users, resources and tags often called a Folksonomy. A folksonomy is often represented as a hyper-graph in which each hyper-edge connects a user, resource and tag. This tripartite hyper-graph is often used by data mining applications to provide services for the user such as tag recommenders. This paper provides an overview on the state of the art of graph-based tag recommendation from a critical perspective. In addition, we suggest improving the existing graph-based tag recommendation techniques by introducing a new model of the folksonomy as a directed graph.  

Cite: Maryam Ramezani, "Improving Graph-based Approaches for Personalized Tag Recommendation," Journal of Emerging Technologies in Web Intelligence, Vol. 3, No. 2, pp. 168-176, May 2011. doi:10.4304/jetwi.3.2.168-176