Improving Link Analysis for Tag Recommendation in Folksonomies

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. One of the most well known approaches is FolkRank which constructs an undirected tripartite graph from the hyper-graph and then applies PageRank. However since FolkRank relies on an undirected graph, it does not accurately represent the flow of information across the informational channels. In this paper we model a folksonomy as a weighted direct graph. The weights of the edges are defined by a heuristic that better represents the flow of information from one node to another. We use the proposed model for tag recommendation and the results show an improvement over FolkRank. We show that even the undirected Adapted PageRank with correct parametrization can do better than FolkRank.