Social Media Annotation and Tagging Based on Folksonomy Link Prediction in a Tripartite Graph.

Social tagging has become a popular way for users to annotate, search, navigate and discover online social media, resulting in the sheer amount of metadata collectively generated by people. This paper focuses on two tagging problems—(1) recommending the most suitable tags during a user’s tagging process and (2) labeling latent tags relevant to a social media item—so that social media can be more browsable, searchable, and shareable by users. The proposed approach employs the Katz measure, a path-ensemble based proximity measure, to predict links in a weighted tripartite graph which represents folksonomy. From a graph-based proximity perspective, our method recommends appropriate tags for a given user-item pair, as well as uncovers hidden tags potentially relevant to a given item. We evaluate our method on real-world folksonomy collected from Last.fm. From our experiments, we show that not only does our algorithm outperform existing algorithms, but it can also obtain significant gains in cold start situations where relatively little information is known about a user or an item.