Social tag prediction: resource-centered approaches for broad folksonomie

Abstract

This work addresses the problem of how to predict tags that will be assigned by users in Social Tagging Systems. It is widely known that tag prediction functionality helps promote system usability and increase the quality of the tag vocabulary in use. With that in mind, we verify the difference in performance of several label ranking techniques on two datasets, which differ from each other in several key metrics such as the average number of tags per resource, tag vocabulary length, total number of resources, etc. We also analyze a specific label ranking technique, namely MIMLSVM. We verify whether it generalizes to dense text representations in addition to traditional sparse ones. Experiments are conducted on the two datasets and results are analyzed.